Latest Economic Review: Sunpointe Illuminations

AI and Behavioral Finance

As investors, we all love reviewing an investment that’s soared in value. We love not just the increasingly higher dollar value, but how smart it makes us feel about our ability to predict this particular future. And as honest investors, we know this feeling is biased. If we understand how this self-attribution bias creeps into our thinking, we can become more effective at selecting investments, reviewing results, and avoiding well-marketed but poorly structured schemes.

Self-attribution bias refers to the tendency of people to ascribe their successes to innate talents while blaming failures on outside influences.  Take the previously mentioned example. Suppose an investor makes an investment in a particular stock that goes up in value.  The reason it went up is not due to factors such as competitive advantage, economic conditions, or competitor failures but rather to the investor’s ability to predict the future.

Professor Meng Wang of Georgia State University’s Robinson College of Business  wrote “Heads I Win, Tails It’s Chance: Mutual Fund Performance Self-Attribution” (September 2023).   Wang “investigates the presence of self-attribution bias among mutual fund managers and evaluates its impacts on trading outcomes. He developed a GPT-based Natural Language Processing (NLP) software designed to identify attribution information from mutual funds’ self-assessments of performance in their shareholder reports.

According to Professor Wang, on average, mutual fund managers exhibit a significant self-attribution bias.  To perform the analysis, Professor Wang examined the narrative attribution of performance by mutual fund managers in their N-CSR filings. In that disclosure, these managers highlight what significantly contributed to, and detracted from, fund performance as well as their views on the attributing factors behind those contributions and/or detractions.

Using the previously mentioned NLP model he found that, on average, 41% of the factors attributed to performance contributors were external, while 59% were actually internal contributors. And as you might expect, 83% of the factors attributed to performance detractors were external, while 17% were internal. This result supports the hypothesis that mutual fund managers tend to internalize successes (i.e., performance contributors) – attributing them to skill – and externalize failures (i.e., performance detractors) – attributing them to bad luck.

The critical takeaways from Professor Wang’s paper:

  1. Big picture:  AI is a powerful tool that, when used properly, can provide insights into very complex problems.  The future is bright leveraging the power of AI, especially in studies like this. As a Behavioral Finance researcher, Wang’s work highlights the types of insights we’re excited to use new technologies to examine more deeply.
  1. When you are evaluating a mutual fund, pay attention to the disclosure commentary and see if it appears as though the manager is exhibiting self-attribution bias. And if so, take heed of that, and consider it when making an investment decision.  Also look at the level of turnover – this may be a sign of lack of consistency of thought and potentially higher tax cost.
  1. There is an old phrase in the investment industry that goes “don’t confuse brains with a bull market.” This is so true. Yes, there is investment skill, but a certain percentage is always due to random chance.
  2. Self-attribution bias can lead to excessive risk-taking, especially if you happen to be on a hot streak. If you attribute investment success to innate abilities, and do not factor in external random factors you risk permanent loss of capital.
  1. Most importantly, stick to your investment plan!

Please contact us if you have any questions on this topic or find yourself making investment decisions that may have behavioral impacts on your investment decision making.