We study behind-the-scenes investor activism promoting environmental, social, and governance (ESG) improvements by means of a proprietary dataset of a large international, socially responsible activist fund. We examine the activist’s target selection, forms of engagement, impact on ESG performance, drivers of success, and effects on the targets’ operations and value creation. Target firms are typically large and visible, perform well, and have high liquidity (stock turnover) and low ESG performance. Engagement induces ESG rating adjustments: firms with poor ex ante ESG ratings experience a ratings increase after complying with the activist’s demands, whereas firms with high ex ante ESG ratings experience a ratings decrease following the revelation of their ESG problems. Activism that is focused on environmental and social issues is more likely to succeed if targets are ESG-sensitive (i.e., they have a strong ex ante ESG profile). Successful engagements boost targets’ sales. Risk-adjusted excess stock returns (with four-factor adjustment and relative to a matched sample of non-engaged firms) of successful engagements outperform those of unsuccessful engagements by 2.7%. Results are especially strong for firms with low ex ante ESG scores. Specifically, targeted firms in the lowest ex ante ESG quartile outperform matched peers by 7.5% in the year after the end of the engagement. Our results thus suggest that the activism regarding corporate social responsibility generally improves ESG practices and corporate sales and is profitable to the activist. Taken together, we provide direct evidence that ethical investing and strong financial performance, both from the activist’s and the targeted firm’s perspective, can go hand-in-hand together.
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Introduction
Prominent activist investors such as hedge funds, pension funds, and influential individual shareholders and families often work to reshape corporate policies and strategy (e.g., (Becht et al., 2009; Becht et al., 2017). In this paper, we focus on activism from a different perspective. Given that socially responsible investments (SRI) have become increasingly important, we examine whether investor activism can promote corporate social responsibility (CSR), as reflected in firms’ environmental, social, and governance (ESG) practices, and in turn affect corporate performance and investment results.
In a 1970 New York Times Magazine column, Milton Friedman famously stated that the “social responsibility of business is to increase its profits” (Friedman, 1970). In the past two decades, however, the notion of businesses’ social responsibility has broadened, and socially responsible investing has grown from a niche segment to a mainstream phenomenon. Recent evidence suggests that (corporate) social responsibility is considered the primary proxy for ethical business practices, and for corporate reputation in particular (Leiva et al., 2016). Principles for Responsible Investing (2019) which establishes principles of responsible investing and guidelines for companies, reports that some 3000 institutions, managing about $90 trillion, have endorsed their principles. In doing so, these institutions are declaring that corporate social responsibility is an essential part of their due diligence process and an integral factor in their investment decisions. Further, the Global Sustainable Investment Alliance (2019) (GSIA) estimates that over $30 trillion of professionally managed assets are now explicitly allocated in accordance with ESG standards, driven not only by pension funds but also, increasingly, by mutual funds, hedge funds, venture capital funds, and real estate funds.1 And the trend is not limited to investment managers who explicitly label themselves as responsible. Increasingly, investors who consider themselves conventional are pushing for corporate social responsibility and ethics as well (Duuren et al., 2016).
Anzeige
A subset of ethical investors actively engages with the companies in their portfolios, requesting that they improve their environmental, social, and governance (ESG) practices (see, e.g., (Dimson et al., 2015; Doidge et al., 2019)).2 As Peattie and Samuel (2018) argue, ethically motivated shareholder activism can be considered a challenge to established societal, ideological, and cultural phenomena that have hardly budged from the paradigm established by Friedman in the 1970s. Arguably, consumers and investors can both shape the landscape of social responsibility, but there is a growing consensus that a quasi-top-down approach, in which investors and asset managers aim to steer corporations towards more ethical business practices, is preferable (Salzmann, 2013).
In our paper, we study the effects of investor activism on corporate social responsibility (with a focus on environmental and social aspects) using a large, detailed, proprietary dataset on CSR activist engagements by a leading European investment management firm. The firm is managing SRI funds both for its own account and for its clients. To the best of our knowledge, this is the first paper to investigate such ESG engagements in an international context. In particular, this paper addresses the following questions: How does the activist investor choose target companies when aiming to improve ESG practices? How are such engagements carried out? Are such engagements successful in improving the targets’ ESG performance? What drives success or failure in ESG activism? Is the activism visible in the targets’ operations (e.g., accounting returns, profit margin, sales growth, etc.)? And what are the effects of the activism on investment value creation (i.e., stock returns)?
Our panel spans a decade (2005–2014), 660 engaged companies from around the globe, and 847 separate engagements. The engagements in our sample primarily concern social matters (43.3%) and environmental issues (42.3%); relatively few involve governance (14.4%). As a result, these CSR engagements are quite different from activities by other activist investors (such as hedge funds) that focus on financial value and advocate for asset restructuring and governance improvement (e.g., (Becht et al., 2017)) but not on changes to social and environmental practices (as independent objectives).
We find that engaged companies typically have a higher market share and are followed by more analysts than their peers. Accordingly, in order to avoid selection bias and to account for unobserved heterogeneity, in subsequent analyses we match the engaged firms to control firms from the same industry that are similar ex ante in terms of size, market-to-book ratio, ESG rating, and ROA. In the case of environmental and social activism, the most common channel for engagement is either a letter or email addressed to the top management or the board of directors. In cases that relate to governance, the activist typically participates in shareholder meetings or meets in person with firm representatives (managers or non-executive directors).
Anzeige
In our sample, firms with lower ex ante ESG ratings are more likely to be engaged by the activist. Our evidence suggests that these engagements reveal information about the ESG practices at the engaged companies—information that is subsequently reflected in commercially available, independent ESG ratings. Targets with ex ante low ESG ratings see their ratings improve during the activism period. Targets with ex ante high ESG ratings experience a negative correction, suggesting that some of the activist investor’s concerns had not been previously incorporated in the ratings and are publicly disclosed due to the activism.
The activist considers the engagement successful if the target sufficiently adjusts its policy on one or more previously determined ESG dimensions. Most of the engagement files in our sample (60%) are considered successfully closed by the activist. Successful closings are more likely for targets with a larger market share, a good ESG track record, and earlier successful engagements. The presence of a large controlling shareholder, high short-term growth, and a larger cash reserve are associated with a lower likelihood of success. The activist’s request for a material change from the engaged company (which we call a reorganization) reduces the likelihood of a successful outcome, relative to other engagement types (e.g., one that stimulates the target to be more transparent in its ESG policies).
Examining the changes in operating performance following engagement, we find no relation between engagement and accounting performance or any of its components. However, sales growth increases substantially, on average, following a successful engagement, which could indicate that the implemented changes appeal to a broader customer clientele. Finally, we find positive buy-and-hold stock returns in the month of the completion of the engagement and over subsequent time windows of 6 and 12 months. After the completion of an engagement, excess stock returns (with four-factor adjustment and relative to a matched sample) are higher after successful outcomes. The difference between successful and unsuccessful engagements is mainly significant within 6 to 12 months of the engagement’s conclusion, and disappears subsequently. For example, the excess returns of targeted firms are higher than those of non-targeted peer firms by 2.7% over the 6-month period following the engagement. Results are especially strong for firms with low ex ante ESG scores. Specifically, targeted firms in the lowest ex ante ESG quartile outperform their matched peers by 7.5% in the year after the end of the engagement. Our results thus suggest that the activism regarding corporate social responsibility generally improves ESG practices and corporate sales and is profitable to the activist.
Our findings do not support the argument that the activist fund and the firm engage in CSR efforts only for marketing or reputational purposes (Dupire & M’Zali, 2018). The activist’s dominant channel to engage the firm is direct contact that takes place behind the scenes and is kept private. Furthermore, the engagement seems to bring about tangible effects: (i) CSR ratings increase for ex ante low-rated firms and decrease for other engaged firms, (ii) there is an operational impact of the engagement, visible in sales growth, (iii) share prices increase more after successful engagements than after unsuccessful ones, and (iv) share prices increase more after successful reorganization cases that require changes in the firms’ operations. All of these tangible effects are measured relative to peer companies that are not engaged and have similar ex ante CSR performance. After all, if the firm valued CSR as a marketing tool, why would it wait for the activist to engage it privately on CSR issues?
Literature Review
This paper links up with several related but confined strands of the literature: shareholder activism in general, SRI fund management and the impact of ESG screening devices, and the impact of unobservable activism (i.e., activism behind the scenes). Shareholder activism in general can be loosely partitioned into three categories (Dimson et al., 2015): traditional, hedge fund, and corporate social responsibility. Traditional activism is typically exercised by mutual funds or pension funds and generally concerns topics related to corporate governance or restructuring. Hedge fund activists seek to create financial value by influencing corporate strategy and structure. CSR activism aims to improve corporate citizenship and mainly focuses on issues related to environmental and social topics.
Social responsibility and ethical investments have religious roots that go back centuries (e.g., to the 17th century Quaker movement, as reported in Renneboog et al. (2008a)). Still, it was not until the 1960s that socially responsible investing (SRI) gained momentum and began to capture the general public’s interest. Growing concerns about human rights, pacifism, and environmental issues paved the way for today’s SRI. The first modern investment vehicle catering to socially responsible investors was Pax World Fund, a mutual fund founded in 1971. Since then, SRI has expanded from a niche market strategy to a mainstream investment style. According to SRI reports, total SRI assets under management (AUM) have surpassed the $30 trillion mark globally (Global Sustainable Investment Alliance, 2019), with $12 trillion in the United States (US SIF, 2019) and $4.45 trillion in Europe ((Eurosif, 2018)).
Fund managers apply various techniques and screens to form socially responsible portfolios. Bollen (2007) and Renneboog et al. (2008b, 2011) differentiate among distinct types of SRI screens. Negative screening, the most basic type, avoids investing in firms that sell products such as alcohol, tobacco, weaponry, abortion-related drugs, and pornography. Positive screening selects companies that meet above-average standards in areas such as the protection of the environment, the promotion of human rights, and the sustainability of investments. Negative and positive screens are often combined, yielding a third type: the so-called “transversal” (Capelle-Blancard & Monjon, 2014), “sustainable,” or “triple bottom line” (“people, planet, and profit”) screens. Finally, the fourth generation of SRI funds combines the sustainable investing approach (third generation) with shareholder activism. In this approach, portfolio managers attempt to influence their portfolio companies’ policies through direct engagement with the management/board of directors or through their use of voting rights at annual shareholder meetings.
The existing literature offers conflicting evidence on the financial returns of activism. English II et al. (2004) argue that the effect of activism is only cursory—apparent in the first six months following the announcement of activism and diminishing thereafter. Nelson (2006) concludes that abnormal returns are insignificant for any time window, once confounding effects are controlled for. Greenwood and Schor (2009) report that returns to activism are positive only in the cases where targeted companies are acquired as a result of activism. In a survey paper, Gillan and Starks (2007) find no positive effect of activism in the long run and no convincing evidence of a causal relation between activism and performance.
Other studies do show evidence of beneficial effects. One of the first studies of institutional investor activism, Smith (1996), found that activism by the California Public Employees’ Retirement System (CalPERS) was able to generate shareholder wealth (the “CalPERS effect”) but had no effect on operating performance. Using information from 13-D filings, Brav et al. (2008) document that firms targeted by activist hedge funds in the United States have abnormal returns of 7% around the announcement of activism, and that there is no reversal in returns in the subsequent year. Bebchuk et al. (2015) find no evidence of reversals in the 5-year period subsequent to the 13-D filings and observe lasting improvements in operating performance.3
Investor activism is not always conducted publicly: influential and major shareholders (institutional investors, families and individuals, and corporations) may be active behind the scenes. In a case study of the Hermes UK Focus Fund, Becht et al. (2009) find evidence that activism through private channels creates significant returns and increases operating performance in periods before the market becomes aware of it. Doidge et al. (2019) confirm, for a sample of Canadian institutional investors, that engaging companies through private channels increases shareholder value.
Another body of literature on the performance of SRI funds (see, e.g., Margolis et al. (2011) and Barko and Renneboog (2016) for comprehensive overviews) indicates that SRI funds, at best, perform on par with their market benchmarks or their conventionally managed counterparts. Krueger (2013) shows that stock prices react to the release of CSR news, especially when the news is negative. However, a few papers show that some SRI funds are able to outperform: ** demonstrate that specialized management SRI firms that perform active portfolio selection are able to outperform conventional mutual funds,4 and Gibson et al. (2020) show that funds’ investment strategies based on sustainability are related to the chosen investment horizon and yield positive risk-adjusted returns. Highlighting the pressure on individual firms to address ESG issues, the US SIF (2019) and Eurosif (2018) reports state that about 28% and 40% of institutional investors filed ESG-related requests to their portfolio companies in the United States and Europe, respectively. Among these institutional investors, it is predominantly mutual funds and pension funds that contact companies regarding environmental and social issues (Dyck et al. 2019).
Using a proprietary sample of U.S. activist files, Dimson et al. (2015) also find that successful engagements in social and environmental topics induce positive returns and improvements in operating performance and corporate governance. Hoepner et al. (2020) find that ESG activism reduces left tail firm risk, especially when target firms respond with material actions to the activist’s requests. Looking at shareholder proxy proposals, Flammer (2015) documents that proposals that pass only by a small margin generate significant returns and superior long-term accounting performance. It is not ex ante clear that specific activist tactics are effective across countries. One reason for this is that legal rules and corporate orientations toward shareholders or stakeholders (and the resulting regulation regarding ESG issues), as well as the voluntary adoption of CSR policies (e.g., reflecting social preferences or institutional development), differ across countries, inducing varying levels of CSR performance (among others, see Attig et al. (2016), Boubakri et al. (2016), and Liang and Renneboog (2017, 2020)).
It is interesting to dwell on the ethical perspectives of the type of ESG activism described in this paper. While a vast number of financial institutions, including banks and funds, are signatories of the United Nations Principles of Responsible Investing, the incentives to have ESG as an important investment criterion are ultimately driven by investor demand. That investors care about not only about a financial return but also a (non-financial) “moral dividend” is clear both from investor surveys (e.g., (Lewis & Mackenzie, 2000)) and money-flows in and out of investment funds. For instance, Renneboog et al. (2011) show that, while most funds’ money-flows follow returns (high returns attract net inflows, poor returns the inverse), this is not the case for ESG (or SRI) funds, as negative returns in these funds do not cause investors to withdraw their money. The sensitivity to financial returns is less pronounced for these funds’ investors, who seem to derive utility from the ethical impact of their investments. The fund activism captured in this paper is part of what is called “impact investing” (Scholtens, 2006), wherein investors try to generate a return and to ensure that their direct or indirect investments create societal value (Nilsson, 2008). Societal value can be created by reducing environmental externalities of production, improving labor conditions (throughout the whole supply chain), or ensuring inclusion of minorities in the corporate labor force, among other changes.
Using the terminology of Dembinski et al. (2003) on the ethical investment foundations, we observe that the ESG activist fund in this paper is at the intersection of their four ethical categories. The fund adheres to (i) value-based ethics, as it applies negative screening to specific “sin” industries and tries to induce changes to firms’ ESG policies that are expected to generate share price increases; (ii) fructification-oriented ethics, as the fund demands that investees make policy changes that are meant to have a long-term effect (although we will see that the increased value generated by activism is quickly incorporated in share prices and traded away within 6–10 months after the successful closure of an engagement file); (iii) consequence-based ethics, as the fund aims to induce corporate behavioural change (this is visible, in this paper, in the fact that once a firm is convinced of the importance of ESG standards, the suggestions from subsequent ESG activism are more quickly adopted); and (iv) ethics envisaged as a discriminating criterion in the search for the best financial performance.
A key difference between the type of ESG activism in this paper and traditional corporate governance activism is that the former bypasses a conflictual approach in favor of a dialogue-based process (Logsdon & Buren, 2009; Rehbein et al., 2013) in which corporations and shareholder activists aim for mutual agreement on the improvement of ESG issues.
As a guide to the structure of the remainder of the paper, we formulate the following research questions: What determines whether a firm is a suitable target for an ESG activist? What actions does shareholder engagement comprise? Does shareholder engagement successfully improve a target’s ESG performance? What are the determinants of success or failure in ESG activism? Are the consequences of activism visible in a target’s operations? And does shareholder engagement lead to value creation (i.e., improved stock returns)?
Data
Engagement Data
We have obtained a proprietary database on investor activism from a large European asset manager with more than $250 billion in total net assets under management. The activist has offices and manages funds across Europe, North America, and Asia, and has long focused on ESG-specific investments. The activist mainly manages mutual funds and pension funds. It has a specialized team of analysts that combine in-house and independent third-party research to identify companies that have room for improvement in their ESG policies. Our database covers the universe of the asset manager’s completed engagement cases over the period starting in the third quarter of 2005 and ending at the end of 2014. This enables us to test differences in engagement techniques and corresponding outcomes. As Liang and Renneboog (2017) and Sievänen et al. (2013) show, there are important differences in the perception and implementation of CSR across countries with different legal, political, and historical origins, such that the findings for one region do not necessarily apply to another. Therefore, we split the sample into three distinct regions based on the corporate domiciles: North America, Europe, and Other (mostly Asia-Pacific). Engaged companies are all either part of the MSCI All-Cap World Index or a major regional or country index. In total, our database has 847 completed engagement sequences involving 660 different companies.
The asset manager employs a specialized ESG-team that screens companies around the world. An activist case starts with the identification of a concern—an area where the target company can improve upon its ESG practices. To identify concerns, the engagement team relies on its own research as well as reports by specialized research companies and institutes (e.g., the environmental report of the World Bank or the UN Global Compact Monitor). An unforeseen event or crisis can also trigger an engagement case, if an engager screens a firm’s ESG policies and concludes that they are insufficient to deal with the crisis. A prominent example of this is the 2010 Deepwater Horizon oil spill in the Gulf of Mexico, which BP arguably could have avoided or mitigated had it put in place clearly formulated environmental and disaster contingency plans (Watkins, 2010). The spill has since triggered numerous policy adjustments in the energy sector and enhanced scrutiny by the providers of CSR performance scores and activists.
At the initiation of an engagement, the activist formulates a clearly defined objective. We first partition the engagement cases into two groups based on the engagement’s objectives, distinguishing between those aimed at (i) changing the operations of the firm, e.g., implementing new environmental technology for better water management, or board-restructuring (“reorganization”-oriented engagements); and (ii) providing more information on specific ESG dimensions, e.g., better reporting standards, such as the publication of a detailed sustainability report (“transparency”-oriented engagements). Each of these engagement categories can be further partitioned according to which of the E, S, and G dimensions is the main dimension of interest.
At the start of the engagement, the activist also decides whether to carry out the engagement alone or as part of a coalition with one or more other activists, and whom to contact at the company. Typical contact persons in the engaged firm include executive and non-executive management (such as the CEO, investor relations personnel, and ESG representatives). The activist in this study has a self-imposed deadline of three years during which to achieve the desired outcome. If a successful outcome is reached, it usually occurs within 20 months. The ESG team advises its own in-house fund managers (of both SRI and conventional funds) and also works on commissioned cases on behalf of consulting clients’ portfolios. The activist typically does not own a major block (one surpassing the 5% reporting threshold), so it is generally not required to file 13-D reports in the United States.
In an environmentally related example, the engager contacted a large French cosmetics and beauty company regarding its use of palm oil, after a major UK retailer announced a ban on palm oil products from unsustainable sources. The engager was concerned that this ban, together with the public’s skeptical attitude towards palm oil use, would affect the company’s competitive position within its industry, and requested clarification regarding the use of palm oil in its products. The company provided the requested information, demonstrating that it was only a minor user of palm oil and that it was purchasing its supplies from sustainably managed sources. The activist asked the company to provide this information on its website. After the company complied and published a detailed sustainability report with a special focus on environmental reporting (demonstrating that its potential liability in relation to palm-olive concerns was very limited), this transparency case was successfully closed. This example shows two elements that are typical of the engagement cases in our sample: First, there is a trigger for the engagement—a significant event, a surfacing of new information, or a change or changes in the regulatory or competitive environment. Second, the engager formulates a specific request, and the engagement team follows through on this request, making sure the engaged company fulfills all of the requirements before the file is successfully closed. In “Appendix A”, we provide more illustrations for each main ESG dimension.
For each engagement sequence, we verify that the “successful” closure of the engagement case is indeed determined by the ESG criteria that the activist initially set forth. Furthermore, we cross-reference outcomes with Factiva records and company websites to check the validity of the registered outcomes. We find no evidence that the data include erroneous reporting.
Company-Level Data
We obtain our firm-level data from a variety of sources: accounting and stock return data from Datastream, ESG performance indicators from Asset4 (available through Datastream), analyst coverage data from I/B/E/S, and ownership data from Morningstar and Orbis. We merge the data from different sources using ISINs, Datastream codes, and I/B/E/S identifiers, and cross-check, by means of company names, that all available data are properly matched. We use the global factor return data from Kenneth French’s website to calculate abnormal returns. We define industries in various ways, following the classification on French’s website for 10, 17, and 49 major industry groups, depending on the availability of a suitable control firm (see below). All variable definitions and their respective sources are provided in “Appendix C”.
Engagement Characteristics
The engagement cases are categorized into three themes, based on whether the underlying goal is environmental, social, or governance-related. Within each theme, the engager distinguishes among a variety of topics and subtopics.5 We show the frequency with which these topics and subtopics occur in Panel A of Table 1. This panel also exhibits the percentage of successfully closed engagement files, the number of contacts between engager and target firm, the length of the engagement sequence, and the main contact type. The table shows that the engager focuses mostly on environmental and social topics, as these make up 42.3% and 43.3% of the 847 cases, respectively. About 60 percent of cases are closed successfully.6 The success rates vary by topic: firms are most responsive to engagements regarding public health issues, labor standards, climate change, reporting standards, and corporate governance issues. The average number of contacts with targeted firms is higher, and the average length of the engagement process is lower, in successful cases than in unsuccessful cases. The most frequently used means of contact is a formal letter or email. In cases involving public health issues, the engager and the firm often meet. In corporate governance engagements, the activist raises the issue at the annual or extraordinary shareholder meeting about half the time.
In Panel B, we further break down the engagements by ESG theme by determining whether (i) the aim of the engagement is to trigger reorganization (board or asset restructuring or operational changes) or to enhance transparency (see section “Engagement data”), and (ii) the engaged firm is initially open to the activist’s demand (in which case receptiveness equals one) or resists the activist’s demand (in which case receptiveness equals zero). An initial receptiveness, in management, to the activist’s demands does not necessarily imply success at the end of the engagement period; this variable just measures the willingness of companies to start a conversation with the activist.
Overall, 51.5% of engagements aim at inducing a material change in company policy (reorganization). Two-thirds of the engaged companies are initially receptive to the engager’s request and participate in an initial discussion (Panel B). When we study the percentage of successful cases over time (by year of engagement initiation), we observe that success rates by year vary between 61% and 78%. The lone exception is 2009, when the highest number of cases were initiated and the success rate dropped to 33%, most likely due to the financial crisis.7
We also examine the frequency of the various forms of communication between engager and target. Out of the nearly 3000 activities recorded in the case files, public channels such as annual or extraordinary general meetings and press releases account for only 170 (or 5.6%), and these activities mainly occur in corporate governance cases. One-third of the contacts occur via email, 18.5% by letter, and 11.4% via a conference call. In 10.9% of the cases, a personal meeting takes place (at the firm’s premises in 2.8% of the cases, and at the engager’s offices in 8.1% of the cases).8 Over the whole sample period, the number of contacts between targets and engagers across all activist cases has been steady. Out of the 17 Fama–French industries, oil and petroleum firms and financial firms are engaged the most (93 and 86 cases, respectively), followed by pharmaceuticals, utilities, and retail companies. In terms of geographical focus, 54% of the targets are from Europe, 24% are from North America, 16% are from the Asia-Pacific region, and the remainder are from Latin America or Africa.9
Fig. 1
Engagement case count and success rate by year. On the left axis, the figure shows the number of cases initiated in a given year, as well as the number of cases that were initiated in that year and were completed at some later date. On the right axis, the figure depicts the success rate of cases that were initiated that year and were subsequently closed successfully
Table 1
(A) Engagement characteristics by outcome, (B) Number of engagements by reorganization and target firm receptiveness
(A) Breakdown of completed engagements by ESG themes and topics. A further breakdown of engagement topics is provided in “Appendix B”. In the first part, the number and percentage of all engagements and successful engagements are reported. The second and third part report statistics for the number of contacts, the length of the engagement, and the typical contact type, for successful and unsuccessful cases, respectively. The length of engagement sequences is defined in calendar days. The contact type is the most frequently applied contact channel per topic. Percentages are calculated over all cases.
Full sample (1)
Successful (2)
Unsuccessful (3)
All engagements
Successful
Contact number
Length of sequence
Contact type
Contact number
Length of sequence
Contact type
N
%
N
%
Mean
Median
Mean
Median
%
Mean
Median
Mean
Median
%
Theme: Environmental
Climate change
21
5.9
17
81.0
5.2
5
584.4
491
Email
41.2
4.8
4.5
383.8
500
Email
75.0
Ecosystem Services
113
31.6
64
56.6
5.2
4
857.8
909
Email
50.0
3.9
4
700.9
895
Letter
65.3
Environmental Mgmt.
224
62.6
109
48.7
3.1
2
379.6
328
Letter
37.6
2.5
2
583.0
730
Letter
62.6
Total
358
42.3
190
53.1
4.0
3
559.0
451
Letter
42.1
2.9
2
612.6
730
Letter
63.7
Theme: Social
Public health
30
8.4
27
90.0
3.0
2
395.5
341
Meeting
37.0
1.7
2
329
357
Email
66.7
Human rights and ethics
238
66.5
116
48.7
3.3
3
424.1
374.5
Letter
37.1
2.7
3
479.6
491
Letter
47.5
Labor standards
99
27.7
80
80.8
4.6
4
647.4
716
Email
63.7
6.3
5
938.9
1,064
Email
68.4
Total
367
43.3
223
60.8
3.7
3
500.7
391
Letter
46.6
3.1
3
537.1
491
Letter
50.7
Theme: Governance
Corporate governance
86
70.5
66
76.7
3.4
2
448.6
270.5
A/EGM
39.4
2.3
2.5
234.4
98
A/EGM
50.0
Mgmt. and Reporting
36
29.5
30
83.3
4.3
3.5
402.5
388
Meeting
36.7
3
2.5
600.2
681.5
Meeting
33.3
Total
122
14.4
96
78.7
3.7
3
434.2
355.5
Letter
38.5
2.4
2.5
318.8
196.5
Email
46.2
Total
847
509
60.1
(B) Breakdown of completed engagements by ESG themes and topics. A further breakdown of topics is provided in “Appendix B”. An engagement case is defined as reorganization
if the engager requests a change in the strategy or governance of the firm. A company is deemed as receptive if it responds to the request of the engager. The first part of the table reports the
breakdown of reorganization cases, and the receptiveness by firms for all cases. The second and third part of the table give a breakdown of reorganization and receptiveness by successful and
unsuccessful cases, respectively. Percentages are calculated over all cases for each topic.
Full sample (1)
Successful (2)
Unsuccessful (3)
All
Reorganization
Receptiveness
Reorganization
Receptiveness
Reorganization
Receptiveness
N
N
%
N
%
N
%
N
%
N
%
N
%
Theme: Environmental
Climate change
21
0
0.0
17
81.0
0
0.0
16
76.2
0
0.0
1
4.8
Ecosystem services
113
74
65.5
82
72.6
31
27.4
56
49.6
43
38.1
26
23.0
Environmental Mgmt.
224
178
79.5
116
51.8
69
30.8
107
47.8
109
48.7
9
4.0
Total
358
252
70.4
215
60.1
100
27.9
179
50.0
152
42.5
36
10.1
Theme: Social
Public health
30
0
0.0
24
80.0
0
0.0
21
70.0
0
0.0
3
10.0
Human rights and ethics
238
124
52.1
143
60.1
47
19.7
115
48.3
77
32.4
28
11.8
Labor standards
99
2
2.0
90
90.9
1
1.0
78
78.8
1
1.0
12
12.1
Total
367
126
34.3
257
70.0
48
13.1
214
58.3
78
21.3
43
11.7
Theme: Governance
Corporate governance
86
49
57.0
67
77.9
34
39.5
64
74.4
15
17.4
3
3.5
Mgmt. and Reporting
36
9
25.0
33
91.7
8
22.2
30
83.3
1
2.8
3
8.3
Total
122
58
47.5
100
82.0
42
34.4
94
77.0
16
13.1
6
4.9
Total
847
436
51.5
572
67.5
190
22.4
487
57.5
246
29.0
85
10.0
×
Engaging Target Firms
Matching Methodology
To examine the determinants of the activist’s decision, we first consider the characteristics of target companies in the year preceding the engagement relative to a matched sample. This allows us to mitigate the possibility that any observed ESG changes would have happened without the engagements. The matching procedure allows for an identification strategy that alleviates, to the highest degree possible, the endogeneity concerns that are inherent in all studies of investor activism. Our matching pool is the entire universe of companies in the Thomson Reuters Asset4 ESG database, which contains firms in major indices such as MSCI World, MSCI Europe, DJ Stoxx600, NASDAQ100, Russell 1000, FTSE250, and ASX 300 and which comprises more than 4200 stocks. The Asset4 ESG database has several advantages. First, it is an international index with broad coverage of large international companies, and contains virtually all our sample firms. Second, this database provides dynamic ESG performance scores, by a rating agency that is independent from the engager. This allows us to examine whether the engagements lead to ESG changes that are captured by outsiders. Third, Thomson Reuters is a for-profit organization, and the revenue stream for its ESG ratings comes from the SRI investors, who pay Thomson Reuters for access to them, rather than from the rated companies. This implies that rating shopping is unlikely to be an issue (as opposed to, for example, credit ratings that are paid for by the issuers; see Benmelech and Dlugosz (2009)).
To construct the matched sample, we take several steps. First, we exclude all engaged companies that are also part of the Asset4 database. Second, we restrict the pool to industries based on the 49 Fama-French industry group classification. Third, we calculate the Mahalanobis distance metric for each possible engaged and matching company combination based on size, market-to-book ratio, ESG score, and ROA in the year prior to the engagement. The advantage of this matching method is that we do not impose a hierarchy on the matching variables by sequentially sorting companies into portfolios. Furthermore, the Mahalanobis distance is not sensitive to the scaling of the data and performs well with a small number of matching covariates (Stuart, 2010). The outcome of the matching procedure, the Mahalanobis distance, is an intuitive measure that takes the covariance of matching variables into account (and that reduces to the Euclidean distance if the covariances are equal to zero). We cannot find a match based on 49 industries for 14 engaged firms; for these companies, we relax the set of possible matches based on 17 (rather than 49) industries. After calculating the distance for each company in our universe, we pick the three companies with the lowest distance metric from the engaged company as the controls. For companies that have multiple engagement cases, we keep the same set of matching companies for subsequent engagements. As a robustness test, we re-estimate all our multivariate analyses with (i) a single best match and (ii) other matching methods based on propensity scores (Leuven & Sianesi, 2003), but we do not report these results, as they lead to similar conclusions.
In the selection of our matching variables, we follow a well-established trend in the literature by matching on industry, size, and market-to-book. Additionally, corporate social responsibility has been shown to significantly affect the cost of capital (see for example, El Ghoul et al. (2011)), so we include firms’ ESG scores in our matching algorithm. Finally, to address the concern that a profitable firm may have higher ESG scores due to its financial slack (Hong et al., 2012), we also use ROA in calculating the Mahalanobis distance.
Univariate Results
We present summary statistics for target and matching firms in Table 2, testing the difference in means and medians between the engaged and matching sample using a paired t-test and a Wilcoxon signed-rank test, respectively. To test the difference between the means of the engaged and the control sample, we create a “pseudo-company” for each engaged company using the equally weighted mean of three matched companies, as in Brav et al. (2008). The pseudo-company characteristic is calculated as
where \({\tilde{X}}_i\) represents a characteristic variable for a pseudo-company for each engaged company i and \({X}_j\) is the characteristic variable for each matched company. All variable definitions and their respective sources are provided in “Appendix C”.
Table 2
Descriptive statistics
This table reports summary statistics for all variables. For each case, we keep the first firm-year observation and use a lag of 1 year. The control sample is determined by Mahalonobis distance metric matching. For all engaged companies, we draw three matching pairs with replacement. The Mahalanobis distance is determined based on industry, ESG score, size, market-to-book ratio and ROA. The t-statistics stand for the difference in means between the engaged and the control group. The Z-score is calculated for the Wilcoxon signed rank test, for which we use the median difference between the engaged firm and the control group. For the t-statistics and Z-scores we report p-values in brackets. Variables are winsorized at 2.5% on both tails of the distribution. All variable definitions are in the Appendix.
Variable
Full sample
Control
Difference
Obs.
Mean
Sdev.
25%
Median
75%
Obs.
Mean
t-test
Rank
ESG ratings
ESG sentiment
819
57.769
12.893
50.889
57.044
67.460
2,534
50.465
[0.000]
[0.000]
ESG score
705
77.315
23.821
70
88.520
94.010
2,337
67.861
[0.000]
[0.000]
Environmental score
705
74.627
25.317
63.900
86.990
93.030
2,336
67.412
[0.000]
[0.000]
Social score
705
76.913
23.534
67.860
86.770
94.010
2,336
67.194
[0.000]
[0.000]
Governance score
705
64.412
26.324
45.940
73.910
85.530
2,336
57.244
[0.000]
[0.000]
Economic score
705
71.345
26.151
54.780
81.480
92.660
2,336
63.508
[0.000]
[0.000]
E-index
641
0.376
0.252
0.250
0.250
0.500
1,988
0.360
[0.136]
[0.151]
Risk and performance
Buy-and-hold return
833
0.075
0.459
-0.209
0.067
0.290
2,544
0.052
[0.224]
[0.835]
Volatility
826
0.324
0.183
0.185
0.280
0.409
2,530
0.327
[0.609]
[0.001]
Amihud ILLIQ
827
0.176
0.851
0
0
0.002
2,452
0.164
[0.703]
[0.000]
Asset turnover
846
0.848
0.566
0.460
0.760
1.130
2,544
0.827
[0.375]
[0.371]
Profit margin
841
0.080
0.147
0.035
0.071
0.123
2,537
0.083
[0.637]
[0.177]
ROA
846
0.059
0.064
0.020
0.052
0.090
2,544
0.053
[0.009]
[0.000]
ROE
846
0.157
0.166
0.086
0.152
0.235
2,544
0.133
[0.000]
[0.000]
Sales growth
835
0.101
0.290
− 0.061
0.079
0.219
2,534
0.109
[0.445]
[0.020]
Market share
847
0.028
0.030
0.004
0.015
0.048
2,544
0.017
[0.000]
[0.000]
Market-to-book
843
2.578
1.986
1.338
1.982
3.202
2,544
2.361
[0.001]
[0.255]
Tobin’s Q
843
1.977
1.284
1.124
1.604
2.392
2,544
1.891
[0.073]
[0.033]
Cash and expenses
Cash holding
846
0.066
0.073
0.019
0.041
0.084
2,544
0.067
[0.771]
[0.000]
CapEX
846
0.053
0.046
0.021
0.041
0.075
2,544
0.057
[0.060]
[0.000]
Operating expenses
817
0.862
0.128
0.806
0.881
0.938
2,532
0.862
[0.933]
[0.779]
SGA
664
0.178
0.152
0.053
0.141
0.271
2,085
0.168
[0.114]
[0.754]
Size and capital structure
Log total assets
846
9.623
1.858
8.461
9.862
11.060
2,544
9.293
[0.000]
[0.000]
Log sales
841
9.146
1.719
8.177
9.549
10.617
2,537
8.798
[0.000]
[0.000]
Log market equity
843
9.164
1.752
8.095
9.486
10.802
2,544
8.907
[0.000]
[0.000]
Book leverage
846
0.327
0.220
0.161
0.302
0.461
2,544
0.320
[0.381]
[0.408]
Tangibility ratio
845
0.313
0.234
0.119
0.271
0.479
2,538
0.338
[0.010]
[0.000]
Equity issuance
847
− 0.002
0.005
0.000
0.000
0.000
2,544
− 0.002
[0.077]
[0.000]
Debt issuance
847
0.031
0.110
− 0.021
0.008
0.063
2,544
0.030
[0.770]
[1.000]
Other
Dividend yield
843
0.029
0.027
0.011
0.024
0.040
2,544
0.026
[0.012]
[0.138]
Dividend payout
846
0.389
0.508
0.121
0.325
0.525
2,544
0.353
[0.070]
[0.756]
Company age
845
51.850
52.544
14
37
81
2,544
52.573
[0.681]
[0.000]
Analysts
810
19.076
10.621
11
19
27
2,502
14.169
[0.000]
[0.000]
Ownership
Holding of engager
847
0.002
0.002
0
0.001
0.002
2,544
0.001
[0.051]
[0.580]
Average ownership
847
0.048
0.077
0.011
0.019
0.048
2,544
0.046
[0.314]
[0.000]
Blockholders
847
3.851
1.813
3
4
5
2,544
4.092
[0.001]
[0.000]
Funds
847
0.018
0.068
0
0
0
2,544
0.015
[0.196]
[0.000]
Hedge fund & PE
847
0.009
0.020
0
0.003
0.007
2,544
0.010
[0.172]
[0.000]
Individuals
847
0.018
0.068
0
0
0
2,544
0.015
[0.196]
[0.000]
Independent firm
829
0.840
0.367
1
1
1
2,498
0.848
[0.547]
[0.000]
ESG Performance
As explained above, we use ratings by Thomson Reuters Asset4 that capture the ESG attributes of target and matching companies. The “aggregate” ESG rating is the equally weighted average of the following four underlying sub-ratings or pillars: environmental, social, governance, and economic outlook issues. The first three pillars refer to the usual ESG topics, while the fourth, the economic pillar, addresses the firm’s financial performance and economic outlook. We document, in Table 2, that engaged companies, both at the aggregate ESG level and the individual pillar level, have significantly higher ESG scores than nonengaged firms. This observation is similar to Dimson et al. (2015), who also find that engaged companies already have a higher standard of corporate governance in place prior to investor activism. We also use a modified version of the Entrenchment index (E-index) of Bebchuk et al. (2009). Our model includes four of their six proposed governance provisions—poison pills, golden parachutes, staggered boards, and supermajority for bylaws and mergers—because Asset4 records these variables for all companies. We find that, on average, engaged firms do not have a different aggregate level of these governance provisions than non-engaged firms. Following Boubakri et al. (2016), we also construct a country ESG sentiment measure by calculating the mean ESG rating of all firms within that country in a given year. Table 2 shows that targeted firms are, on average, located in higher ESG sentiment countries.
Risk and Performance
The annual stock returns of engaged companies are not statistically different from those of the matched, non-engaged firms, but the engaged firms exhibit lower stock return volatility and greater liquidity. Engaged firms also have somewhat higher accounting returns, sales growth, Tobin’s Q, and interest coverage. Economically, however, these differences are modest. Engaged companies have somewhat higher market share in their respective industries. Other variables (profit margin, sales growth, asset turnover) do not differ between the groups.
Cash and Expenses
Free cash flow and cash holding figures are comparable across the two samples (Table 2). Engaged companies have slightly lower capital expenditures as a fraction of total assets (0.4%), spend more on advertising, and pay out more in dividends, both in absolute terms and as a percentage of their net income. Cash holdings, free cash flows, and operating expenses do not differ from those of matched firms.
Size and Capital Structure
Engaged companies are significantly larger in terms of assets, sales, and market value of equity, but have significantly fewer tangible assets. Their book leverage is similar to that of their matched peers.
Ownership
Table 2 also reveals that the average holding of our activist engager in engaged firms is small but still significantly higher than its average holding in the matched counterparts. Engaged companies have fewer blockholders (owning a stake of 5% or larger), but when we consider the different types of owners (e.g., financial institutions, industrial companies, the government, hedge funds and private equity, individuals and families), we find no meaningful differences. The seemingly high number of blockholders (Edmans and Holderness, 2017) is driven by firms outside of North America. When we partition the sample into North American, European, and other domiciled firms, we see that North American firms, on average, have three blockholders, European firms have four, and other, mainly Asian companies have more than four. The majority of engaged firms are independent companies, with no shareholder controlling 25% or more of the shares through direct or indirect holdings.
Multivariate Results
In Table 3, we show the results of probit regressions estimating the likelihood of being engaged by the activist. We first analyze whether firm size, performance, market share, leverage, stock liquidity, cash holdings, dividend yield, capital expenditure, SGA, analyst coverage or legal origin10 are related to the choice of the targets, while controlling for year, industry, and geographic fixed effects. The marginal effects exhibited in column (1) of Table 3 indicate that our matching procedure was effective, as only a few of the above variables (smaller size, higher stock market performance, higher product market share, and more analyst coverage) help predict which firms are targeted. The results also show that the asset manager does not generally target companies multiple times, which suggests that engagements are evaluated and started on a per-case basis and that the activist does not have “favorite” targets.
In column 2 of Table 3, we add the percentage of shares owned by the activist prior to the engagement, whether the firm is independent (does not have a major blockholder controlling at least 25% of the equity), the corporate governance index, the country ESG sentiment, and the aggregate ESG score. For the sample of all engagement cases, we find that firms with lower ESG scores but in high-ESG-sentiment countries are more likely to be targeted. Economically, the marginal likelihood of being targeted, − 0.160 (z-statistic of − 1.93), implies that a standard deviation decrease in the ESG score (of 23.8) is associated with a 3.81% increase in likelihood, an increase of over 10% over the unconditional probability. This shows that the activist tends to target companies with more room for improvement in their ESG practice. Ex ante, it seems reasonable to expect greater scope for ESG improvements at firms with low ESG scores.
In the remaining columns of Table 3, we separately estimate the likelihood of being engaged in the environmental (columns 3–4), social (columns 5–6), and governance (columns 7–8) areas. We find that the results from columns 1–2 largely hold, although, in case of the governance dimension, more targeting occurs for companies that have lower potential growth opportunities but are profitable (in terms of share price performance) and have a high Entrenchment index.11 Overall, the results indicate that the activist targets visible firms, with large market shares, and in which the activist holds a larger share stake. The tests on the whole sample indicate that the activist does concentrate on firms in the poorest ESG performance category.12
Table 3
Analysis of targeting by engagement themes
This table reports the marginal effects obtained from probit regressions on the probability of targeting relative to a matched sample, where the dependent variable is 1 if a company if targeted and 0 otherwise. The first two columns report regression results for the whole sample of engagements (1–2), while the second, third, and fourth sets of columns refer to environmental (3–4), social (5–6) and governance (7–8) cases, respectively. Marginal effects are evaluated at the mean of the respective independent variable. The variable “ESG score” is the equal ESG rating for the full sample and the corresponding score for each specific engagement theme, expressed as a percentage. Standard errors are clustered at the firm level. The matching sample is determined by Mahalanobis score matching on industry, size, market-to-book, ESG, and ROA. Variable definitions are provided in the Appendix
Full sample
Environmental
Social
Governance
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Log total assets
− 0.031**
0.025
− 0.008
0.036
− 0.030*
0.02
− 0.060**
0.036**
Tobin’s Q
0.001
0.002
0.023
0.029
− 0.003
− 0.009
− 0.029
− 0.068***
Sales growth
− 0.056
− 0.072
− 0.062
− 0.074
− 0.076
− 0.133
0.176*
− 0.063
BHR over 12 months
0.077***
0.096***
0.038
0.023
0.085***
0.130***
0.142**
0.247***
ROA
0.253
0.097
0.252
0.076
0.285
0.029
− 0.208
− 0.163
Sales market share
3.899***
2.752***
3.272***
2.412***
4.590***
3.193***
3.785***
1.760**
Cash holding
− 0.097
0.066
0.105
0.124
− 0.211
− 0.131
0.1
0.343**
Book leverage
0.078
0.038
0.114
0.055
− 0.019
0.041
0.196
− 0.212***
Dividend yield
0.547
0.953
0.593
1.200*
0.345
0.922
2.907**
1.015*
CapEX
0.103
0.271
0.278
0.391
− 0.076
− 0.333
− 0.902
0.930**
Amihud ILLIQ
− 0.006
− 0.544**
− 0.05
− 0.430**
0.021
− 0.603
− 4.221
− 3.022
Analysts
0.012***
0.010***
0.013***
0.010***
0.009***
0.014***
0.014***
0.007***
SGA
0.044
0.165
0.12
0.181
− 0.122
0.054
0.481**
0.488***
Common law
− 0.071**
− 0.028
0.003
0.027
− 0.064*
− 0.041
− 0.219***
0.119*
Previous engments
− 0.023**
− 0.030***
− 0.032**
− 0.052***
− 0.021*
− 0.025**
− 0.018
− 0.009*
Holding of engager
3.027
20.871
1.118
6.443
Independent company
− 0.005
− 0.064
− 0.008
0.039
ESG sentiment
0.008***
0.008***
0.003
0.017***
Entrenchment index
− 0.025
− 0.031
− 0.062
0.124**
ESG score
− 0.160***
E score
− 0.038
S score
− 0.161**
G score
− 0.419***
Year dummy
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Industry dummy
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Geographic dummy
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Pseudo R2
0.12
0.23
0.13
0.20
0.10
0.24
0.42
0.75
N
2567
2028
1127
900
1090
816
346
304
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively
Engagement Success
In this section, we consider the drivers of “successful” engagements. As we noted above, success is not determined by a realization of value triggered by the adoption of the activist’s requirements, nor by the amount of effort the target exerts to meet the activist’s demands. It depends solely on whether the target complies with whatever the activist set forth as the ex ante demand. Table 4 explores possible drivers of successful engagements, which include (in addition to the variables in Table 3) indicator variables for whether or not the activist requests a reorganization effort (captured by the variable Reorganization) rather than just more transparency, whether or not the engagement was conducted jointly with other activists (Joint targeting), whether the activist targeted top executives in the target versus lower-level managers or non-executive directors (Contacted executives), the number of contacts over the course of the engagement (Number of contacts), and, finally, whether any previous engagement involving the firm was successfully concluded (Success streak).13
The results in column 1 reveal that, on average, cases where the activist requests that the target make significant changes in terms of board or asset restructuring or a change in ESG-related operations are significantly less likely to lead to successful case closure. The coefficient of Reorganization, − 0.163, suggests that such far-reaching requests have a 16.3% lower likelihood of being successfully closed, compared to an overall success rate of 60%. This is not surprising, as the effort level required of firms is much higher in reorganization engagements. In general, it is easier to achieve “success” in transparency cases, but whether these cases can generate significant value—value that is subsequently reflected in the stock price or the accounting performance—is questionable. In contrast, reorganization cases may be more likely to lead to value enhancement, but they are also harder to achieve, as they require more substantial, farther-reaching corporate decisions, which the management may be reluctant to make.
Returning to column 1 of Table 4, we find that the eventual success of the engagement is higher if the activist jointly targets a company with other activists; however, this finding is driven by engagements in the social domain. The success rate is not higher if executives rather than non-executives are the engager’s main contact at the target, if the number of contacts between the activist and the firm is higher, or if the firm is more visible (as indicated by a larger number of analysts following the firm). Companies that previously implemented activist-requested changes are more likely to comply with future requests, albeit mainly in environmental engagements. Targets are also more likely to comply with activist requests when their sales growth is lower. In particular, the coefficient on Sales Growth of − 0.357 indicates that a standard deviation decrease in sales growth (of 0.290) is associated with a 10.4% increase in the likelihood of success. The coefficient of SGA is significant and consistently positive across specifications for the full sample, as well as for both environmental and social engagements. This suggests that companies with a large overhead cost base are significantly more compliant (by up to 14.7% for social cases). One explanation for this is that companies with a high level of SGA can rechannel their non-production costs towards ESG activities. An alternative explanation is that they deem it more relevant to spend on ESG rather than, for example, advertising, as they attempt to increase consumer awareness (Servaes & Tamayo, 2013).
Next, column 2 examines additional variables capturing governance and ESG aspects. We find no persistent relation between engagement success and the proportion of the shares owned by the activist and the increases in this equity stake during the engagement process (“Holding increase”), or between engagement success and the target’s corporate governance (as proxied by the aggregate index of shareholder rights provisions—the entrenchment index). However, we do find that engagement success is more likely for firms with a higher ESG score prior to engagement. The marginal likelihood of 0.448 means that a standard deviation increase in ESG ratings is associated with a 10.7% increase in the probability of success. This is consistent with the ex ante ESG score indicating how much firms care about ESG issues, and also with the notion that firms with a stronger ESG track record have the necessary ESG resources and know-how largely in place, such that compliance does not require a large departure from existing practices. In contrast, ESG sentiment, captured by the average ESG score within the country, does not influence engagement outcomes. Together, these findings suggests that it is the activist’s efforts, not other external factors, that propagate success.
It is possible that the activist is more likely to target firms where the activist anticipates that a successful engagement can be more easily achieved. To control for this and other potential selection issues with the selection equation model (2) of Table 3, we estimate, as a robustness analysis, a two-stage Heckman model. We find that the above results in Table 4 carry through, and that selection does not appear to be an issue (as the inverse Mills ratio is insignificant in all our specifications).14
When we analyze the outcome of engagement by ESG theme in columns 3–8 of Table 4, we find that reorganization requests are less likely to be successful, and that previous successful engagements matter for the subset of engagements related to environmental issues but not for social or governance engagements. For environmental engagements, large cash holdings are associated with a reduced probability that the case is closed successfully, perhaps because large cash holdings occur at corporations that are less dependent on external capital markets and that, accordingly, are less interested in good investor relationships. For the subset of social engagements, those at firms with a larger market share are more likely to be successful, which suggests that market leaders in their industry are more open to investor engagement or are more worried about potential negative media stories. The sensitivity to the engagement is also larger for firms that have lower sales growth, possibly because lower growth puts them under pressure from investors. Finally, governance engagements are more likely to be successful at firms with low buy-and-hold returns over the past year. This finding is strongly statistically significant once we control for the entrenchment index and ESG characteristics in column (8). However, lower buy-and-hold returns are not related to a higher likelihood of success for environmental or social engagements. This suggests that corporations deem investor concerns more relevant when they have performed relatively poorly in the stock market, but primarily when they face governance activism (in which case they may hope to forestall more significant shareholder activism).
Table 4
Analysis of success
This table reports the marginal effects obtained from linear probability regressions on the probability of success. The dependent variable equals 1 if the engagement is successful and 0 otherwise. The first two columns report regression results for the whole sample of engagements (1–2), while the second, third, and fourth sets of columns refer to environmental (3–4), social (5–6), and governance (7–8) cases, respectively. Standard errors are clustered at the firm level. The dummy “Reorganization” takes the value 1 for reorganization cases and 0 otherwise. The dummy variable “Joint targeting” equals one for cases where the engager contacts the company with a group of other activists. The variable “Contacted executives” is 1 if executive management is contacted and 0 otherwise. “Number of activities” and “Success streak” refer to the number of contacts per case and the number of previous successful cases with the company. Other variable definitions are provided in the Appendix
Full sample
Environmental
Social
Governance
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Reorganization
− 0.163***
− 0.144**
− 0.434***
− 0.335**
− 0.017
0.014
0.124
0.170
Joint targeting
0.094*
0.039
0.081
0.021
0.128*
0.106
− 0.246
− 0.229
Contacted executives
− 0.111**
− 0.090
0.080
0.006
− 0.267***
− 0.194**
− 0.147
− 0.054
Number of contacts
0.008
0.006
0.008
0.010
− 0.023
− 0.020
0.015
0.026
Success streak
0.010
0.010
0.057*
0.048
− 0.025
− 0.010
− 0.011
− 0.033
Log total assets
0.020
− 0.036
0.028
− 0.038
0.035
0.001
0.086**
0.075
Tobin’s Q
− 0.030
− 0.023
− 0.038
− 0.063
− 0.042
− 0.024
− 0.066
− 0.284***
Sales growth
− 0.357***
− 0.333***
− 0.380**
− 0.242
− 0.154
− 0.260
− 0.908***
− 0.720**
BHR over 12 months
0.036
− 0.019
0.026
0.024
0.042
0.070
− 0.161
− 0.597***
ROA
− 0.342
− 0.596
− 0.373
− 0.103
0.366
− 0.404
0.699
4.238**
Sales market share
1.357
1.903**
0.099
0.240
1.387
2.071
− 1.840
− 5.341**
Cash holding
− 0.392
− 0.804**
− 0.980**
− 1.265**
− 0.419
− 0.854*
0.331
− 0.222
Book leverage
0.040
− 0.103
− 0.024
0.043
0.223
− 0.088
− 0.077
0.290
Dividend yield
0.230
0.490
− 0.182
0.808
0.634
1.001
− 0.545
− 6.120
CapEX
− 0.258
0.515
− 0.763
− 0.069
1.200
2.668**
0.262
− 1.169
Amihud ILLIQ
− 0.011
0.299
0.114*
− 0.127
− 0.025
1.501**
− 9.996
− 30.889**
Analysts
0.001
− 0.001
− 0.006
− 0.011**
0.008*
0.005
− 0.006
0.008
SGA
0.725***
0.597***
0.675**
0.748**
0.795***
0.828***
0.291
− 0.475
Common law
− 0.008
− 0.031
0.025
− 0.047
− 0.089
− 0.040
0.657
0.405
Toehold
0.008
− 0.013
0.005
0.027**
Toehold increase
− 0.036
− 0.187***
0.047
0.275**
Independent company
0.111
0.035
0.138
− 0.250
Entrenchment index
0.033
0.176
− 0.059
− 0.243
ESG sentiment
0.000
− 0.004
0.003
− 0.020*
ESG score
0.366**
E score
0.484***
S score
0.191
G score
− 0.217
Year dummy
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Industry dummy
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Geographic dummy
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Adjusted R2
0.25
0.24
0.24
0.26
0.34
0.35
0.32
0.37
N
616
471
272
218
263
185
81
68
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively
Analysis of Performance After Engagement
Implementing or increasing CSR can increase firm value several ways, as pro-social behavior can be rewarding for various stakeholders, shareholders, and management (Baron, 2008; Bénabou & Tirole, 2006). First, higher ESG standards can increase consumer loyalty through product quality signaling, leading to higher market share and higher and less volatile profits (Albuquerque et al., 2019). Second, employee satisfaction fosters productivity and efficiency, also leading to higher profits (Edmans, 2011; Edmans, 2012). Third, corporate social responsibility can attract a shareholder base that has long-term investment goals, reducing pressure on management to generate short-term profits and allowing for investments that yield returns over a longer time horizon (Gaspar et al., 2013). Fourth, improved governance standards indicate better management practices and result in higher future performance (Ferrell et al., 2016). Finally, investments in CSR could be similar to paying an insurance premium to avoid rare events that could harm a firm and which are not priced yet (Hong et al., 2015; Lins et al., 2017).
We first test the impact of engagements on the operations and characteristics of target firms. We estimate differences-in-differences (DD) specifications (Eqs. 2and 3) in which the dependent variables are market-based measures of performance (Tobin’s Q), accounting-based measures of performance (ROA, operating expenses, SGA, sales growth, profit margin, asset turnover), and measures of sales market share, investments (CapEx), ownership (long-term holdings, toehold stake of the activist), ESG performance (ESG ratings; environmental, social, governance scores), corporate governance (entrenchment index), changes in capital structure (equity and debt issuance), and visibility (analysts following). We use two treatments, the successful completion of the engagement case (Eq. 2) and the engagement treatment irrespective of subsequent success (Eq. 3):
where Post is an indicator variable that equals one for the 1-year period following the successful closure of a case, and zero otherwise (Eq. 2), or for the 2-year period after the engagement, and zero otherwise (Eq. 3). The latter case captures the typical duration of the engagements. Equation (2) is estimated for the sample of engaged companies (both successful and unsuccessful outcomes), whereas Eq. (3) is estimated for the sample of both engaged and non-engaged matched firms.
We apply the same methodology on various subsamples: the reorganization-oriented engagements, the quartiles of firms with the lowest and highest ESG scores (measured prior to engagement), and the environmental-, social-, and governance-oriented cases. Furthermore, we split the sample into low and high governance and low and high visibility groups. The split is based on the median of the E-index and the analyst base, respectively. Finally, we separate companies that have no shareholder controlling more than 5% (Independent company) from companies that do (Controlling shareholder). In all these specifications, the vector Controls includes leverage, size, tangibility of assets, and time and industry fixed effects.15 We cluster standard errors at the firm level.
For the sake of brevity, we only report the \(\delta\) coefficients in Table 5, where each coefficient comes from a separate regression. In Panel A, we report the \(\delta\) coefficients for the evaluation of success for all engagement cases (column 1) and for the 12 subsamples. The results indicate that, on average, accounting performance does not significantly change following a successful engagement. This is in line with Klein and Zur (2011) results showing that hedge fund activism does not improve accounting performance. And while Di Giuli and Kostovetsky (2014) show that a higher level of ESG is detrimental to future profitability (especially through increased costs), we find that this claim is unsubstantiated in our sample.
Sales growth, in contrast, improves by 3–22% on average after successful engagements. This improvement is seen across all subsamples except social engagements. Given the typical sales growth of 10.1% in the year preceding engagement, the overall jump of 7.6% is quite meaningful not only statistically but also economically.
The coefficients on the ESG performance ratings confirm that successful engagements lead to higher ESG scores for the targets with the ex ante weakest ESG ratings (the lowest quartile). The results suggest that if a case is closed successfully with an ex ante poorly rated company, the ESG rating, on average, increases by 10.6, which is a significant boost of 13.7% compared to the mean. This growth is most pronounced for environmental ratings, where we observe an 18.6% gain relative to the initial rating.
It is possible that an activist’s targeting of a firm generates an effect even if the activist does not attain its specific goals for the engagement. To investigate this possibility, we turn to panel B of Table 5, where we report the DD coefficients of an analysis where the treatment effect is engagement and the non-treated sample consists of matched non-engaged firms. As before, we study the changes in corporate and ESG performance as well as other firm characteristics for the full sample and a set of subsamples. We find that the engagement, in itself, has little impact on the ex post accounting performance (column 1) or any other firm characteristic (with the exception of the market share, which is a little lower). For example, the increases in sales growth that we document for successful cases do not occur for unsuccessful ones.
The subsamples of firms within the lowest versus highest (ex ante) ESG quartiles yield some interesting results. For example, the mere fact of engaging low ESG targets triggers significant increases in the targets’ ESG scores. The overall score and the subscores on the E, S, and G aspects all augment, as does the economic outlook sub-score (which proxies for shareholder and customer loyalty). So, the mere engagement, independent of outcome, triggers changes in the ESG profile of the target, which are, in turn, picked up by independent ESG evaluation providers. For the firms in the highest ex ante ESG quartile, we observe the inverse, as all of the ESG scores go down after the engagement. This could be the result of an information revelation process: if, after conducting research, the activist correctly identifies companies with a potential for improvement in one of the ESG dimensions, then subsequent ESG ratings should reflect this new information. The adjusted ESG scores thus incorporate the potential ESG problem, driving down the score. Together, these results imply that research and engagement activity brings new information to market actors and helps reveals companies’ ESG practices. The new information might show that previously low-rated companies are not “lost cases,” or that recent best-performers still have room for improvement. As the activist engages companies, the rating agency generally seems to realize, over the course of the engagement, that previous scores did not incorporate all of the activist’s concerns, i.e., that engaged companies still had key ESG points to improve on.16
Our results also indicate that there are significant post-engagement changes in how firms attract capital. We find that after an engagement (Panel B), engaged firms with low corporate governance (a high E-index, capturing weak shareholder rights) issue more debt than non-engaged firms. The same occurs for engaged firms with high visibility and for engaged firms with widely held ownership. The low-corporate-governance finding is especially important, as it suggests that ESG engagement could lead to improved governance and reduced agency problems. We next turn to the effect of successful versus non-successful closure of ESG engagement (Panel A). After a successful engagement, we observe more debt issues in firms with low corporate governance (high E-index) and firms with low visibility (low analyst coverage). This suggests that a successful engagement could induce trust in the corporation, such that the firm can more easily issue debt (possibly induced by a lower expected cost of capital). Taken together, our results tie in with the Boubakri et al. (2018) evidence that shareholders and bondholders react differently to externally influenced corporate actions.
Table 5
Financial and ESG performance, and ownership after engagement
This table reports the results of differences-in-differences estimations of the effect of engagement and success on financial and ESG performance, as well as changes in ownership. The table reports the coefficient of the differencing term. The pre-treatment period is defined as one year before the start of an engagement sequence. In panel A, post-treatment is defined as one year after completion. In Panel B, post-treatment is defined as two years after the first contact with the company. The period variable is 1 for post-treatment and 0 otherwise in both panels. In Panel A, the treatment is success versus no success, where the treatment variable is 1 for success and 0 otherwise. In Panel B, the treatment is engaged versus matched companies, where the treatment variable is 1 for engaged companies and 0 for the control sample. The matching sample is determined by Mahalanobis score matching on industry, size, market-to-book, ESG, and ROA. Leverage, size, tangibility, and industry and time fixed effects are included in all specifications. Additionally for Tobin’s Q ROA, CapEx and sales growth are also included. Standard errors are clustered at the firm level.
Panel A: Success vs. no success
Full sample
Reorg.
Lowest ESG quartile
Highest ESG quartile
E cases
S cases
G cases
Low E-index
High E-index
Low analyst coverage
High analyst coverage
Independent company
Controlling shareholder
Tobin’s Q
− 0.043
− 0.008
− 0.167
0.110
0.036
− 0.124
0.266*
0.315*
0.497
0.115
0.244
0.283*
− 0.007
ROA
− 0.003
− 0.003
0.006
0.002
0.008
− 0.006
− 0.019
− 0.023
− 0.009
− 0.032
− 0.013
− 0.017
0.013
Operating expenses
0.002
− 0.006
0.014
− 0.012
− 0.008
0.008
− 0.019
0.005
− 0.095
0.046
− 0.05
− 0.009
− 0.105
CapEX
0.004
0.002
0.001
0.003
0.005
0.001
− 0.001
− 0.002
0.006
− 0.004
− 0.001
0.001
0.005
SGA
− 0.002
− 0.001
− 0.003
− 0.026**
0.010
− 0.011
− 0.006
0.017
− 0.04
− 0.015
0.004
− 0.013
0.027*
Sales growth
0.076***
0.053*
0.093*
0.103*
0.097***
0.032
0.229**
0.135
0.306**
0.449**
0.101
0.239**
0.139
Sales market share
0.000
0.001
− 0.002
0.003
0.002
0.000
0.000
− 0.002
0.001
− 0.003
0.002
0.001
0.006
Profit margin
− 0.018
− 0.005
0.004
0.001
0.022
− 0.039**
− 0.093
− 0.067
− 0.183
− 0.249
− 0.012
− 0.091
− 0.042**
Asset turnover
0.010
− 0.023
0.032
0.004
0.003
0.023
− 0.043
− 0.087
− 0.114
− 0.072
− 0.037
− 0.067
0.078*
Equity issuance
− 0.001
− 0.000
0.001
− 0.005**
− 0.001
− 0.001
0.004**
0.003
0.007
0.004
0.004*
0.004**
0.001
Debt issuance
0.014
0.011
− 0.021
0.027
0.027
− 0.011
0.059
0.016
0.091***
0.196**
− 0.017
0.063
− 0.065
Long-term holdings
0.304
− 0.217
0.527
− 1.708
2.098**
− 0.778
− 4.161
− 4.203
− 0.602
− 12.798*
0.649
− 2.743
− 17.146
Holding of engager
0.012
0.012
0.007
− 0.028*
− 0.019
0.043**
− 0.010
0.096**
− 0.121
− 0.063
0.028
− 0.009
0.052
ESG rating
− 0.654
1.605
10.635***
− 0.231
1.844
− 3.849
− 0.953
3.104
− 7.068*
0.892
− 0.874
− 1.841
− 2.774
Environmental score
0.129
2.780
13.917***
− 0.491
1.552
− 2.122
− 3.103
3.19
− 12.122
2.465
− 5.394
− 3.511
5.641
Social score
− 0.491
1.557
4.394
− 1.016
0.143
− 2.374
− 0.553
1.671
− 3.157
− 1.272
− 0.191
− 0.413
− 10.637**
Governance score
− 1.855
− 0.905
− 2.513
0.900
1.157
− 4.603*
− 2.629
− 2.999
− 1.059
− 4.974
0.495
− 3.611
− 4.293
Economic score
− 1.129
1.612
6.429
6.070
2.604
− 4.368
0.265
6.134
− 9.411
3.75
0.249
− − 1.226
− 9.734*
Entrenchment index
0.026
0.037
0.003
0.031
0.002
0.040
0.016
0.066
− 0.028
− 0.061
0.045
0.032
0.163*
Analysts
− 0.336
− 0.147
− 0.468
− 1.567
− 1.037
0.470
0.522
− 0.142
2.055
− 3.003
2.597
− 1.461
5.411
This table reports the results of differences-in-differences estimations of the effect of engagement and success on financial and ESG performance, as well as changes in ownership. The table reports the coefficient of the differencing term. The pre-treatment period is defined as one year before the start of an engagement sequence. In panel A, post-treatment is defined as one year after completion. In Panel B, post-treatment is defined as two years after the first contact with the company. The period variable is 1 for post-treatment and 0 otherwise in both panels. In Panel A, the treatment is success versus no success, where the treatment variable is 1 for success and 0 otherwise. In Panel B, the treatment is engaged versus matched companies, where the treatment variable is 1 for engaged companies and 0 for the control sample. The matching sample is determined by Mahalanobis score matching on industry, size, market-to-book, ESG, and ROA. Leverage, size, tangibility, and industry and time fixed effects are included in all specifications. Additionally for Tobin’s Q ROA, CapEx and sales growth are also included. Standard errors are clustered at the firm level.
Panel B: Engaged vs. matched
Tobin’s Q
0.013
0.039
− 0.060
0.019
0.058
− 0.062
0.093
− 0.122
0.249
0.023
− 0.063
0.061
− 0.459***
ROA
− 0.000
− 0.003
0.008
− 0.005
0.001
0.001
− 0.005
0.002
0.001
0.002
0.003
0.003
0.023
Operating expenses
0.003
0.004
0.010
0.000
− 0.007
0.010
0.009
− 0.004
− 0.063***
− 0.014
− 0.028*
− 0.016*
− 0.068
CapEX
0.002
0.000
− 0.002
0.003
0.002
0.000
0.007**
− 0.003
0.005
0.001
0.001
0.001
− 0.005
SGA
− 0.001
− 0.001
0.013
− 0.011
0.001
− 0.001
− 0.010
− 0.006
0.002
0.005
− 0.014
0.006
− 0.024
Sales growth
− 0.011
− 0.018
0.031
− 0.015
0.005
− 0.008
− 0.064
0.166**
0.165***
0.133*
0.151**
0.171***
− 0.028
Sales market share
− 0.001***
− 0.001
0.001
− 0.004**
− 0.001
− 0.002**
− 0.002
− 0.001
− 0.001
− 0.001
− 0.003*
− 0.002
0.004
Profit margin
0.002
− 0.004
0.026
− 0.008
0.000
0.003
0.004
− 0.051
− 0.081
− 0.103
− 0.012
− 0.062
0.103
Asset turnover
− 0.016
− 0.028**
− 0.014
− 0.050**
− 0.004
− 0.022
− 0.030
− 0.013
− 0.012
0.017
− 0.055*
0.015
− 0.130*
Equity issuance
0.001
0.001
− 0.001
0.001
0.001
− 0.001
0.001
0.001
0.003**
0.001
0.002**
0.002**
0.001
Debt issuance
− 0.006
− 0.016**
0.016
− 0.009
− 0.006
0.002
− 0.025
− 0.003
0.034
− 0.016
0.014
− 0.002
0.004
Long-term holdings
0.520
0.380
− 0.155
1.178
0.379
0.282
1.659*
− 0.592
0.082
2.141**
− 2.854**
0.086
− 6.579
Holding of engager
0.009
− 0.004
− 0.025
0.017
− 0.004
0.006
0.048
0.005
− 0.024
− 0.031
0.005
− 0.015
0.013
ESG rating
0.522
0.957
9.284***
− 4.134***
0.677
0.385
− 0.214
4.341*
− 1.442
4.512
− 1.099
0.394
4.795
Environmental score
0.281
1.376
10.425***
− 4.901***
0.135
0.119
0.720
3.938
− 2.952
4.177
− 1.337
1.146
0.108
Social score
− 0.996
− 0.982
4.167
− 6.406***
− 1.114
− 0.858
− 1.367
4.417
− 2.809
5.689
− 2.773
0.232
3.131
Governance score
− 0.475
0.322
8.822***
− 8.681***
0.208
− 1.113
− 1.611
0.661
1.016
− 2.157
1.392
− 1.886
1.671
Economic score
2.229
3.469*
21.680***
− 9.294***
2.852
2.299
− 0.467
1.332
0.268
3.141
− 1.271
0.081
7.148
Entrenchment index
0.006
0.009
0.006
0.021
− 0.001
0.012
0.018
0.011
− 0.022
0.002
0.016
− 0.006
0.041
Analysts
0.258
0.351
0.705
0.788
0.688*
0.108
− 0.640
− 0.924
0.683
− 0.904
0.401
− 0.529
5.009
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively
Returns to Engagement
In this section, we assess two measurements of stock performance during and after the engagement: buy-and-hold returns (BHRs), which are raw, unadjusted cumulative returns, and cumulative abnormal returns (CARs), which are returns corrected for exposure to the global market, size, book-to-market, and momentum Fama-French-Carhart return factors. We use stock return data from Datastream and download our factor data from the website of Kenneth French.
In Table 6, we report BHRs over the engagement period and by the ex post outcome for all of the firms in our sample. Additionally, we report the contemporaneous returns of matched firms. Since engagement sequences vary in length (with a mean of about 20 months), we calculate annualized returns. We calculate the BHRs over the engagement period and then annualize to make the returns comparable. For matched firms, we annualize over the same horizon as for their respective targeted counterparts. The table reports returns for the entire sample, for low and high ESG firms, for reorganizations, and by ESG topic. Two main results emerge from this table. First, targeted firms always realize higher returns over the engagement period than their matched counterparts (with the exception of high ESG firms and firms targeted for social topics). On average, engaged firms have a return of 14.5%, compared to 11.9% for the control group in the same period, but the difference is not statistically significant. Engaged firms perform significantly better when they are in the lowest ESG quartile, are subject to reorganization, or are engaged for environmental reasons. Second, using the ex post measure of success, we find that firms that are engaged (eventually) successfully earn returns during the process that are not statistically different from the returns of unsuccessful cases.
In Table 7, we report BHRs in the month around the completion of the engagement (distinguishing between successful versus unsuccessful completion, and control firms) and over time windows of 6 and 12 months following the end of the engagement. We find that, on average, BHRs are small but positive and statistically significant in the month following the closure of a case (at 1.2%). These positive returns stem from successfully closed cases, which generated BHRs of 1.6%; cases that were not successfully closed do not generate any significant return. Over the period of six months after their completion, successful cases generate returns of 5.5%, while unsuccessful cases incur insignificant price movement (1.1%). Extending the time window to one year, we find large positive returns for both successful and unsuccessful cases, but the difference between the successful and unsuccessful cases is now insignificant.
We re-estimate these BHRs over the same time windows for different subsamples and also report the results in Table 7. The target subsamples with the lowest ESG scores ex ante generate 4.7% higher returns over a six-month window and 6.8% higher returns over a one-year window, relative to the control firms. Successfully and unsuccessfully engaged target firms do not show any significant return difference over any time window. For the ex ante highest-rated ESG firms, the return difference with the control firms is immediate (in the first month after ending the engagement); again, this finding does not depend on the outcome of the engagements.
Reorganizations do not perform better, in general, after the engagement than control firms do. Although successful reorganizations yield BHRs of 2.5% in the month of the engagement completion and 5.4% over a 1-year post-engagement window, unsuccessful reorganizations yield BHRs that are close to zero. When we partition the engagement files by ESG dimension, we find that it is mainly the firms that are engaged for environmental reasons that significantly outperform the control firms (by 3% over a year). Over the time window of 6 months after the end of the engagement, successful environmental engagements outperform unsuccessful environmental engagements by 5.9%, and successful social engagements outperform unsuccessful social engagements by 3.6%.17
In Fig. 2, we depict the mean BHR of equally weighted portfolios of engaged companies, where the portfolios were created one month prior to the event month and the returns are calculated over the subsequent 18 months. The return difference between successful and unsuccessful cases is highest for the period 6–12 months following the completion. Figures depicting the mean BHR over 18 months after the completion of the engagement for the subsamples of engaged North American, European, and Other (mainly Asia-Pacific) firms exhibit a similar picture (not shown).18 For North American and European firms, the BHRs gradually increase, then level off after about 8–9 months, and the difference in BHRs between (un)successful engagement firms is at its maximum between 6 and 12 months. For the Other subsample, the average BHR across all firms gradually declines over 5 months, and the returns of the unsuccessful cases decline faster than those of successful cases.
Fig. 2
Buy-and-hold returns after completion. The figure shows buy-and-hold returns for an equally weighted portfolio of engaged companies, as well as for control firms. The portfolios are formed at the completion of engagements
Table 6
Buy-and-hold portfolio returns over engagements
The table presents mean annualized buy-and-hold returns over engagements for various subsamples. For each subsample, returns are calculated for the entire subsample, for successful and unsuccessful engagements, and for their respective control groups. The table reports whether the mean is equal to zero and the difference between successful and unsuccessful cases and the control group. The matching sample is based on Mahalanobis score matching on industry, size, market-to-book, ESG, and ROA. We report t-statistics for differences
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Targeted
Control
Success
No success
Success – control
No success – control
1 v. 2
3 v. 4
3 v. 5
4 v. 6
All cases
Mean
0.145***
0.119***
0.127***
0.171***
0.116***
0.123***
0.026
\(-\)0.045
0.011
0.048
Obs.
846
2544
509
337
1530
1014
3390
846
2039
1351
Lowest ESG quartile
Mean
0.115**
0.009
0.137
0.098**
0.007
0.011
0.106***
0.039
0.130**
0.087**
Obs.
176
525
78
98
234
291
701
176
312
389
Highest ESG quartile
Mean
0.117***
0.203***
0.090**
0.221*
0.149***
0.407*
\(-\)0.086
\(-\)0.132
\(-\)0.060
\(-\)0.185
Obs.
165
495
131
34
393
102
660
165
524
136
Reorganization
Mean
0.147***
0.102***
0.115**
0.172***
0.090***
0.111***
0.045*
\(-\)0.057
0.025
0.061*
Obs.
435
1314
190
245
576
738
1749
435
766
983
Environmental
Mean
0.151***
0.079***
0.108***
0.199**
0.102***
0.052***
0.072**
\(-\)0.091
0.006
0.147***
Obs.
358
1068
190
168
570
498
1426
358
760
666
Social
Mean
0.141***
0.163***
0.160***
0.110**
0.139***
0.201***
\(-\)0.023
0.050
0.022
\(-\)0.091
Obs.
366
1113
223
143
675
438
1479
366
898
581
Governance
Mean
0.138**
0.101***
0.086
0.331**
0.090***
0.141**
0.037
\(-\)0.245
\(-\)0.004
0.190*
Obs.
122
363
96
26
285
78
485
122
381
104
Low E-index
Mean
0.128***
0.068***
0.071***
0.235***
0.037***
0.126***
0.060***
\(-\)0.164
0.034*
0.109***
Obs.
529
1584
347
182
1041
543
2113
529
1388
725
High E-index
Mean
0.156***
0.171***
0.146***
0.180**
0.189***
0.125***
\(-\)0.015
\(-\)0.034
\(-\)0.043
0.056
Obs.
112
336
80
32
240
96
448
112
320
128
Low analyst coverage
Mean
0.156***
0.131***
0.121***
0.195***
0.078***
0.190***
0.025
\(-\)0.074
0.043*
0.005
Obs.
447
1341
235
212
705
636
1788
447
940
848
High analyst coverage
Mean
0.080***
0.060***
0.060**
0.133***
0.061***
0.055**
0.020
\(-\)0.073
\(-\)0.002
0.077**
Obs.
362
1086
261
101
783
303
1448
362
1044
404
Independent company
Mean
0.103***
0.084***
0.083***
0.137***
0.071***
0.105***
0.019
\(-\)0.055
0.011
0.032
Obs.
700
2094
438
262
1314
780
2794
700
1752
1042
Controlling shareholder
Mean
0.185***
0.215***
0.126**
0.230***
0.058**
0.333***
\(-\)0.030
\(-\)0.104
0.069*
\(-\)0.103
Obs.
118
363
51
67
156
207
481
118
207
274
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively
Table 7
Buy-and-hold portfolio returns after completion
The table presents mean buy-and-hold returns after engagements for different event windows and various subsamples. For each subsample, returns are calculated for the entire subsample, the control group, and successful and unsuccessful engagements. The table reports whether the mean is equal to zero and the difference between successful and unsuccessful cases and the control group. The matching sample is based on Mahalanobis score matching on industry, size, market-to-book, ESG, and ROA. We report t-statistics for differences
t = [0]
t = [0,6]
t = [0,12]
Targeted
Control
Success
No success
Targeted
Control
Success
No success
Targeted
Control
Success
No success
All cases
Mean
0.012***
0.005***
0.016***
0.006
0.037***
0.021***
0.055***
0.011
0.064***
0.054***
0.076***
0.048**
Obs.
846
2544
509
337
841
2529
504
337
810
2436
477
333
Diff.
0.008**
0.010*
0.016*
0.044***
0.01
0.028
Lowest ESG quartile
Mean
0.014**
0.006
0.018*
0.01
0.035*
\(-\)0.012
0.058*
0.017
0.091***
0.024
0.075*
0.105***
Obs.
176
525
78
98
176
525
78
98
172
513
75
97
Diff.
0.008
0.008
0.047**
0.041
0.068**
\(-\)0.03
Highest ESG quartile
Mean
0.016***
0.002
0.015**
0.017*
0.047***
0.044***
0.057***
0.005
0.062**
0.065***
0.073**
0.021
Obs.
165
495
131
34
165
495
131
34
155
465
122
33
Diff.
0.013**
\(-\)0.002
0.002
0.052*
\(-\)0.003
0.052
Reorganization
Mean
0.014***
0.007***
0.025***
0.006
0.025*
0.01
0.049**
0.006
0.045**
0.042***
0.054**
0.039
Obs.
435
1314
190
245
435
1314
190
245
424
1281
182
242
Diff.
0.007*
0.019**
0.015
0.044**
0.004
0.016
Environmental
Mean
0.016***
0.001
0.020***
0.012**
0.027**
\(-\)0.003
0.055***
\(-\)0.004
0.047***
0.016
0.051**
0.042
Obs.
358
1068
190
168
354
1056
186
168
335
999
171
164
Diff.
0.015***
0.008
0.030**
0.059***
0.031*
0.009
Social
Mean
0.002
0.004*
0.006
\(-\)0.005
0.039***
0.038***
0.053***
0.018
0.065***
0.087***
0.087***
0.032
Obs.
366
1113
223
143
365
1110
222
143
353
1074
210
143
Diff.
\(-\)0.002
0.01
0.002
0.036*
\(-\)0.022
0.055*
Governance
Mean
0.033***
0.018***
0.034**
0.029*
0.059**
0.043***
0.057**
0.065
0.112***
0.062***
0.096**
0.169
Obs.
122
363
96
26
122
363
96
26
122
363
96
26
Diff.
0.015*
0.004
0.015
\(-\)0.008
0.049
\(-\)0.073
Low E-index
Mean
0.019***
0.004*
0.017***
0.022***
0.033***
0.005
0.041***
0.018
0.065***
0.029***
0.061***
0.072***
Obs.
529
1584
347
182
528
1581
346
182
506
1515
327
179
Diff.
0.015***
\(-\)0.004
0.027**
0.023
0.036**
\(-\)0.011
High E-index
Mean
0.018***
0.014***
0.021**
0.011
0.100***
0.054***
0.121***
0.05
0.109***
0.086***
0.123***
0.076
Obs.
112
336
80
32
109
327
77
32
101
303
70
31
Diff.
0.004
0.01
0.046**
0.071**
0.023
0.048
Low analyst coverage
Mean
0.020***
0.005**
0.031***
0.008
0.052***
0.023***
0.072***
0.031
0.094***
0.057***
0.102***
0.085***
Obs.
447
1341
235
212
445
1335
233
212
432
1296
223
209
Diff.
0.015***
0.022**
0.029**
0.041*
0.037**
0.016
High analyst coverage
Mean
0.007*
0.003
0.004
0.014*
0.019*
0.013*
0.038***
\(-\)0.028
0.035**
0.037***
0.053***
\(-\)0.01
Obs.
362
1086
261
101
359
1077
258
101
341
1023
241
100
Diff.
0.003
\(-\)0.011
0.007
0.066***
\(-\)0.002
0.063**
Independent company
Mean
0.016***
0.005***
0.016***
0.014***
0.044***
0.026***
0.052***
0.031*
0.073***
0.061***
0.075***
0.069***
Obs.
700
2094
438
262
697
2085
435
262
668
1998
410
258
Diff.
0.011***
0.002
0.018*
0.02
0.011
0.007
Controlling shareholder
Mean
\(-\)0.003
0.006
0.020*
\(-\)0.020*
\(-\)0.004
0.002
0.077***
\(-\)0.063**
0.013
0.022
0.066
\(-\)0.025
Obs.
118
363
51
67
116
357
49
67
115
354
48
67
Diff.
\(-\)0.008
0.040***
\(-\)0.006
0.140***
\(-\)0.009
0.09
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively
×
We calculate the cumulative abnormal returns (CARs) for the three time windows following engagement completion (as in Table 7) using the four-factor global Fama–French–Carhart model. We do so for all engaged firms and for the subsamples with successful and unsuccessful engagements. By subtracting the CARs of engaged firms from those of their matched firms, we obtain excess CARs (ECARs), which we report in Table 8.19 The top panel shows that the average ECARs are positive and close to zero (0.5%) but still significantly different from zero in the month after engagement completion (be it successful or unsuccessful). This means that engaged firms slightly outperform non-engaged firms. This difference increases to 2.7% in the 6-month period after the engagement file is closed (but there is no difference between successful or unsuccessful case completion). The firms of which the activist demands a reorganization outperform the matched firms by 4.4% in the six months after the closure of the activist’s case (but the difference between successfully or unsuccessfully closed files is not statistically significant).
Firms in the lowest (ex ante) ESG quartile outperform matched firms by 7.1% (7.5%) in the 6 months (1 year) after the engagement end. This implies that the targeting of low ESG firms prompts them to significantly outperform their non-engaged peers. Successfully engaged low-ESG firms outperform unsuccessfully engaged low-ESG firms. The successfully engaged low-ESG firms have an average ECAR of 8.4% over the 6-month period (outperforming the unsuccessfully engaged firms by 2.4%) and of 11.3% over the year (outperforming the unsuccessfully engaged firms by 6.8%). Similar patterns are not visible for engaged firms with an (ex ante) high ESG classification, as these firms do not obtain significant ECARs. Firms targeted for environmental or governance deficiencies exhibit significant and positive ECARs of 3% over a 6-month period and 14.1% over a one-year period, respectively.20
Figure 3 corroborates the findings in Table 8: the CARs for the successful engagements remain flat for about 6–7 months, after which they decline. The decrease in CARs for unsuccessful cases begins about one month after case completion. The gap in the CARs between successful and unsuccessful cases reaches a maximum after about 8–12 months. For successfully engaged North American targets, CARs remain positive for about 9 months, then rapidly decline; for unsuccessfully engaged North American targets, CARs go down after 2 months, creating a big gap in CARs between successful and unsuccessful targets after about 8-9 months. For European targets, there is hardly a difference in CARs between (un)successful targets; CARs for both gradually decrease after about 9 months.21
Taken together, the results in Tables 7 and 8 imply that the activist can make a significant return by selling its stake in a successfully engaged target 6 to 12 months after closing the case.
Fig. 3
Cumulative abnormal returns after completion. The figure shows cumulative abnormal returns for equally weighted portfolios of engaged companies and above a matched sample. The portfolios are formed at the completion of engagements. Returns are adjusted for Fama-French-Carhart global factors
Table 8
Excess cumulative abnormal returns at case closure
This table reports cumulative abnormal return statistics for various event windows and subsamples in excess of a matched sample. For each subsample, cumulative abnormal return statistics are reported for three event windows. For each event window and subsample combination, we test whether the mean cumulative abnormal return is 0 and the difference between successful and unsuccessful cases. The beginning of an event window is defined as the month when an engagement case is completed, the end of the window is either the month when the engagement is completed or 6 or 12 months following completion. The estimation period is 36 months prior to engagement. We use the Fama–French–Carhart model for the estimation of normal returns. Excess abnormal returns are calculated monthly subtracting the returns of an equally weighted portfolio of matched companies. The matching sample is based on Mahalanobis score matching on industry, size, market-to-book, ESG, and ROA. For each event window and subsample combination we test whether the mean cumulative abnormal return is 0 and the difference between successful and unsuccessful cases. We report t-statistics for differences.
t = [0]
t = [0,6]
t = [0,12]
All
Success
No success
All
Success
No success
All
Success
No success
All cases
Mean
0.005*
0.006
0.007
0.027***
0.022*
0.036**
0.019
0.024
0.012
Obs.
846
509
337
841
504
337
810
477
333
Difference
0.228
\(-\)0.737
0.400
Lowest ESG quartile
Mean
0.006
0.025**
\(-\)0.001
0.071***
0.084**
0.060*
0.075**
0.113**
0.045
Obs.
176
78
98
176
78
98
172
75
97
Difference
2.488***
0.462
0.921
Highest ESG quartile
Mean
0.007
0.002
0.024
0.003
0.004
0.003
\(-\)0.006
\(-\)0.004
\(-\)0.012
Obs.
165
131
34
165
131
34
155
122
33
Difference
\(-\)1.524
0.022
0.102
Reorganization
Mean
0.006
0.011
0.002
0.044***
0.035
0.051***
0.022
0.046
0.005
Obs.
435
190
245
435
190
245
424
182
242
Difference
0.912
\(-\)0.549
0.914
Environmental
Mean
0.009**
0.005
0.014
0.030**
0.008
0.055**
\(-\)0.004
0.001
\(-\)0.010
Obs.
358
190
168
354
186
168
335
171
164
Difference
\(-\)0.887
\(-\)1.711
0.237
Social
Mean
0
0.007
\(-\)0.006
0.015
0.022
0.004
\(-\)0.002
0.004
\(-\)0.011
Obs.
366
223
143
365
222
143
353
210
143
Difference
1.913**
0.654
0.330
Governance
Mean
0.011
0.004
0.041
0.057
0.047
0.094
0.144***
0.109**
0.272***
Obs.
122
96
26
122
96
26
122
96
26
Difference
\(-\)1.098
\(-\)0.547
\(-\)1.425
Low E-index
Mean
0.011*
0.006
0.023**
0.045***
0.028*
0.077***
0.059***
0.041*
0.093***
Obs.
529
347
182
528
346
182
506
327
179
Difference
\(-\)1.329
\(-\)1.831
\(-\)1.294
High E-index
Mean
0.007
0.001
0.024*
0.033*
0.029
0.04
0.001
\(-\)0.007
0.02
Obs.
112
80
32
109
77
32
101
70
31
Difference
\(-\)1.309
\(-\)0.275
\(-\)0.347
Low analyst coverage
Mean
0.014**
0.024**
0.004
0.032**
0.024
0.041*
0.023
0.034
0.012
Obs.
447
235
212
445
233
212
432
223
209
Difference
1.467*
\(-\)0.583
0.491
High analyst coverage
Mean
0.002
\(-\)0.006
0.020*
0.027**
0.018
0.048*
0.025
0.017
0.043
Obs.
362
261
101
359
258
101
341
241
100
Difference
\(-\)1.856
\(-\)1.031
\(-\)0.544
Independent company
Mean
0.007
0.004
0.011
0.024**
0.014
0.041**
0.019
0.014
0.026
Obs.
700
438
262
697
435
262
668
410
258
Difference
\(-\)0.632
\(-\)1.213
\(-\)0.357
Controlling shareholder
Mean
0.01
0.034**
\(-\)0.007
0.04
0.063**
0.023
0.008
0.068
\(-\)0.034
Obs.
118
51
67
116
49
67
115
48
67
Difference
1.636*
0.761
1.26
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively
×
Conclusion
Using a proprietary dataset of a large international, socially responsible activist fund, we analyze the reasons for, and the success of, corporate engagement involving mainly environmental and social issues. We match each engaged firm with three firms that were not engaged, that belong to the same industry, and that are most similar to the engaged firm in terms of size, market-to-book ratio, ROA, and ESG score in the year prior to the engagement.
The activist generally targets large firms with large market shares. Targeted firms are more likely to be in the highest ex ante ESG quartile. This is somewhat surprising, as one would expect the activist to concentrate on firms with poor ESG performance if ESG improvements are expected to generate value. Relative to the matched sample, the target firms have a higher stock market performance and a higher product market share and are more visible (have more analyst coverage). Firms that are engaged on corporate governance issues are somewhat smaller, have a more dispersed ownership structure, and have lower potential growth opportunities (Tobin’s Q), but are otherwise profitable (both in terms of previous year buy-and-hold returns and accounting performance).
Next, we study whether the engagement is successfully completed. Success is defined as compliance with the activist’s demands. One could question the relevance of this definition, considering that in some cases compliance may require little effort from the firm, while in others it may involve substantial changes—e.g., board or asset restructuring or overhauls of ESG-related operations. It is hence not surprising that when a “hard” engagement occurs, the likelihood of successful completion is lower than in cases that just require more ESG transparency or information provision.
We find that engagement success depends neither on joint targeting nor on the position of the main contact in the target firm (management or non-executive directors). More intensive contact between the activist and the target does yield success more frequently, though only for European targets. Companies that were targeted in the past and complied with the activist’s requests are more likely to comply again if targeted. European firms under pressure—those with declines in sales and negative buy-and-hold returns—more frequently adopt the activist’s suggestions. Our results also reveal that firms with a good ESG track record prior to the engagement (e.g., firms in the highest ESG performance quartile in North America and Europe) are more likely to comply. Firms that cared little about ESG issues ex ante seem to continue to do so, as they are reluctant to adopt suggestions by the CSR activist.
The real effects of engagement of the target firm are rather modest. Our differences-in-differences analyses reveal that, on average, accounting performance measures and their components do not significantly improve or change after engagement. The only exception is sales, which grows significantly after the engagement, both statistically and economically.
Interestingly, the mere engagement—independent of whether or not the case is successfully closed—triggers changes in the ESG profile of the target, which are picked up by the independent ESG evaluation providers. After the engagement, firms with poor ex ante ESG performance scores obtain higher scores, and firms with high ex ante ESG performance scores receive lower scores. If the activist correctly identifies companies with an ESG problem, then subsequent ESG ratings may reflect this new information, and the adjusted ESG scores then incorporate the potential ESG problem. In the case of firms with ex ante high scores, this drives the scores down. The engagement seems to reveal that previously low-rated companies are not “lost cases,” and that best-performers might still have room for improvement. As the activist engages companies, the rating agency seems to realize that previous scores did not incorporate all of the activist’s concerns, and that engaged companies still have key ESG points to improve on.
From the activist’s perspective, activism seems to create, at best, modest financial returns during the engagement period, though we find no evidence that targets are negatively affected. Over the engagement period, targeted firms realize returns that are narrowly higher than those of the control firms over the same period. On average, the annualized BHRs of engaged firms are 2.6% higher than those of the control group, but the difference is not statistically significant. Engaged firms do perform significantly better in the engagement period when they are in the (ex ante) lowest ESG quartile, are subject to reorganization, or are engaged for environmental reasons.
On average, the buy-and-hold returns for completed engagements are positive and statistically significant in the month following the case closure (at 1.2%, a significant 80 basis points higher than for the non-engaged control firms). These returns can be dissected into positive returns that stem from the successfully closed cases (generating BHRs of 1.6%), and zero BHRs for unsuccessful engagements. Over longer time windows (e.g. 6 months), successful cases generate returns of 5.5%, whereas unsuccessful ones have a return of zero. Successful reorganizations, which require the most compliance effort from the target, yield BHRs of 1.4% in the month the engagement is completed, increasing to 5.4% over a 1-year time window, but these BHRs are not significantly different from those of the control sample. Still, successful reorganizations generate significantly higher BHRs than unsuccessful attempts, with differences of 1.9% over a 1-month window and 4.4% over a six-month window.
When we partition the engagement files by ex ante ESG performance, we find significant differences: the largest BHRs are generated by targets in the ex ante lowest ESG quartile. Engaging these firms yields BHRs that are 4.7% and 6.8% higher than those of control firms over post-engagement time windows of 6 and 12 months, respectively. For targets in the ex ante highest ESG quartile, post-engagement BHRs are significantly higher (70 basis points) than those of the control firms in the month after the engagement. This finding is mainly due to successful engagements (which yield 2.5% in that month).
When we examine targets that are classified by ESG dimension, we find that environmental engagement leads to significant outperformance (BHRs are 3% higher than those of the control firms over the year after the engagement). Over a 6-month time window after the engagement, successful environmental and social engagements outperform unsuccessful environmental and social engagements by 5.9% and 3.6%, respectively.
When BHRs are calculated over the 18 months starting one month prior to the engagement, the greatest divergence is between successful and unsuccessful engagements, and occurs 6 to 12 months following the completion of the case.
An analysis of excess cumulative abnormal returns—controlling for exposure to the global market, size, book-to-market, and momentum factors and measured relative to the CARs of matched peer firms—shows that that engaged firms slightly outperform non-engaged firms: the average ECARs are positive (0.5%) and significantly different from zero in the month after the completion of the engagement, and augment to 2.7% over the 6-month period after the engagement file is closed. Reorganization demands by the activist make a targeted firm outperform its non-targeted (but otherwise similar) peer company by 4.4% in the 6 months after the completion of the activist’s case. Targeting firms in the lowest (ex ante) ESG quartile pays off, in that these firms outperform their matched peers by 7.1% (7.5%) in the 6 months (1 year) after the activist ends the engagement. Furthermore, successfully engaged low-ESG firms outperform the unsuccessfully engaged low-ESG firms; the former have an average ECAR of 8.4% over the 6-month period (outperforming the unsuccessfully engaged firms by 2.4%) and of 11.3% over the year (outperforming the unsuccessfully engaged firms by 6.8%).
We conclude that our study provides direct evidence that ethical investing and strong performance can go hand-in-hand. The engagement from the activist investor seems most beneficial—both in terms of ESG performance and financial performance—for firms with ex-ante low ESG performance, suggesting that these ethical investors play an important role in helping firms understand how they can improve outcomes for all their stakeholders. Our results do not support the notion that the ESG efforts by activists and firms are merely for marketing or reputational purposes. On the other hand, our findings that the ESG ratings for targeted firms with ex-ante high ESG ratings tend to decline after targeting suggests that some of the activist investor’s concerns have not been previously incorporated in the ratings and are then publicly disclosed due to the activism. This suggests both that ethical engagement affects ESG dislosure and that ESG ratings deserve close scrutiny.
Acknowledgements
We would like to thank the data provider for providing us with detailed, proprietary information on their shareholder activism procedures. We would like to thank the editor, as well as three anonymous referees for their suggestions on improving the paper. We are grateful for comments from Fabio Braggion, Peter Cziraki, Frank de Jong, Bart Dierynck, Elroy Dimson, Joost Driessen, Alex Edmans, Caroline Flammer, Julian Franks, William Goetzmann, Marc Goergen, Muris Hadzic, Camille Hebert, Hao Liang, Alberto Manconi, Ernst Maug, Anjana Rajamani, Zorka Simon, Oliver Spalt, Michael Ungehauer, Servaes van der Meulen, Cara Vansteenkiste, Chendi Zhang, Yang Zhao, and seminar participants at the HAS Summer Workshop in Economics, ICGN, the 2nd Conference on CSR, the Economy, and Financial Markets, University of Mannheim, Cardiff Business School, Ghent University, Tilburg University, and University Paris-Dauphine. An earlier version of this paper was titled “Activism on Corporate Social Responsibility.” Disclaimer: The views, information, or opinions expressed in this paper are solely those of the individuals involved and do not necessarily represent those of Prime Capital AG and its employees.
Declarations
Conflict of interest
The authors declare that they have no conflict of interest.
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Amid a changing regulatory environment, the activist hired a third party analyst firm to evaluate the effects of new legislation on utility companies. The activist was specifically interested in the risks associated with the CO2 emissions of energy companies. After assessing the report, the activist reached out to company XXX on March 12, 2009. In a phone call, the activist requested information on two specific issues related to CO2 emissions. They were interested in the company’s strategies to 1) reach statutory CO2 targets and 2) acquire and construct new power plants. Following up on the phone call, the activist paid a visit to XXX’s headquarters on April 24, 2009, meeting an investor relations officer of the company. At this meeting, the activist elaborated on the requests in more detail, stressing that their ultimate goal was that the company publish a sustainability report in response to these requests. The company representative assured the activist that the company was aware of the changing regulatory environment and that they were already working on a sustainability report to appease investors. Following the publication of the report, the activist replied to the company in email on September 18, 2009, requesting more details on future power plants. This was followed by a further email on December 8. Finally, the company fulfilled all requests of the activist, publishing all information online. After the activist verified the published information, the case was closed as successful on February 25, 2010.
Social
The activist engaged financial institution YYY on March 10, 2006, to acquire more information on its human rights policies, after a January report by BankTrack indicated that YYY reported less information on the topic than its peers. Specifically, the activist was concerned about the ethical standards of the bank corresponding to investments in Russia and third world countries. The first meeting took place at the activist’s offices with an investor relations officer of YYY. This meeting was followed by a conference call on April 6, 2006, during which a YYY executive assured the activist that the bank had “nothing to hide.” Furthermore, the executive explained that they do take human rights issues into account for project financing and investments, although, as this was part of their internal scoring processes, they did not want to disclose details to maintain their competitive position. In response to the request for more transparency, the YYY executive promised that they would publish a sustainability report for 2006. Following the publication of the report, engagers had a last meeting on October 26, 2006, with the investor relations officer to go over the details of the report. As the report covered all concerns that the engager previously raised, the case was closed as “successful”.
Governance
The activist engaged company ZZZ in 2007 concerning the size and composition of the supervisory board of the company. The activist was concerned that the board was not large enough to fully oversee the company’s operations. A further concern was that the CEO of the company was also the chairman of the supervisory board. The activist voiced these concerns in collaboration with other investors at the AGM in mid-2007. ZZZ showed willingness to revise its governance practices. However, the CEO remained the chairman of the board. The activist revisited the case at the 2008 and 2009 AGMs to no avail. Since they could not reach their goal of improving ZZZ’s corporate governance, they closed the case as “unsuccessful” on May 12, 2009.
Ecosystem Services: Alternative Energy, Biodiversity, Eco-Efficiency; Emissions, Effluents and Waste; Nuclear Power, PVC and Phthalates, Tropical Hardwood, Water
Human Rights and Ethics: Animal Testing, Anti-Corruption, Customer Satisfaction, Ethics, Fur, Gambling, Human Rights, Military Production and Sales, Pornography and Adult Entertainment Services, Social Supply Chain Standards, Stakeholder Management & Reporting, Sustainability Reporting
Labor Standards: Attraction & Retention, Controversial Regimes, Forced and Compulsory Labor, Human Capital, Labor Standards, Privacy & Freedom of Speech, Third World, Training & Education, UN Global Compact
Public Health: Access to Medication, Alcohol, Genetic Engineering, Healthy Nutrition, Integration in Products, Intensive Farming & Meat Sale, Product Safety, Tobacco
This table provides variable definitions. All variables based in $ terms, if applicable.
Variable
Definition
Source
ESG scores
ESG sentiment
Annual average of ESG scores within a country. (0–100)
Datastream - Asset4
ESG score
Equally weighted Asset4 score: based on the Environmental, Social, Governance and Economic pillars (0–100)
Environmental score
Environmental pillar score: a company’s impact on living and non-living natural systems, as well as complete ecosystems (0–100)
Social score
Social pillar score: a company’s ability to generate trust and loyalty with its workforce, customers and society (0–100)
Governance score
Governance pillar score: a company’s systems and practices that ensure that its executives and board act in the interest of (long-term) shareholders (0–100)
Economic score
Economic pillar score: a company’s capacity to generate sustainable growth and returns through the efficient use of its assets and resources (0–100)
Entrenchment index
Index of entrenchment measures (E-index): poison pill, golden parachute, staggered board, bylaws and lock-ins (0–1)
Risk and performance
BHR
Buy-and-hold stock return over 12 months
Datastream
Volatility
Stock return volatility
Amihud ILLIQ
Amihud illiquidity measure multiplied by $1 million
Asset turnover
(Total sales)/(Total assets)
Profit margin
(Net income)/(Total sales)
ROA
(Net income)/(Total assets)
ROE
(Net income)/(Book value of equity)
Sales growth
Year-over-year sales growth
Sales market share
Percentage of total industry sales
Market-to-book
(Market value of equity)/(Book value of equity)
Tobin’s Q
(Market value of equity + Total book liabilities)/(Book value of equity + Total book liabilities)
Cash and expenses
Cash holding
(Total cash)/(Total assets)
Datastream
CapEX
(Capital Expenditures)/(Total assets)
Operating expenses
(Operating expenses)/(Sales)
SGA
(Selling, General and Administrative expenses)/(Total assets)
Size and capital structure
Log total assets
Natural log of total assets
Datastream
Log sales
Natural log of total sales
Log market equity
Natural log of total market capitalization
Book leverage
(Total book liabilities)/(Total book liabilities + Book value of equity)
Tangibility ratio
(Plant, property and equipment)/(Total assets)
Equity issuance
(Annual change in equity issue and redemption)/(Lagged total assets)
Debt issuance
(Annual change in long and current debt)/(Lagged total assets)
Other
Dividend yield
(Total dividends paid)/(Market value of equity + Market value of preferred shares)
Datastream
Dividend payout
(Total dividends paid)/(Net income)
Company age
Years since incorporation or IPO date
Analysts
Mean number of analysts issuing earnings (EPS) forecasts annually
I/B/E/S
Common law
Indicator if a company is headquartered in a common law country, following the classification of Liang and Renneboog (2017). 1 for common law, 0 otherwise.
Datastream
Ownership
Holding of engager
Portfolio holdings of engager (total)
Morningstar
Toehold
Indicator variable; 1 if the engager increases its holdings prior to targeting
Toehold increase
Indicator variable; 1 if the engager increases its holdings over the course of targeting
Average ownership
Mean of ownership stakes
Orbis
Number of blockholders
Number of owners with a +5% stake
Long-term investors
Holdings by pension and mutual funds
Hedge funds and PE
Holdings by edge funds, venture capitalists and private equity firms
Individuals and family
Holdings by individuals and families
Independent company
Indicator if a company has no majority shareholder with a stake larger than 25%
Miscellaneous
Contact number
Number of contacts with the target company
Activist
Contact type
The dominant channel of communication
Contacted executives
Role of contact person at target company; 1 for executive officers, 0 otherwise
Geographic FE
Fixed effects for Asia, Europe, North America and Other regions
Industry FE
Fixed effects for 17 Fama-French industries
Joint targeting
Targeting in collaboration with other activists; 1 if jointly targeted, 0 otherwise
Length of sequence
Time span of targeting in days
Previous engagements
Number of previous cases with the same company
Success
The originally defined goal is achieved; 1 for success, 0 otherwise
Success streak
Number of previous successful cases with the same company
Receptiveness
1 if the target firm is initially willing to collaborate with the activist; 0 otherwise
Reorganization
1 for material request aimed at changing the company’s operations; 0 for an engagement aimed at enhancing transparency
Appendix D: Analysis by Geography and Legal Origin
Global heat map by number of engagements. The figure depicts the geographical concentration of engagement cases across the globe
Fig. 5
Country legal origin. The graph indicates the legal origin of countries where engaged companies are headquartered. We follow Liang and Renneboog (2017) in classifying countries by their legal origin
Table 10
Cases by domicile and legal origin
This table reports the country distribution of engagement cases as well as the legal origin of the country. We follow Liang and Renneboog (2017) in classifying countries by their legal origin.
ISO code
Country name
Cases
Legal origin
ARG
Argentina
5
French
AUS
Australia
15
English
AUT
Austria
3
German
BEL
Belgium
5
French
BGD
Bangladesh
1
English
BGR
Bulgaria
2
Socialist
BIH
Bosnia and Herzegovina
1
Socialist
BRA
Brazil
17
French
CAN
Canada
31
English
CHE
Switzerland
28
German
CHL
Chile
2
French
CHN
China
7
Socialist
CIV
Côte d’Ivoire
1
French
COL
Colombia
1
French
DEU
Germany
66
German
DNK
Denmark
6
Scandinavian
EGY
Egypt
1
French
ESP
Spain
12
French
FIN
Finland
8
Scandinavian
FRA
France
69
French
GBR
United Kingdom
90
English
GRC
Greece
2
French
HKG
Hong Kong
12
English
HRV
Croatia
2
German
IND
India
20
English
IRL
Ireland
2
English
ISR
Israel
1
English
ITA
Italy
23
French
JPN
Japan
22
German
KEN
Kenya
1
English
KOR
South Korea
26
German
LTU
Lithuania
3
French
LUX
Luxembourg
2
French
MEX
Mexico
6
French
MKD
Macedonia
1
Socialist
MYS
Malaysia
1
English
NGA
Nigeria
2
English
NLD
Netherlands
106
French
NOR
Norway
7
Scandinavian
NZL
New Zealand
1
English
PAK
Pakistan
9
English
PRT
Portugal
1
French
RUS
Russia
2
Socialist
SGP
Singapore
18
English
SVN
Slovenia
1
German
SWE
Sweden
10
Scandinavian
TUR
Turkey
2
French
TWN
Taiwan
2
German
USA
United States of America
171
English
VEN
Venezuela
1
French
ZAF
South Africa
19
English
Table 11
Analysis of targeting by regions
This table reports the marginal effects obtained from probit regressions on the probability of targeting relative to a matched sample. The first two columns report regression results for the whole sample of engagements (1–2), while the second, third, and fourth sets of columns refer to North American (3–4), European (5–6), and Other domiciled (7–8) companies, respectively.The dependent variable equals 1 if the company is targeted and 0 otherwise. Marginal effects are evaluated at the mean of the respective independent variable. Standard errors are clustered at the firm level. The matching sample is determined by Mahalanobis score matching on industry, size, market-to-book, ESG, and ROA. Variable definitions are provided in the Appendix
Full sample
North America
Europe
Other
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Log total assets
\(-\)0.031**
0.025
0.029
0.041
\(-\)0.067***
\(-\)0.002
0.030
0.081**
Tobin’s Q
0.000
0.002
\(-\)0.004
0.011
\(-\)0.008
\(-\)0.001
0.027
0.021
Sales growth
\(-\)0.056
\(-\)0.072
\(-\)0.183***
\(-\)0.212**
0.019
\(-\)0.011
\(-\)0.048
\(-\)0.148
BHR over 12 months
0.077***
0.096***
\(-\)0.046
\(-\)0.018
0.082***
0.087***
0.073**
0.032
ROA
0.253
0.097
0.641*
0.522
\(-\)0.065
\(-\)0.225
0.209
\(-\)0.153
Sales market share
3.899***
2.752***
3.304***
3.646**
3.764***
2.209***
1.628
0.083
Cash holding
\(-\)0.097
0.066
0.138
0.217
\(-\)0.109
0.121
\(-\)0.212
\(-\)0.474
Book leverage
0.078
0.038
0.016
0.136
0.036
\(-\)0.049
\(-\)0.037
\(-\)0.115
Dividend yield
0.547
0.953
\(-\)0.056
0.975
0.990*
0.715
0.495
1.917**
CapEX
0.103
0.271
0.059
\(-\)0.027
\(-\)0.444
0.037
0.980**
0.280
Amihud ILLIQ
\(-\)0.006
\(-\)0.544**
\(-\)0.003
\(-\)214.903***
0.002
\(-\)0.255**
\(-\)0.005
\(-\)0.414
Analysts
0.012***
0.010***
\(-\)0.001
\(-\)0.000
0.017***
0.010***
\(-\)0.008**
\(-\)0.002
SGA
0.044
0.165
0.128
0.180
0.053
0.122
\(-\)0.051
0.348
Common law
\(-\)0.071**
\(-\)0.028
0.483***
0.000
\(-\)0.188***
\(-\)0.098***
\(-\)0.015
0.100*
Previous engments
\(-\)0.023**
\(-\)0.030***
\(-\)0.036
\(-\)0.043
\(-\)0.022**
\(-\)0.029***
\(-\)0.020
0.019
Holding of engager
3.027
47.694**
2.662*
27.015**
Independent company
\(-\)0.005
\(-\)0.056
0.014
0.105
ESG sentiment
0.008***
0.027
\(-\)0.057
\(-\)0.339**
Entrenchment index
\(-\)0.025
\(-\)0.008
0.010***
\(-\)0.010***
ESG score
\(-\)0.160***
0.030
\(-\)0.111*
\(-\)0.022
Year dummy
yes
yes
yes
yes
yes
yes
yes
yes
Industry dummy
yes
yes
yes
yes
yes
yes
yes
yes
Geographic dummy
yes
yes
no
no
no
no
no
no
Pseudo \(\text {R}^2\)
0.12
0.23
0.22
0.28
0.33
0.46
0.07
0.35
N
2,567
2,028
698
438
1,345
1,174
524
254
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively
Table 12
Analysis of success by regions
This table reports the marginal effects obtained from linear probability regressions on the probability of success. The dependent variable equals 1 if the engagement is successful and 0 otherwise. The first two columns report regression results for the whole sample of engagements (1–2), while the second, third, and fourth sets of columns refer to North American (3–4), European (5–6) and Other domiciled (7–8) companies, respectively. Standard errors are clustered at the firm level. The dummy “Reorganization” takes the value 1 for reorganization cases and 0 otherwise. The dummy variable “Joint targeting” equals one for cases where the engager contacts the company with other activists. The variable “Contacted executives” is 1 if executive management is contacted and 0 otherwise. “Number of activities” and “Success streak” refer to the number of contacts per case and the number of previous successful cases with the company. Other variable definitions are provided in the Appendix
Full sample
North America
Europe
Other
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Reorganization
\(-\)0.163***
\(-\)0.144**
\(-\)0.274*
\(-\)0.255
\(-\)0.143***
\(-\)0.120**
0.079
0.053
Joint targeting
0.094*
0.039
0.258**
0.232*
0.001
\(-\)0.043
0.165
\(-\)0.251
Contacted executives
\(-\)0.111**
\(-\)0.090
\(-\)0.160
\(-\)0.236
0.017
0.045
\(-\)0.374**
0.029
Number of contacts
0.008
0.006
0.003
\(-\)0.003
0.014
0.011
0.037
0.051
Success streak
0.010
0.010
0.046
0.008
0.015
0.008
0.102
0.181
Log total assets
0.020
\(-\)0.036
\(-\)0.023
\(-\)0.094*
0.052*
0.054
0.049
0.013
Tobin’s Q
\(-\)0.030
\(-\)0.023
0.018
0.005
\(-\)0.037
\(-\)0.021
\(-\)0.045
\(-\)0.253*
Sales growth
\(-\)0.357***
\(-\)0.333***
\(-\)0.083
\(-\)0.152
\(-\)0.415***
\(-\)0.447***
\(-\)0.289
0.211
BHR over 12 months
0.036
\(-\)0.019
0.202**
0.184*
\(-\)0.081
\(-\)0.200**
0.079
0.457
ROA
\(-\)0.342
\(-\)0.596
\(-\)0.820
\(-\)1.555*
0.154
0.121
\(-\)0.116
\(-\)4.706
Sales market share
1.357
1.903**
1.829
1.447
\(-\)0.002
\(-\)0.630
0.046
\(-\)6.106
Cash holding
\(-\)0.392
\(-\)0.804**
\(-\)1.026**
\(-\)1.082**
\(-\)0.289
\(-\)0.673
0.449
1.259
Book leverage
0.040
\(-\)0.103
\(-\)0.371*
\(-\)0.456**
0.199
0.123
0.235
\(-\)0.791
Dividend yield
0.230
0.490
1.048
4.164**
\(-\)1.661
\(-\)2.226
1.770
6.248
CapEX
\(-\)0.258
0.515
1.398*
2.045**
\(-\)1.177*
\(-\)0.597
\(-\)0.276
1.091
Amihud ILLIQ
\(-\)0.011
0.299
0.099***
\(-\)277.833***
0.072
0.226
\(-\)0.053
19.770
Analysts
0.001
\(-\)0.001
\(-\)0.002
\(-\)0.002
0.001
\(-\)0.003
0.000
\(-\)0.010
SGA
0.725***
0.597***
0.442
0.342
0.619***
0.663***
0.476
1.033
Common law
\(-\)0.008
\(-\)0.031
-
-
-
-
-
-
Toehold
0.008
0.012
\(-\)0.002
0.065
Toehold increase
\(-\)0.036
\(-\)0.013
0.039
\(-\)0.032
Independent company
0.111
0.056
0.011
0.082
Entrenchment index
0.033
0.028
0.239*
1.086
ESG sentiment
0.000
\(-\)0.057***
0.002
\(-\)0.013
ESG score
0.366**
0.474*
0.108
0.972
Year dummy
yes
yes
yes
yes
yes
yes
yes
yes
Industry dummy
yes
yes
no
no
no
no
no
no
Geographic dummy
yes
yes
no
no
no
no
no
no
Adjusted \(\text {R}^2\)
0.25
0.24
0.18
0.28
0.29
0.26
0.20
0.69
N
616
471
186
162
317
268
113
41
*, ** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively
The GSIA definitions of sustainable investment, published in the Global Sustainable Investment Alliance (2013), have emerged as a global standard of classification. These are: 1. Negative/Exclusionary Screening: the exclusion from a fund or portfolio of certain sectors, companies or practices based on specific ESG criteria; 2. Positive/Best-in-class screening: investment in sectors, companies or projects selected for positive ESG performance relative to industry peers; 3. Norms-Based Screening: screening of investments against minimum standards of business practice based on international norms, such as those issued by the OECD, ILO, UN and UNICEF; 4. ESG Integration: the systematic and explicit inclusion by investment managers of environmental, social and governance factors into financial analysis; 5. Sustainability Themed Investing: investment in themes or assets specifically related to sustainability (for example clean energy, green technology or sustainable agriculture); 6. Impact/Community Investing: targeted investments aimed at solving social or environmental problems, and including community investing, where capital is specifically directed to traditionally underserved individuals or communities, as well as financing that is provided to businesses with a clear social or environmental purpose; and 7. Corporate Engagement and Shareholder Action: the use of shareholder power to influence corporate behavior, including through direct corporate engagement (i.e., communicating with senior management and/or boards of companies), filing or co-filing shareholder proposals, and proxy voting that is guided by comprehensive ESG guidelines.
However, Cremers et al. (2020) find that firms targeted by activist hedge funds have similar stock returns and lower increases in Tobin’s Q compared to ex ante similar firms that were not targeted by activist hedge funds; this suggests that, while activist hedge funds may have stock-picking ability, their capacity to improve firm performance, on average, is unclear.
This is in line with Cremers and Petajisto (2009), who show that mutual funds’ outperformance of their benchmark is positively correlated with the portion of actively managed stocks in their portfolio.
A more detailed overview for the subthemes is presented in “Appendix B”. In order to keep things tractable and to avoid working with very small subsamples, in the multivariate analysis we will focus on the three main ESG topics (for which we also distinguish between reorganization and transparency cases).
A success rate of 60% is higher than the rate reported in Dimson et al. (2015), but our sample covers a different time period. A high success rate in activist cases is not unprecedented as, for example, Klein and Zur (2009) report a success rate of 60% and 65% for hedge fund and private equity activists, respectively. Furthermore, engagements that are triggered by previously unforeseen events or violations are likely to receive more support from corporate management, as discussed in Lee and Xiao (2020).
In Fig. 1, we show the distribution of engagement cases and their success rate over time. The figure shows a drop in the number of engagements around the Great Recession (2007–2009), with particularly low case counts in 2007 and 2008. The success rate of the cases initiated in this period also varies broadly, with the lowest rate—in terms of cases initiated in that year which were eventually completed being in 2009.
We present detailed breakdowns of legal origin by country in “Appendix D”, following Liang and Renneboog (2017). In our analyses, we concentrate on common law vs. civil law, as ESG is shown to be the most varied across this separation.
As a robustness test, we repeat the analysis in the first panels of Table 3 for varying levels of engagement, whereby the ordering refers to differences in the effort level in engagement. Specifically, we estimate ordered probit models in which the dependent variable is one for engagements triggered for reasons of transparency (“light engagements”), two for engagements triggered for reorganization reasons (“strong engagements”), and zero in cases with no engagement. In unreported results, we find that our previous findings are robust to ordering and that, for the strong engagements, the coefficients are larger (in absolute terms).
We repeat the analysis of Table 3 with geographical segmentation between North American, European, and other domiciled companies. The analysis is presented in “Appendix D”, Table 11. We find that the results are qualitatively similar.
We repeat the analysis of Table 4 with geographical segmentation between North American, European, and other domiciled companies. The analysis is presented in “Appendix D”, Table 12.
In unreported results, we define the pre- and post-periods of Eqs. 2 and 3 in various ways. Specifically, we move the cutoffs to 1–3 years after the start and to 1–3 years after the completion of engagements. The results are qualitatively similar to the ones presented here.
We repeat the analysis of Table 7 for the subsamples of North American, European, and Other cases. The results are largely in line with the ones reported for the overall sample (although some subsamples, partitioned based on geography and (un)successfulness, become small). The results are available upon request.
As a robustness check, we also use Fama–French–Carhart factors, 17 Fama–French industry portfolios, as well as size and book-to-market matched portfolios. We find that the results are qualitatively similar.
Because the activist focuses mostly on the E and S factors and less on governance, the subsample of (un)successful cases is rather small, which may explain the reason why the unsuccessfully closed cases yield higher ECARs than the successful ones.
