Positive memory bias in investing is investors' tendency to recall past returns as more favorable than what they achieved. In "Investor memory of past performance is positively biased and predicts overconfidence" (2021), Walters and Fernbach documented this bias among real investors. They surveyed 411 U.S. investors who recalled a 44.1% return on their most consequential 2019 trade versus an actual 39.8%. Positivity bias predicted overconfidence with a Cohen's d of 0.71 in Study 1's regression analysis.
What the Study Found
Investors overestimated the return on their second-most consequential trade too, recalling 40.6% versus an actual 33.5%, t(410) = 3.43, P < 0.001. In Study 2, 144 participants recalled a 29.6% average return on their top 10 trades versus an actual 21.6%. Participants forgot 39.7% of losing trades compared to 29.9% of gains in Study 2's selective forgetting analysis. Distortion and selective forgetting each predicted overconfidence, with effect sizes of d = 0.67 and d = 0.39 respectively. In Study 3, overconfidence fell from 9.2% in the control condition to 5.8% in the treatment condition, t(364) = 2.91, P = 0.004.
Methodology
Study 1 surveyed 411 U.S. investors on Pollfish about their two most consequential trades from 2019. Study 2 recruited 151 Prolific Academic participants, 144 of whom completed trades bought after January 1 and sold before July 1, 2020. Study 3 recruited 366 online-forum investors, randomly assigning them to look up 2018 stock trades or report investment sectors. Regressions controlled for age, gender, income, education, total investment assets, number of stocks owned, and financial advisor status.
Key Statistics
| Metric | Finding | Context |
|---|---|---|
| Memory bias, Trade 1 | 44.1% recalled vs. 39.8% actual | Study 1, t(410) = 2.14, P = 0.033, n = 411, 2019 trades |
| Memory bias, Trade 2 | 40.6% recalled vs. 33.5% actual | Study 1, t(410) = 3.43, P < 0.001, n = 411, 2019 trades |
| Overall positivity bias | 29.6% recalled vs. 21.6% actual | Study 2, t(143) = 3.54, P < 0.001, n = 144, 2020 trades |
| Selective forgetting rate | 39.7% of losses forgotten vs. 29.9% of gains | Study 2, logistic regression, n = 144 |
| Distortion effect on overconfidence | d = 0.67 | Study 2, Table 4 column 1 |
| Selective forgetting effect on overconfidence | d = 0.39 | Study 2, Table 4 column 1 |
| Positivity bias effect on overconfidence | d = 0.71 | Study 1, Table 2 column 1, n = 411 |
| Overconfidence by condition | 9.2% control vs. 5.8% treatment | Study 3, t(364) = 2.91, P = 0.004, n = 366 |
| Trading frequency by condition | 16.4 trades control vs. 13.2 trades treatment | Study 3, t(364) = 2.26, P = 0.024, incentive-compatible measure |
Why This Matters
Financial advisors and platforms could reduce investor overconfidence by regularly displaying account statements rather than relying on client recall. Because traders who overestimate past success intend to trade more often, correcting memory bias may also lower unnecessary transaction costs. The gap between distortion (misremembering magnitude) and selective forgetting (omitting losses entirely) suggests debiasing tools need to address both memory failures separately. Self-reported performance reviews used in coaching or robo-advisory nudges are vulnerable to the same biases documented here.