Layoff announcements and intra-day market reactions

Event studies measure “the impact of a specific event on the value of a firm” (MacKinlay 1997, p. 13) and are commonly used to study the effects of layoff announcements on market returns. Although event studies can be traced back to the 1930s (e.g., Dolley 1933), the first event study focused on evaluating the effects of layoff announcements on stock prices was published in 1991 (see Worrell et al. 1991). The results of most studies indicate negative stock price reactions to layoff announcements. Some studies, however, provide evidence of positive responses (e.g., Brookman et al. 2007). Various studies also report mixed results, highlighting the relevance of the reasons for the layoff (e.g., Lin and Rozeff 1993; Chalos and Chen 2002) or the market conditions at the moment of the announcement (Farber and Hallock 2009; Marshall et al. 2012). One potential reason that explains the existing mixed findings can be due to the sampling frequency of data. In particular, the extant literature commonly analyzes abnormal returns (ARs) or cumulative abnormal returns (CARs) on daily data. However, the use of daily data brings problems that intra-day data analysis may alleviate (see, for example, Lee et al. 1993). First, and most importantly, a daily data event window provides no information on the intra-day behavior of stock prices around the events, and these immediate market reactions cannot be gauged by using only closing prices. For example, Lee et al. (1993, p. 354) argue that “since most of the price reaction to a news event occurs within minutes after the announcement, closing quotes may not reflect the announcement effect.” Second, the use of daily data requires a longer estimation window that can bring problems due to time-varying volatility, that is heteroscedastic asset returns (see, e.g., Engle and Bollerslev 1986; Bollerslev et al. 1992). Moreover, because the use of daily returns requires long estimation windows, several unrelated events may falsify the results, especially if they are not spread over time. Importantly, intra-day data may allow the consideration of more than one layoff announcement per firm in a month.

Our paper contributes to the extant investigation of investor reactions to corporate layoffs, but instead of using daily data, we use intra-day data from the Helsinki Stock Exchange, which enables us to identify possible immediate market reactions (and in certain cases even subsequent reactions) and to analyze how such complex information is processed in the markets during the course of a day. Delayed reactions are of particular interest to us, and our paper focuses on answering how markets react to initial announcements and final decisions by assessing (i) if markets react to announcements immediately, (ii) if the reactions are positive or negative, (iii) what the magnitude and statistical significance of the reactions are, (iv) how markets post-process the announcements during the following hours, (v) what the relation between paired initial announcements and final decisions is in terms of returns, and (vi) if company background characteristics and market reactions are linked. We also provide an additional test by analyzing intra-day versus daily stock market reactions to layoff announcements.

The results of this paper, which are statistically significant, show that investors react strongly and negatively to initial announcements on the aggregated level and that the reaction occurs within the first 10 min after layoff negotiations have been announced. However, the immediate reaction then retracts by an upward post-drift in aggregated cumulative abnormal returns during the following hours, perhaps because markets need hours to process such unpredictable and complex information and its consequences, even if their first reaction was strong and immediate. We refer to this phenomenon as the “intra-day post-reversal effect”. Moreover, our results included no significant reactions to the final announcement at the significance level of 1 %, suggesting that the final decisions have been partially predictable or perhaps known in advance. We further evaluated the pairs of initial announcements and their corresponding final decisions and gained weak evidence that if an initial announcement conveyed no useful information about the stock valuation (i.e., the market did not react significantly to the initial announcement), the associated decision may affect stock returns strongly. Additionally, our analysis demonstrates the importance of the use of intra-day data: The reactions, which can be strong but short-lived, are identifiable with intra-day data only. Furthermore, we found no statistical link between various company background characteristics (number of employees, net sales, operating profit, EBT, EBTR, assets/liabilities) and the magnitude or type (positive or negative) of reactions to either initial announcements or final decisions.

This paper contributes in various ways to the extant literature. First, this is the first study to evaluate the effect of employee downsizing announcements in stock exchange releases on market returns using intra-day market data, and therefore, it casts light on the immediate effects of layoff announcements. Importantly, we observe not only an immediate negative reaction but also a reversal effect on the aggregated level, which we consider the main result of this paper. Second, to our knowledge, our analysis of the pairs of initial announcements and final decisions is unique. Third, by drawing on a sample of 369 announcements of companies trading on the Helsinki Stock Exchange (OMXH), this is the first study to analyze the effects of layoff announcements on market returns in a Nordic context. Overall, this paper responds to the call for more research on downsizing and its consequences (Cascio et al. 1997; Datta et al. 2010).

The rest of the paper is organized as follows. The following section is a literature review and develops a hypothesis. The third and fourth sections discuss the data and methodology of the study. The fifth section reports results where we first analyze 10-min abnormal returns and cumulative abnormal returns on initial announcements and final decisions before performing an explanatory analysis of the relationship between initial announcements and the corresponding final decisions. We also perform an additional analysis to regress the abnormal returns against background information on companies’ characteristics. The final section discusses the conclusions and limitations of the paper.

A layoff can be defined as a temporary or permanent termination of one or more employees from the payroll of an organization (Cornfield 1983, p. 504; Chen et al. 2001, p. 172). Other similar definitions include reductions in force, downsizing, downscoping, reduction in personnel, and workforce reduction. Whereas reductions in force “denote large, permanent, frequently reactive” layoffs, downsizing commonly signifies “a streamlining of operations” to improve a company’s competitive position (De Meuse et al. 1994, p. 522). Furthermore, Lee (1997, p. 880)¹ compares downsizing to downscoping and defines the latter as “the process through which firms reemphasize their core businesses.” For this paper, we adopted Cornfield’s definition (1983) and consider any termination of employment a layoff regardless of its magnitude.

The literature review by Datta et al. (2010) groups the antecedents of layoffs into two main categories, namely, environmental (e.g., economic environment and types of industries) and organizational factors (e.g., human resources policies and organizational strategies). As for consequences, layoffs may allow an organization to change its competitive position within the industry and fight financial distress (Lee 1997). In addition, improvement of profit margins and labor productivity after layoffs were noted in a study by Chen et al. (2001). Wayhan and Werner (2000) conclude that reductions in the workforce improve financial performance, particularly in the short term. However, according to Hillier et al. (2007, p. 469), downsizing leads only to a “marginal improvement” in company performance. Moreover, Cascio (1993) states that for many companies, layoffs do not result in increased profits. In fact, De Meuse et al. (2004) argue that after downsizing, a company will need several years to financially recover. In addition, studies show that a company’s financial performance, in fact, deteriorates after layoffs (De Meuse et al. 1994). Other negative consequences include loss of valuable knowhow and triggering of negative attitudes, for example, increase in fear and a drop in motivation, morale, punctuality, and general satisfaction among the workers who are not laid off (Cascio 1993; Franz et al. 1998; Wayhan and Werner 2000; De Meuse et al. 2004).

Event study methodologies are applied in very different contexts, including marketing (see Corrado 2011), mergers and acquisitions (see Mentz and Schiereck 2008), law (MacKinlay 1997) and layoffs (see Elayan et al. 1998; Farber and Hallock 2009). MacKinlay (1997, p. 13) notes that in “the majority of applications, the focus is the effect of an event on the price of a particular class of securities in the firm, most often common equity.” Event studies assume that changes in a firm’s stock price reflect investor expectations of future performance after important strategic moves have been announced (Lee 1997; Nixon et al. 2004).

According to the existing literature, layoff announcements can result in positive, negative, or mixed stock price reactions. Investors may react positively to a layoff announcement if they perceive this action as improving company performance and generating a positive net present cash flow when comparing layoff and non-layoff scenarios (Elayan et al. 1998; Nixon et al. 2004). In particular, the market may react positively if investors view layoffs as a strategy for overcoming financial difficulties, as resulting from restructuring or consolidation, as a pure-efficiency cost-cutting action, as a measure to reorganize and ultimately reduce costs, as a decision to control product prices to improve competitiveness, or as a way to increase sales, profitability, and firm value (Worrell et al. 1991; Lin and Rozeff 1993; Chatrath et al. 1995; Palmon et al. 1997). Moreover, the market may act neutrally if the layoff announcement conveys information that investors already know or if the perceived positive and negative aspects offset each other (Worrell et al. 1991; Chatrath et al. 1995). The results by Cascio et al. (1997) suggest that the total returns on the common stock of downsizing companies are better when compared to their industries. Other positive market responses are discussed in Wayhan and Werner (2000) and Brookman et al. (2007).

However, investors may perceive layoff announcements negatively if they contain bad news about a company’s financial performance and if layoffs are perceived as defensive (i.e., as reactions to difficulties). Examples of negative news include declining sales, money losses, unexpected lower earnings, signals that the firm and/or its growth opportunities are in a more precarious state than previously thought, closure of plants or facilities, negative revision of future operating prospects, new information about adverse market conditions, or an insufficient layoff (Worrell et al. 1991; Lin and Rozeff 1993; Chatrath et al. 1995; Palmon et al. 1997; Elayan et al. 1998; Chen et al. 2001; Capelle-Blancard and Couderc 2006; Hillier et al. 2007). Negative market reactions have been observed in studies by Worrell et al. (1991), Lin and Rozeff (1993), Lee (1997), Elayan et al. (1998), Franz et al. (1998), Hallock (1998), Chen et al. (2001), Nixon et al. (2004), Capelle-Blancard and Couderc (2006), and Hillier et al. (2007). Last, Goins and Gruca (2008) found that layoff announcements result in a negative but statistically non-significant market reaction.

Some scholars have noted mixed reactions depending on the time or market conditions upon the announcement. For example, Farber and Hallock (2009), who studied the period from 1970 to 1999, found that whereas layoff announcements tend to result in negative stock price reactions, the reaction becomes less negative over time. Similarly, Chatrath et al.’s (1995) results suggest that investor reactions to layoff announcements changed from statistically significantly negative abnormal returns in the 1980s to statistically significant and positive returns in the period from 1991 to 1992. In addition, Marshall et al. (2012) found that markets reacted positively to layoff announcements during rising financial markets and negatively during the 2008 financial crisis. Mixed reactions may also depend on the reason given for the layoff. Palmon et al. (1997) conclude that whereas negative abnormal returns follow statements of declining demand, announcements of efficiency improvement tend to result in positive abnormal returns. Furthermore, Wertheim and Robinson (2000) argue that the market reacts differently depending on whether the layoff announcement signals financial distress or potential benefits. In line with this, the results by Chalos and Chen (2002) show that layoff announcements about plant closings resulted in slightly negative market reactions whereas those about strategic plans to refocus lines of business resulted in statistically significantly positive abnormal returns.

Consequently, given the far-reaching consequences associated with employee reduction and the expected market reaction to the first layoff announcement, we hypothesize regarding our research questions (i–iii) that.

A layoff announcement is an unscheduled event, which “provides a signal to the market regarding the firm’s current state as well as its future ability to compete effectively in the marketplace” (Nixon et al. 2004, p. 1122). Moreover, the relevant literature seems to acknowledge that markets react to announcements and not to actual layoffs (Worrell et al. 1991). Ferguson (2015, p. 322) notes that given the limitation of resources and cognitive constraints, investors “rely on the news to filter negative information.” Ferguson (2015, p. 323) also notes that “investors overreact to highly visible news”. Overall, announcements on layoffs can be informationally complex due to their multi-dimensional and partially soft character, and thus, it can be demanding to convert the information contents to Euros. Although “hard information” is easy to process (e.g., the information on income statements that follows a standardized template), “soft information” is often communicated in text, as in the case of layoff announcements. As Engelberg (2009) explains: “The textual data is qualitative, not easily comparable across firms (e.g., it is not easy to compare ‘demand is weak’ with ‘management is inexperienced’), dependent upon who collects it (e.g., not everyone may agree that ‘demand is weak’), and thus, difficult to interpret, store and pass on without loss of information.” Consequently, given that investors may overreact to highly visible news (Ferguson 2015) and that such soft information is difficult to interpret (Engelberg 2009), we hypothesize the following regarding our research question (iv):

In addition, an increase in available information–for example corporate decisions, such as earnings announcements–reduces information asymmetry (Libby et al. 2002; Lee and O’Neill 2003; Ke 2013). Therefore, the period before an initial layoff announcement should carry more information asymmetry than the period after the announcement. According to De Meuse et al. (2004), most studies consider only the effects of the first layoff announcement because investors assume that companies will perform the announced layoffs. Moreover, because the information content of a final decision may either confirm or disconfirm the expected layoffs, it is unlikely to provide any surprising information. Consequently, we hypothesize the following regarding our research questions (i–iii):

In addition to these three hypotheses, we perform an explanatory analysis of the relationship between initial announcements and corresponding final decisions to answer question (v).

Finally, previous studies have analyzed the relationship between various company background characteristics (e.g., financial data, number of employees, industry type) and layoff announcements. According to Datta et al. (2010), relationships have been found between layoff announcements and other variables, including economic conditions, industry deregulation, industry type, financial performance, market performance, firm productivity, firm size, market share, and reputation. Therefore, regarding question (vi), our last hypothesis is formulated as follows:

In testing the last hypothesis, our paper is in line with previous studies that analyze the relationships between firm characteristics and market returns.

Our data consists of company stock exchange announcements and intra-day stock price data (both provided by NASDAQ OMX Helsinki), and information on company backgrounds drawn mainly from published annual reports. Stock releases were collected from internet pages of the Finnish commercial newspaper Kauppalehti (www.​kauppalehti.​fi) provides freely available stock and press releases of all publicly owned companies. In our analysis, there is one announcement time-stamp for each event, where the first time-stamp is always applied. However, we considered only stock releases in this study because the content of press releases is commonly known to the public well before the release is published (sometimes several days in advance). As a result of this decision, our sample did not include any layoff announcements of the company Nokia because all of their un-confounded layoff announcements during the analysis period were published in press releases. The exclusion of press releases that are not immediately published after the decision has become public is crucial in order to spot immediate reactions with intra-day data. Overall, non-delayed news announcements with fresh time-stamps are the basis of our data sample.

Our research period ranges from January 1, 2006 to December 31, 2010, and the raw data comprised 20,816 stock exchange announcements published by the companies listed on NASDAQ OMX Helsinki. We identified 404 layoff announcements from 80 companies during the research period. We deleted 35 announcements with confounding information, released at non-trading hours or containing intra-day market data on poor liquidity. As a result, our final sample consisted of 369 layoff announcements. Importantly, these intra-day data events came with precise time stamps, which allowed us to analyze immediate reactions to the layoff announcements. Thus, because we did not use multiple-day cumulative abnormal returns, our analysis was not subject to possible confounding events.

The intra-day high-frequency stock price (trade) data, provided by NASDAQ, covers the dates between January 2006 and December 2010. Our abnormal return calculation measured the deviation of an individual stock price from the market index, that is OMXHPI, which is a capitalization-weighted price index that follows all firms listed on NASDAQ OMX Helsinki. The index follows changes in the general investment environment and is not dependent on the volatility of a single firm’s share price or changes in a single sector price. In addition to market index information, we acquired from Nasdaq all stock market transactions on the NASDAQ OMX Helsinki over the five-year research period. The data included all completed transactions during the period together with transaction-specific information, such as number of shares, price, and time of transaction. The total number of individual stock transactions was 68,827,956.

The data is available every second, but we applied 10-min intervals to eliminate the effects of micro-structure noise (Aït-Sahalia et al. 2005). Compared to the existing literature on event studies that typically rely on daily closing prices, this stock market data is very accurate and rich with almost 500 daily observations. This is important in analyzing the length of the investors’ reaction time concerning layoff announcements and the investors’ processing of the information during the announcement day. The estimation window was 510 steps long with a step size of 10 min, which is about 10 trading days. Therefore, instead of using two-year daily data in the estimation windows, with intra-day data we obtained the same number of observations in a 10-day window. This lessens markedly the effects of confounding events and heteroscedasticity (time-varying volatility and correlation).

Finally, most of our data on background company information was obtained from the companies’ published annual reports. This background information included the number of employees, net sales, operating profit, EBIT, net profit, and total assets over a six-year period from 2005 to 2010.

Event studies typically use a market model, where the return of security i is regressed against the return on the market portfolio R _mt :where \(\alpha_{i}\) and \(\beta_{i}\) are constants. An alternative model is a constant mean model, for whichwhere \(\alpha_{i}\) is a constant. As pointed out by Mucklow (1994), a constant mean model is superior to a market model when relying on intra-day data.² For a robustness check, we used constant mean and market portfolio return models, and because we obtained very similar results by using intra-day data, only the results for the constant mean model are reported.

The estimation period is from \(T_{0} + 1\) to \(T_{1}\) with \(L_{1} = T_{1} - T_{0},\) and the event window is from \(T_{1} + 1\) to \(T_{2}\) with \(L_{2} = T_{2} - T_{1}\). We used the same notation as Campbell et al. Let \({\mathbf{R}}_{i} = \left[ {R_{{i,T_{0} + 1}} + \cdots + R_{{i,T_{1} }} } \right]^{{\prime }}\) be an \((L_{1} \times 1)\) vector, \({\mathbf{X}}_{i} = \left[ {{\mathbf{i}}, {\mathbf{R}}_{m} } \right]\) be an \((L_{1} \times 2)\) matrix with a vector of ones in the first column and market returns \({\mathbf{R}}_{m} = \left[ {R_{{m,T_{0} + 1}} + \cdots + R_{{m,T_{1} }} } \right]^{{\prime }}\) in the second column, and \({\varvec{\uptheta}}_{i} = \left[ {\alpha_{i} \beta_{i} } \right]^{{\prime }}\) be a \((2 \times 1)\) vector. Then the ordinary least squares estimators areWhen parameter estimates \({\varvec{\uptheta}}_{i}\) are applied to the \((L_{2} \times 1)\) returns from the event window, the estimate for the abnormal return vector is \({\hat{\mathbf{\epsilon }}}_{i}^{*} = {\mathbf{R}}_{i}^{*} - {\mathbf{X}}_{i}^{*} {\hat{\mathbf{\theta }}}_{i} ,\) where \({\mathbf{R}}_{i}^{*} = \left[ {R_{{i,T_{1} + 1}} + \cdots + R_{{i,T_{2} }} } \right]^{{\prime }}\) is an \((L_{2} \times 1)\) returns vector from the event window, and \({\mathbf{X}}_{i}^{*} = \left[ {{\mathbf{i}}, {\mathbf{R}}_{m}^{*} } \right]\) is an \((L_{2} \times 2)\) matrix with a vector of ones in the first column and market returns \({\mathbf{R}}_{m}^{*} = \left[ {R_{{m,T_{1} + 1}} + \cdots + R_{{m,T_{2} }} } \right]^{{\prime }}\) in the second column. Then, as shown in Campbell et al. (1998), the covariance matrix \({\mathbf{V}}_{i}\) is \({\mathbf{V}}_{i} = \hat{\sigma }_{{\epsilon_{i} }}^{2} \left( {{\mathbf{I}} + {\mathbf{X}}_{i}^{*} \left( {{\mathbf{X}}_{i}^{{\prime }} {\mathbf{X}}_{i} } \right)^{ - 1} {\mathbf{X}}_{i}^{{*{\prime }}} } \right),\) wherein I is the \((L_{2} \times L_{2} )\) identity matrix.³

Under H₀, the null hypothesis is \({\varvec{\upepsilon}}_{i}^{*} \sim\,N\left( {0, {\mathbf{V}}_{i} } \right)\). Therefore, to test H₀, let us denote the standardized cumulative abnormal return of stock i from \(\tau_{1}\) to \(\tau_{2}\) aswhere \(\widehat{CAR}_{{i,\left( {\tau_{1} ,\tau_{2} } \right)}}\) is the cumulative abnormal return from \(\tau_{1}\) to \(\tau_{2}\) with a standard deviation of \(\hat{\sigma }_{{i,\left( {\tau_{1} ,\tau_{2} } \right)}}\). In particular, \(\widehat{AR}_{i,\tau } \equiv\varvec{\gamma}^{{\prime }} {\hat{\mathbf{\epsilon }}}_{i}^{*}\), where \(\varvec{\gamma}\) is \((L_{2} \times 1)\) with one in a position \(\tau_{1} - T_{1}\) to \(\tau_{2} - T_{1}\) and \(\hat{\sigma }_{{i,(\tau_{1} ,\tau_{2} )}} \equiv\varvec{\gamma}^{{\prime }} {\mathbf{V}}_{i}\varvec{\gamma}\).

Let N be the number of announcements. Then \(\overline{SCAR}_{{\left( {\tau_{1} ,\tau_{2} } \right)}} = \frac{1}{N}\widehat{SCAR}_{{i,\left( {\tau_{1} ,\tau_{2} } \right)}}\) is normally distributed in large samples with a mean of zero and a variance of \((L_{1} - 2)/(N(L_{1} - 4))\); therefore, the null hypothesis can be tested usingSimilarly, we may calculate a test statistic for standardized non-cumulative abnormal returns at time \(\tau\):where \(\overline{SAR}_{\tau } = \frac{1}{N}\widehat{SAR}_{i,\tau }\) and \(\widehat{SCAR}_{i,\tau } = \widehat{CAR}_{i,\tau } /\hat{\sigma }_{i,\tau }\).

A nonparametric test statistic is also available [see, e.g., Campbell et al. (1998, pp. 172–173)], but this parametric test seems to be more commonly used in the current literature.

This study is the first to evaluate the effect of employee layoff announcements on market returns using intra-day market data. Altogether, 369 announcements by 80 companies were scrutinized, and the stock price reactions to these announcements were analyzed by calculating the cumulative abnormal returns. Using intra-day data, we analyzed the reactions that occurred in the first minutes after the announcements.

Various conclusions can be drawn from our study. First, on average, stock markets immediately react negatively and strongly to initial layoff announcements with statistical significance (supports Hypothesis 1). However, the first reaction, which is immediate and strong, retracts during the following hours showing no long-term persistence (supports Hypothesis 2). In fact, the processing of the initial announcements seems to take several hours as investors probably are updating their beliefs and completing the assessment of the consequences of the announcements.

Second, final decisions seem to convey no useful information for investors, suggesting that a final decision is predictable and already priced before its announcement (supports Hypothesis 3). Another possibility is an information leak before the announcement is made public. Consequently, final decisions do not seem to drive stock prices, a finding that supports the common practice of focusing only on the first announcement. Third, our results did not highlight any statistically significant relationship between firm characteristics (e.g., number of employees, net sales, operating profit, EBT, EBTR, total assets, industry) and the sign/magnitude of market reactions. Similarly, no statistically significant relationship was found between the sign/magnitude of stock price reactions and reasons for layoffs in announcements (Hypothesis 4 not supported).

Moreover, for the initial announcement and its corresponding final decision (i.e., the pairs), if the initial announcement conveys information that is not useful for stock valuation, then the final decision may greatly affect stock returns. Conversely, if the initial announcement conveys information useful for stock valuation, then the final decision may only marginally affect stock returns. This is an interesting result and suggests that although reactions to final decisions are not statistically significant on the aggregated level, investors may react to final decisions if they did not react to the initial announcements.

Our work has answered the call for more research on downsizing and its consequences (Cascio et al. 1997; Datta et al. 2010). Future research could take note of the methodology used in our study. With intra-day data, we were able to identify the market reactions that are not present when analyses are conducted with daily closing prices. There may be various phenomena leading to these results, including the nature of formation of closing prices, dilution of reactions in time, information-processing. Importantly, we showed that with intra-day data we can find immediate reactions to complex information that are then revised in the following hours, potentially driven by information post-processing. This result may provide clues for future research on detecting reactions to other events as well. Moreover, future research could take note of the methodology used in this paper and draw on intra-day data to analyze layoff announcements in other economic and geographic scenarios. In addition, the effects of employee reduction on market returns make for an interesting and insightful research topic. For example, researchers could follow the suggestion of Oxley et al. (2009) and analyze the effects of layoff announcements on the abnormal returns of rival companies. In this context, it would be interesting to study if layoff announcements are linked to attenuation or enhancement of competition.

Last, this paper has some limitations. Our sample of pairs (formed of the initial announcements and related final decisions) was small in comparison to the total sample. Therefore, future research could consider larger announcement samples to compile a statistically more robust sample of pairs. Moreover, some authors (Farber and Hallock 2009; Marshall et al. 2012) have noted that markets tend to react negatively or positively to layoff announcements depending on the prevailing economic conditions (booms or recessions). The data in this study comprises mostly market reactions during the housing bubble recession that started in 2008. Therefore, future studies that use intra-day data could consider and compare layoff announcements during economic booms and depressions.