When fund managers outperform, they tend to attribute it to their skill. When they underperform, they tend to blame external factors. While that information has been known for some time, it wasn't something that researchers were able to quantify. But the advent of ChatGPT and large language models has changed that. In this episode, we are joined by Meng Wang, a PhD student at Georgia State University. He used this new technology to analyze and quantify self-attribution bias among fund managers and recently published a paper "Heads I Win, Tails It’s Chance: Mutual Fund Performance Self-Attribution?" where he highlighted his findings. We discuss his research process, what he learned and the most important conclusions for investors.

We hope you enjoy the discussion.

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Quantifying Self Attribution Bias with Meng Wang

Excess Returns