Bridge over ocean
12 September 2019 CFA Institute Journal Review

Mark Twain’s Cat: Investment Experience, Categorical Thinking, and Stock Selection (Digest summary)

  1. Biharilal Deora, CFA, CIPM

Does prior investment experience in specific industries offer a blessing or a disadvantage for future investing? For investors, prior alpha in a given industry increases the likelihood of investing in the same industry. The author finds a stronger effect for experiences that are more recent and for investors who are less sophisticated or diversified and also that it does not enhance wealth.

What Is the Investment Issue?

Behavioral finance, financial history, and lessons from successful investors demonstrate that past experience affects investors’ subsequent financial decisions, including risk taking, financing, and buying common stocks. The effect of positive past investment experience with a particular stock increases the likelihood of repurchasing that stock, but does prior success in stocks in a given industry increase the likelihood of subsequent purchases in the same industry? Do investors categorize the stocks by industry, size, or value? What drives such experience or biases? Do investors sort industries into mental categorizations?

How Did the Author Conduct This Research?

The dataset includes the trading records of 47,993 (scaled down from 78,000) households at a large discount brokerage house from 1991 to 1996, the Standard Industrial Classification (SIC) codes for 10 industry groupings (F10), per Fama and French (Journal of Financial Economics 1997), are from Compustat and CRSP. The author controls for four confounding effects that can drive correlations between past and future trades in the same industry: (1) industry momentum trading, (2) wealth effect (increase in portfolio value), (3) investor’s heterogeneity (degree of sophistication), and (4) portfolio rebalancing.

The results are tested using a probit regression model, with an additional robustness check with Fama–French 48 subindustry classifications (F48). The author defines the top five and bottom five industries by industry average returns and industry time-fixed effects. 

What Are the Findings and Implications for Investors and Investment Professionals?

The author finds additional evidence of investors’ greater propensity to purchase new stocks in an industry if they previously earned positive excess returns in that same industry. The effect varies by time horizon, degree of investor sophistication, and diversification. The effects are significant even with greater industry categorization, such as F48, or when size or value is used for categorizations.

For investors with concentrated portfolios, a positive previous experience in an industry increases the probability of buying new stocks in that industry by 1.32%—an effect that is dampened by about two-thirds in the case of a more diversified portfolio.

The author identifies potential explanations for the experience effect, which may be apt for consideration for performance attribution as well as continuing education for market participants.

  • Naive reinforcement learning: Investors’ past good experiences drive posterior expected returns upward, and thus investors are likely to buy stock in the same industry. At the same time, investors may build up biases against industries in which they have lost money, which could lead them to miss good investment opportunities elsewhere.
  • Learning about ability: The positive (negative) experiences in a particular industry may convince investors that they are more skilled (less skilled) in one industry, leading them to specialize in (diversify away from) a diversified (concentrated) set of industries.
  • Learning by trading: Investors may become more precise or astute or have a greater propensity to procure/process the information for the industry for future investment given their past success. The investor returns used in the models suggest a lack of learning for investors, but this effect may vary based on investor sophistication.

The research also shows self-attribution bias among investors, which leads to lower wealth resulting from delayed exits from inferior stocks/industries because of finer categorization and, eventually, to an impact on asset pricing and industry momentum.

We’re using cookies, but you can turn them off in Privacy Settings.  Otherwise, you are agreeing to our use of cookies.  Accepting cookies does not mean that we are collecting personal data. Learn more in our Privacy Policy.