Top

Financial Markets and Portfolio Management

Published in:

26-11-2024

Financial knowledge acquisition and trading behavior: empirical evidence from an online information tool

Authors: Anthony Bellofatto, Marie-Hélène Broihanne, Catherine D’Hondt

Published in: Financial Markets and Portfolio Management | Issue 1/2025

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Using the advent of the MiFID regulation in Europe, we conduct a robust quasi-natural experiment in a large sample of retail investors to analyze how some of them changed their trading behavior after being granted access to an online information tool. Focusing on investors who asked for the information tool, we find that subjective financial literacy and education are both positively related to financial knowledge acquisition, as suggested in Lusardi et al. (2017)′s model. Using propensity score matched difference-in-differences regressions, we show that financial knowledge acquisition has mixed effects on trading behavior: better portfolio diversification but higher trading intensity and lower net returns. Our empirical evidence indicates financial knowledge acquisition being complementary to financial literacy (instead of a substitute). It also reveals that the investment profile does matter to explain heterogeneity in behavior across investors.

next article Political uncertainty and sovereign bond markets

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

MiFID is central to the regulation of financial markets in the EU since it regulates the provision of investment services in financial instruments by banks and investment firms as well as the operation of stock exchanges and alternative trading venues. One of its core objectives is to ensure a high degree of harmonized protection for investors in financial instruments. MiFID I (2004/39/EC) was the first version of this directive that came into force in late 2007, while a revision of it (known as MiFID II (2014/65/UE)) was implemented in January 2018. For further details on the MiFID regulation, please visit the European Commission website (https://ec.europa.eu/info/law/markets-financial-instruments-mifid-ii-directive-2014-65-eu_en).

The brokerage house that provided us with the data did not offer portfolio management services during the sample period under scrutiny in this paper.

Robo-advice has emerged as a cost-effective alternative to traditional financial advisors. Formally, a robo-advisor is a digital platform that provides automated, algorithm-driven financial planning services with little to no human supervision (Investopedia). Such a platform typically collects information from clients about their financial situation and future goals through a quick risk profile survey. The data collected are then used to offer advice and automatically invest the client’s assets, most often based on optimized mean-variance passive indexing strategies. The goal is to automatically create and manage an investment portfolio suitable to the client’s risk-return profile.

The 10,270 matched non S-investors includes 4663 duplicates since we allow replacement as explained in Sect. 4.1.

In Jappelli and Padula (2013)’s model, financial literacy and wealth are determined jointly, which gives rise to a positive correlation between financial literacy and wealth over individuals’ life-cycle. Lusardi et al. (2017) show that financial knowledge acquisition is a major determinant of retirement wealth inequality. Barthel and Lei (2021) build a model in which consumers’ investment in financial literacy drives financial advice-seeking and financial literacy acquisition to be substitutes.

Jappelli and Padula (2013) use the Survey of Health, Aging, Retirement in Europe (SHARE) but no measure of investment in financial knowledge. Lusardi et al. (2017) use simulations to obtain empirical insights from their theoretical model. Barthel and Lei (2021) work on the 2016 SCF Survey and consider investment in financial literacy for respondents who assess that they were “attending [an] investment seminar or investment club”.

Abreu and Mendes (2012) use a survey conducted in 2000 by the Portuguese Securities Market Commission in which 1559 investors were interviewed. Tauni et al. (2015) analyze the survey results of 333 individual investors in Chinese futures markets. In both papers, survey data are based on questions such as “How often do you buy and sell financial assets?”. Furthermore, these authors focus exclusively on trading frequency and do not analyze trading behavior in a broader sense or performance. Guiso and Jappelli (2006) is an unpublished paper that provides evidence based on actual behavior. Nevertheless, these authors rely on indirect measures of financial knowledge acquisition, since they use a questionnaire to measure the time investors spent acquiring financial knowledge from different sources (such as newspapers, internet, etc.).

Their model relies on two main assumptions that are (1) investment in financial knowledge acquisition increases the return on savings, and (2) it also increases the probability to receive “good” advice, which additionally increases the return on savings. Barthel and Lei (2021) solve their model by taking different functional forms for the probability that advice is “good” and show that financial knowledge acquisition is higher when no professional advice is bought.

Jappelli and Padula (2013) propose a consumption model in which individuals who are endowed with an initial stock of financial literacy can increase their net returns from intertemporal trades by investing in financial literacy. Their model implies that financial literacy and wealth are positively correlated over the consumers’ life cycle and shows that there exists an optimal investment in literacy. Barthel and Lei (2021) specifically address whether investment in financial literacy and financial advice-seeking are substitutes or complements when both advice costs money and the quality of advice fluctuates. Their model is similar to that of Jappelli and Padula (2013), except that individuals explicitly decide whether to purchase advice from a financial adviser.

In this situation, the investor may either avoid any information likely to create dissonance or process the available information so as to reduce it. Cognitive dissonance might explain why an investor may decide to ignore free information to preserve his/her beliefs. This could explain why, although investors obtain access to a new financial knowledge set, they can decide not to trade. A related theoretical direction is to assume, as the model of Andries and Haddad (2020) does, that retail investors exhibit information aversion that makes them inattentive to flows of news.

Several papers (e.g., D’Hondt and Roger 2017; Bellofatto et al. 2018; D’Hondt et al. 2020; Desagre and D’Hondt 2021; D’Hondt et al. 2021a, b) rely on the same database but focus on different samples, according to their research questions.

Only guidelines and general rules for implementing the MiFID questionnaires are available. They contain neither a clear definition nor a specific list of complex instruments. The general consensus is that derivatives (such as options, futures, and warrants) and structured products are typically complex instruments. However, due to the precautionary principle, the broker that provided us with the data considered all securities that differ from common stocks as complex instruments. The consequence is that all the retail investors were invited to complete the A-test.

www.eurofidai.org.

As mentioned earlier, some detailed items of the S-test are provided in Table 8 (in appendix) as examples.

The monthly portfolio turnover is calculated as the sum of all the purchases and sales in a particular month, divided by the average of the portfolio values at the beginning and the end of this particular month. Since the average portfolio value can sometimes be very small, extreme values for turnover are likely. Hence, the median turnover is more representative. The average turnover has to be interpreted with caution because it can be very high without having any economic meaning. In Sect. 5, portfolio turnovers are winsorized with a 1% cutoff.

This means that a given stock may be considered a lottery-like stock for a specific month and not necessarily over the full sample period.

Some questions are grouped together when they relate to the same topic. For example, the A-test contains distinct questions regarding the investor’s knowledge about options, future contracts, warrants, structured products, etc. For convenience, we group these questions together and create a single item related to the knowledge of complex instruments.

Financially literate households are known to invest more in stocks (e.g., Van Rooij et al. 2011; Balloch et al. 2014) and in derivatives (Hsiao and Tsai 2018).

Since we allow replacement, we actually have 5607 matched non S-investors, some of them being matched to several S-investors.

According to Angrist and Pischke (2008), the inclusion of covariates generates smaller residual variance since “a regression along this point is the result that even in a scenario with no omitted variable bias, the long regression generates more precise estimates of the coefficients on the variables included in the short regression whenever these variables have some predictive power for outcomes because these covariates lead to a smaller residual variance” (Chapter 3, p. 62).

We did not consider the effect of financial knowledge acquisition on the home/local bias because Belgian retail investors tend to be well diversified internationally (e.g., D’Hondt et al. 2020) and we found no theoretical background to hypothesize that the use of the information tool would lead S-investors to exhibit a lower home/local bias. In our sample, only 34% of the total trades are executed on Belgian stocks. Regarding the disposition effect (DE) that is another widespread bias among retail investors, it has been disregarded because it is not possible to calculate a satisfactory DE measure per investor and per month. Indeed, Table 9 (in the appendix) shows that, on average, non S-investors (S-investors) executed a total of 121 (158) trades over the entire sample period, which corresponds to approximately 1.09 (1.42) trade per month. We are grateful to an anonymous reviewer for raising these points.

When estimating our DiD regressions using the entire sample period from 2003 to 2012, results reveal overall similar patterns.

Univariate comparisons across S-investors depending on the investment profile are available in Table 14 in the appendix.

Recently, Oehler et al. (2021) used a survey to identify the determinants of the use of robo-advisors. These authors show that, aside from classical determinants (such as the willingness to take risk), personal characteristics (such as locus of control) influence this decision. This finding is consistent with those reported in Tauni et al. (2015) showing that investors’ personality traits (such as extraversion, conscientiousness and openness) moderate the relationship between financial knowledge acquisition and asset allocation decisions.

Abreu, M., Mendes, V.: Information, overconfidence and trading: Do the sources of information matter? J. Econ. Psychol. 33, 868–881 (2012)CrossRef

Ackert, L.F., Church, B.K., Englis, B.: The asset allocation decision and investor heterogeneity: a puzzle? J. Econ. Behav. Organ. 47, 423–433 (2002)CrossRef

Agnew, J., Balduzzi, P., Sunden, A.: Portfolio choice and trading in a large 401 (k) plan. Am. Econ. Rev. 93, 193–215 (2003)CrossRef

Andries, M., Haddad, V.: Information aversion. J. Political Econ. 128, 1901–1939 (2020)CrossRef

Angrist, J., Pischke, J.S.: Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press, Princeton (2008)CrossRef

Argentesi, E., Lütkepohl, H., Motta, M.: Acquisition of information and share prices: an empirical investigation of cognitive dissonance. German Econ. Rev. 11, 381–396 (2010)CrossRef

Balloch, A., Nicolae, A., Philip, D.: Stock market literacy, trust, and participation. Rev. Finance 19, 1925–1963 (2014)CrossRef

Barber, B.M., Odean, T.: Boys will be boys: Gender, overconfidence, and common stock investment. Q. J. Econ. 116, 261–292 (2001)CrossRef

Barber, B.M., Odean, T.: The internet and the investor. J. Econ. Perspect. 15, 41–54 (2001)CrossRef

Barber, B.M., Odean, T.: Online investors: Do the slow die first? Rev. Financ. Stud. 15, 455–488 (2002)CrossRef

Barthel, A.C., Lei, S.: Investment in financial literacy and financial advice-seeking: Substitutes or complements? Q. Rev. Econ. Finance 81, 385–396 (2021)CrossRef

Bellofatto, A., D’Hondt, C., De Winne, R.: Subjective financial literacy and retail investors’ behavior. J. Bank Finance 92, 168–181 (2018)CrossRef

Bernasek, A., Shwiff, S.: Gender, risk, and retirement. J. Econ. Issues 35, 345–356 (2001)CrossRef

Bertrand, M., Duflo, E., Mullainathan, S.: How much should we trust differences-in-differences estimates? Q. J. Econ. 119, 249–275 (2004)CrossRef

Bhattacharya, U., Hackethal, A., Kaesler, S., Loos, B., Meyer, S.: Is unbiased financial advice to retail investors sufficient? Answers from a large field study. Rev. Financ. Stud. 25, 975–1032 (2012)CrossRef

Bhushan, R.: Firm characteristics and analyst following. J. Account. Econ. 11, 255–274 (1989)CrossRef

Bluethgen, R., Gintschel, A., Hackethal, A., Mueller, A.: Financial advice and individual investors’ portfolios. Available at SSRN 968197 (2008)

Brashear-Alejandro, T., Kang, J., Groza, M.D.: Leveraging loyalty programs to build customer-company identification. J. Bus. Res. 69, 1190–1198 (2016)CrossRef

Brogaard, J., Li, D., Xia, Y.: Stock liquidity and default risk. J. Financ. Econ. 124, 486–502 (2017)CrossRef

Broihanne, M., Orkut, H.: MiFID questionnaire answers and stock market participation. Rev. Econ. 7, 1164–1191 (2021)

Broihanne, M.H., Merli, M., Roger, P.: Diversification, gambling and market forces. Rev. Quantit. Finance Account. 47, 129–157 (2016)CrossRef

Broihanne, M.H., Orkut, H.: Investment goals and mental accounting in French retail clients. Finance 39, 107–144 (2018)CrossRef

Calcagno, R., Monticone, C.: Financial literacy and the demand for financial advice. J. Bank. Finance 50, 363–380 (2015)CrossRef

Calvet, L.E., Campbell, J.Y., Sodini, P.: Measuring the financial sophistication of households. Am. Econ. Rev. 99, 393–98 (2009)CrossRef

Chalmers, J., Reuter, J.: What is the impact of financial advisors on retirement portfolio choices and outcomes? National Bureau of Economic Research (NB10-05) (2010)

Charness, G., Gneezy, U.: Strong evidence for gender differences in risk taking. J. Econ. Behav. Organ. 83, 50–58 (2012)CrossRef

Claessens, S., Glaessner, T., Klingebiel, D.: Electronic finance: reshaping the financial landscape around the world. J. Financ. Serv. Res 22, 29–61 (2002)CrossRef

Collins, J.M.: Financial advice: A substitute for financial literacy? Financ. Serv. Rev. 21, 307–322 (2012)

Committee of European securities regulators.: a consumer’s guide to MiFID: Investing in financial products (2008)

Desagre, C., D’Hondt, C.: Googlization and retail trading activity. J. Behav. Exp. Finance 29, 100453 (2021)CrossRef

D’Hondt, C., De Winne, R., Ghysels, E., Raymond, S.: Artificial intelligence alter egos: Who might benefit from robo-investing? J. Empir. Finance 59, 278–299 (2020)CrossRef

D’Hondt, C., De Winne, R., Merli, M.: Do retail investors bite off more than they can chew? A close look at their return objectives. J. Econ. Behav. Organ. 188, 879–902 (2021a)CrossRef

D’Hondt, C., McGowan, R., Roger, P.: Trading leveraged exchange-traded products is hazardous to your wealth. Q. Rev. Econ. Finance 80, 287–302 (2021b)CrossRef

D’Hondt, C., Roger, P.: Investor sentiment and stock return predictability: the power of ignorance. Finance 38, 7–37 (2017)CrossRef

Dorn, D., Huberman, G.: Talk and action: What individual investors say and what they do. Rev. Finance 9, 437–481 (2005)CrossRef

Dwyer, P.D., Gilkeson, J.H., List, J.A.: Gender differences in revealed risk taking: evidence from mutual fund investors. Econ. Lett. 76, 151–158 (2002)CrossRef

Fang, V., Tian, X., Tice, S.: Does stock liquidity enhance or impede firm innovation? J. Finance 69, 2085–2125 (2014)CrossRef

Festinger, L.: A Theory of Cognitive Dissonance. Stanford University Press, Redwood (1957)CrossRef

Gaudecker, H.M.V.: How does household portfolio diversification vary with financial literacy and financial advice? J. Finance 70, 489–507 (2015)CrossRef

Georgarakos, D., Inderst, R.: Financial advice and stock market participation. Working Paper No 1296, European Central Bank (2014)

Gerhardt, R., Hackethal, A.: The influence of financial advisors on household portfolios: A study on private investors switching to financial advice. Available at SSRN 1343607 (2009)

Glaser, M.: Online broker investors: Demographic information, investment strategy, portfolio positions, and trading activity. Available at SSRN 975985 (2003)

Glaser, M., Weber, M.: Which past returns affect trading volume? J. Financ. Mark. 12, 1–31 (2009)CrossRef

Goetzmann, W.N., Kumar, A.: Equity portfolio diversification. Rev. Finance 12, 433–463 (2008)CrossRef

Grinblatt, M., Keloharju, M.: How distance, language, and culture influence stockholdings and trades. J. Finance 56, 1053–1073 (2001)CrossRef

Grossman, S.J.: On the efficiency of competitive stock markets where trades have diverse information. J. Finance 31, 573–585 (1976)CrossRef

Grossman, S.J., Stiglitz, J.E.: On the impossibility of informationally efficient markets. Am. Econ. Rev. 70, 393–408 (1980)

Guiso, L., Jappelli, T.: Information acquisition and portfolio performance. CSEF Working Paper No. 167.r (2006)

Guiso, L., Jappelli, T.: Financial literacy and portfolio diversification. Working Paper, European University Institute (2008)

Hackethal, A., Haliassos, M., Jappelli, T.: Financial advisors: A case of babysitters? J. Bank. Finance 36, 509–524 (2012)CrossRef

Haslem, J.A.: Why do mutual fund investors employ financial advisors? J. Invest. 17, 91–94 (2008)CrossRef

Hoechle, D., Ruenzi, S., Schaub, N., Schmid, M.: The impact of financial advice on trade performance and behavioral biases. Rev. Finance 21, 871–910 (2017)CrossRef

Hoffmann, A.O., Post, T., Pennings, J.M.: Individual investor perceptions and behavior during the financial crisis. J. Bank. Finance 37, 60–74 (2013)CrossRef

Horn, M., Oehler, A., Wendt, S.: Fintech for consumers and retail investors: opportunities and risks of digital payment and investment services. In: Walker, T., Gramlich, D., Bitar, M., Fardnia, P. (eds.) Ecological, Societal, and Technological Risks and the Financial Sector. Palgrave Macmillan, London (2020)

Hsiao, Y.J., Tsai, W.C.: Financial literacy and participation in the derivatives markets. J. Bank. Finance 88, 15–29 (2018)CrossRef

Hung, A., Yoong, J.: Asking for help: survey and experimental evidence on financial advice and behavior change. In: Mitchell, Olivia S., Smetters, Kent (eds.) The Market for Retirement Financial Advice. Published to Oxford Scholarship Online, Oxford (2014)

Jappelli, T., Padula, M.: Investment in financial literacy and saving decisions. J. Bank. Finance 37, 2779–2792 (2013)CrossRef

Kang, J., Alejandro, T.B., Groza, M.D.: Customer-company identification and the effectiveness of loyalty programs. J. Bus. Res. 68, 464–471 (2015)CrossRef

Koestner, M., Loos, B., Meyer, S., Hackethal, A.: Do individual investors learn from their mistakes? J. Bus. Econ. 87, 669–703 (2017)

Kramer, M.M.: Financial advice and individual investor portfolio performance. Financ. Manag. 41, 395–428 (2012)CrossRef

Kreis, H., Mafael, A.: The influence of customer loyalty program design on the relationship between customer motives and value perception. J. Retail. Consum. Serv. 21, 590–600 (2014)CrossRef

Kumar, A.: Who gambles in the stock market? J. Finance 64, 1889–1933 (2009)CrossRef

Kumar, A., Lee, C.: Retail investors sentiment and returns comovements. J. Finance 61, 2451–2486 (2006)CrossRef

Lee, E.J., Eastwood, D.B., Lee, J.: A sample selection model of consumer adoption of computer banking. J. Financ. Serv. Res. 26, 263–275 (2002)CrossRef

Lusardi, A., Michaud, P.C., Mitchell, O.S.: Optimal financial knowledge and wealth inequality. J. Political Econ. 125, 431–477 (2017)CrossRef

Mazzoli, C., Marinelli, N.: Determinants of risk-suitable investment portfolios: evidence from a sample of Italian householders. Int. J. Finance Bank. 1, 13–28 (2014)

Migliavacca, M.: Keep your customer knowledgeable: financial advisors as educators. Eur. J. Finance 26, 402–419 (2020)CrossRef

Nicolosi, G., Peng, L., Zhu, N.: Do individual investors learn from their trading experience? J. Financ. Mark. 12, 317–336 (2009)CrossRef

Oehler, A., Horn, M., Wendt, S.: Investor characteristics and their impact on the decision to use a robo-advisor. J. Financ. Serv. Res. 62(1), 91–125 (2021)

Orkut, H.: Foreign stock investment and sophistication of French retail investors. Finance 42, 39–80 (2021)CrossRef

Polkovnichenko, V.: Individual investor portfolios. In: Kent Baker, H., Nofsinger, J.R. (eds.) Behavioral Finance: Investors, Corporations, and Markets, pp. 539–557. Wiley, New York (2010)CrossRef

Rosenbaum, P.R., Rubin, D.B.: The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55 (1983)CrossRef

Seasholes, M.S., Zhu, N.: Individual investors and local bias. J. Finance 65, 1987–2010 (2010)CrossRef

Shestopaloff, Y., Shestopaloff, A.: A hierarchy of methods for calculating rates of return. J. Perform. Measurement 12, 39–52 (2007)

Shum, P., Faig, M.: What explains household stock holdings? J. Bank. Finance 30, 2579–2597 (2006)CrossRef

Stuart, E.A.: Matching methods for causal inference: a review and a look forward. Statist. Sci. 25, 1–21 (2010)CrossRef

Tauni, M.Z., Fang, H.X., Yousaf, S., et al.: The influence of investor personality traits on information acquisition and trading behavior: evidence from Chinese futures exchange. Personal. Individ. Differ. 87, 248–255 (2015)CrossRef

Van Rooij, M., Lusardi, A., Alessie, R.: Financial literacy and stock market participation. J. Financ. Econ. 101, 449–472 (2011)CrossRef

Vissing-Jorgensen, A.: Perspectives on behavioral finance: Does “irrationality’’ disappear with wealth? Evidence from expectations and actions. In: Gertler, M., Rogoff, K. (eds.) NBER Macroeconomics Annual 2003, pp. 139–208. The MIT Press, Cambridge (2003)

Volpe, R.P., Kotel, J.E., Chen, H.: A survey of investment literacy among online investors. Financ. Couns. Plan. 13, 1–13 (2002)

Title: Financial knowledge acquisition and trading behavior: empirical evidence from an online information tool
Authors: Anthony Bellofatto
Marie-Hélène Broihanne
Catherine D’Hondt
Publication date: 26-11-2024
Publisher: Springer Berlin Heidelberg
Published in: Financial Markets and Portfolio Management / Issue 1/2025
Print ISSN: 1934-4554
Electronic ISSN: 2373-8529
DOI: https://doi.org/10.1007/s11408-024-00459-0

Springer Professional

Financial knowledge acquisition and trading behavior: empirical evidence from an online information tool

Abstract

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 1/2025

Report of the co-editors 2024

Political uncertainty and sovereign bond markets

Extending the demand system approach to asset pricing

Ben Mezrich, Dumb Money (Harper Collins Publishers, 2023)

Second hand or second generation? The performance of secondary buyouts

Premium Partner

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Report of the co-editors 2024

Political uncertainty and sovereign bond markets

Extending the demand system approach to asset pricing

Ben Mezrich, Dumb Money (Harper Collins Publishers, 2023)

Second hand or second generation? The performance of secondary buyouts