Investor Memory: Godker, Jiao & Smeets (2025)

What this is. The paper’s core results, the experimental designs, and the estimating specifications with exact magnitudes: enough to know what was found and how, without reading all 46 pages. To replicate or extend, read the full source at the original or download the replication data from Harvard Dataverse.

TL;DR

Across three incentivized experiments (N = 229, 498, 487), the paper documents a positive memory bias for investment outcomes. After 1 week, subjects overremember the positive returns and underremember the negative returns of stocks they chose to invest in. This positive memory bias: (a) distorts beliefs about stock quality by 8.16 percentage points toward optimism relative to the objective Bayesian posterior, (b) doubles the rate of suboptimal reinvestment (41.9% vs 20.5%), and (c) raises overconfident betting on one’s own stock picks by 47%. The proposed mechanism is motivated memory suppression, consistent with the framework of Benabou and Tirole (2002): raising the financial incentive for accurate recall reduces measured memory bias among subjects high in self-deceptive enhancement, and the bias is not statistically significant in the passive endowment condition.

Core results

Magnitudes and significance are as reported; * p < .1, ** p < .05, *** p < .01. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Subjects overremember positive investment outcomes after 1 week	Table 2, p. 1614; Figure 1, p. 1613	Delay: +0.89 outcomes overremembered (p = .000); Immediate: +0.27 (p = .058); difference p = .018
R2	Subjects underremember negative investment outcomes after 1 week	Table 2, p. 1614; Figure 1, p. 1613	Delay: -0.73 outcomes underremembered (p = .000); Immediate: -0.28 (p = .037); difference p = .038
R3	Beliefs are 8.16 percentage points too optimistic in the Delay condition relative to the objective Bayesian posterior	Figure 2, p. 1616; Internet App. D	t-test p = .004
R4	Memory bias directly predicts belief distortion: each recalled positive outcome raises subjective belief by 6.20pp; each underremembered loss reduces it by 7.96pp	Table 3 cols 1-2, p. 1617	6.20*** (0.92) and -7.958*** (1.02); R2 = 0.52-0.55
R5	Suboptimal reinvestment doubles in the Delay vs Immediate condition (41.9% vs 20.5%)	Figure 3, p. 1619; Table 4 col. 2, p. 1620	Odds ratio 3.542*** (1.44) for Delay treatment dummy; significant at 1%
R6	Memory bias magnitude predicts suboptimal reinvestment: each overremembered positive outcome is associated with 1.599 times the odds of suboptimal reinvestment	Table 4 cols 3-4, p. 1620	Odds ratio 1.599*** (0.22) for positive-outcome memory bias; 0.617*** (0.10) for negative-outcome bias
R7	Overconfident betting is 47% higher in Delay vs Reminder condition; Delay subjects bet 46.3 vs 31.5 points suboptimally	Figure 4, p. 1621; Table 5 col. 2, p. 1622	14.788*** (4.18) additional suboptimal points in Delay; p < .01
R8	Evidence supports motivated memory suppression: high recall incentives reduce the memory bias for high-SDE subjects; only actively choosing investors exhibit the bias	Table 7, p. 1625; Table 6, p. 1623	HighStake coeff for high-SDE: -0.469** (0.19); NoChoice treatment effect 0.18 (p = .398, insignificant)
R9	Delay raises confidence across all investment choices, in addition to increasing suboptimal betting	Table 5, p. 1622	Delay coefficient +9.381*** (SE 2.85, N = 487, R2 = .02)
R10	High recall incentives attenuate the positive memory bias, consistent with suppression rather than deletion	Table 6, p. 1623	HighStakes Delay mean 0.11 (p = .591); Delay-Immediate treatment effect +0.32 (p = .151); Baseline treatment effect +0.48 (p = .010)
R11	The positive memory bias depends on active choice: it is not significant when subjects are assigned the stock	Table 6, p. 1623	NoChoice Delay mean 0.18 (p = .398); Delay-Immediate treatment effect +0.49 (p = .051); Baseline treatment effect +0.48 (p = .010)
R12	Belief-based estimation implies frequent underremembering of negative outcomes	Text p. 1625; Internet Appendix C	Delay probability of underremembering negative outcomes is almost 60% (p < .001), higher than in Immediate
R13	There is no significant evidence that the memory-elicitation task changes subjective beliefs	Text p. 1626; Internet Appendix G, Table 16	Delay vs NoRecall difference not significant (t-test p = .714)
R14	There is no significant evidence that the memory-elicitation task changes investment decisions	Text p. 1626; Internet Appendix G, Table 16	Delay vs NoRecall difference not significant (t-test p = .343); lower-expected-return subsample p = .383
R15	Randomized task order does not affect the main outcomes	Text p. 1626	t-test p = .314 for memory bias, p = .890 for beliefs, and p = .428 for suboptimal reinvesting
R16	Anticipated regret is not supported as an alternative mechanism	Text p. 1627; Internet Appendix I	Memory bias is not significantly correlated with regret proneness; the main text does not report a coefficient or p-value
R17	Delay increases reinvestment in the stock overall, alongside the increase in suboptimal reinvestment	Table 4, col. 1, p. 1620	Delay odds ratio 2.213** (SE 0.78, N = 152, pseudo-R2 = .05)
R18	Experiment 2 replicates the positive memory bias in its Baseline condition	Table 6, p. 1623	Delay mean +0.36 (p = .023); Delay-Immediate treatment effect +0.48 (p = .010; N = 185)

Overall (paper’s conclusion). Investors systematically overremember their gains and underremember their losses, and this positive memory bias operates as a microfoundation for three stylized facts in behavioral finance: gains weigh more than losses when investors learn from experience (Kaustia and Knupfer (2008)); investors are more likely to repurchase stocks previously sold at a gain (Strahilevitz, Odean, and Barber (2011)); and overconfident investors trade excessively (Barber and Odean (2001)). The paper also extends evidence from Kuhnen (2015) on asymmetric belief updating from financial information by adding a memory channel. The bias is motivated (driven by self-image concerns) rather than a passive cognitive limitation.

Theory / model

The paper has no formal structural model in the body. The hypothesis tested is:

H (Positive memory bias): When subjects have time to form memories (Delay condition, 1 week between observation and elicitation), they recall more positive investment outcomes and fewer negative outcomes than actually occurred, relative to a control group that elicits memory immediately (Immediate condition).

The identification logic is a between-subject random assignment to Delay vs Immediate conditions, with the 1-week gap as the treatment that activates memory processes. Both groups observe outcomes under the same experimental design and with the same level of engagement; the outcome draws need not be identical across subjects.

The Bayesian benchmark for subjective beliefs is the objective posterior (equation 1, p. 1604):

$$\mu_t^G(h_t) = \frac{1}{1 + \frac{1-\mu_0^G}{\mu_0^G} \times \left(\frac{\theta}{1-\theta}\right)^{t - 2n_t^+}} \tag{1}$$

where $\mu_0^G = 50\%$ is the prior probability that the stock is good, $\theta = 60\%$ is the probability that a good stock generates a positive outcome each period, $t$ is the total number of observed outcomes, $h_t$ is the history of outcomes, and $n_t^+$ is the number of positive outcomes observed. The objective Bayesian posterior $\mu_t^G(h_t)$ is the benchmark against which subjective beliefs are compared to measure belief distortion.

The motivated-memory mechanism is tested via two conditions in experiment 2. The HighStake condition increases the financial incentive for accurate memory reporting from 8 to 50 GBP/USD per correct memory answer (following Zimmermann (2020)), so that if the true information is still in memory (suppressed, not deleted), high incentives should induce subjects to report it more accurately. The NoChoice condition randomly endows subjects with a stock rather than letting them choose, so that ego-relevance of outcomes is reduced (following Mather, Shafir, and Johnson (2000, 2003)).

The Internet Appendix formalizes a model in which memory of investment outcomes is systematically biased in a motivated way: memory bias stems from quasi-Bayesian belief updating with a probability of underremembering specific previously observed signals, where this probability depends on whether the signals are consistent with the decision-maker’s motivation (Section 3, p. 1617 reference to the Internet Appendix model).

The parimutuel betting payoff in experiment 3 follows Enke, Graeber, and Oprea (2023), equation 2 (p. 1611):

$$\text{payoff}_i = \frac{b_i}{\frac{\sum_{j=1}^{10} x_j b_j}{\sum_{j=1}^{10} b_j}} + (100 - b_i) \tag{2}$$

where $b_i$ denotes points bet by subject $i$ and $x_i$ is an indicator equal to 1 if the subject’s investment choice was optimal. This design provides an objective measure of overconfidence: subjects should bet 0 if they believe their choice was suboptimal and bet all 100 if they believe it was optimal, so suboptimal betting (betting > 0 when the choice was suboptimal) directly reveals overconfidence.

Method

The paper uses a randomized between-subject design with exogenous variation in the time span between observing investment outcomes and eliciting memory (Delay vs Immediate conditions). Three experiments share a common framework but vary in outcomes, conditions, and elicited beliefs/choices.

Common structure. Subjects observe 12 sequential investment outcomes from a risky stock over 12 periods (each shown for 2 seconds on screen). Outcomes are drawn from known distributions (positive/negative with known probabilities depending on stock quality). After the observation phase, memory, beliefs, and investment choices are elicited. The Delay condition places the elicitation in week t+1 (1 week later); the Immediate condition places it in week t (immediately after).

Memory elicitation. Subjects report how many positive and negative outcomes they observed (experiment 1: how often each specific outcome value occurred; experiments 2-3: total counts summing to 12). The memory bias at the individual level is the difference between recalled and actually observed counts of positive (or negative) outcomes.

Identification via timing variation. The comparison of Delay vs Immediate conditions is designed to isolate memory effects from attention, salience, and information processing, which the design holds constant across groups. A NoRecall condition in experiment 1 (no memory task) finds no significant differences in subsequent beliefs or investment decisions, consistent with the memory elicitation task not affecting those outcomes.

The randomized-survey-experiment primitive underlies all three experiments: random assignment to conditions, incentivized elicitation of beliefs and choices, and exogenous variation in a single treatment variable (delay vs immediate, or HighStake vs Baseline, or NoChoice vs Baseline).

Empirical specifications

The paper reports treatment comparisons and regression tables rather than one common estimating equation. The equations below express those reported specifications. In the tables, standard errors are in parentheses where reported; the paper does not state a clustering adjustment.

Experiment 1 memory bias (R1-R2). Memory bias is recalled minus observed outcomes, tested against zero within each treatment and compared across conditions (Table 2, p. 1614):

$$\text{Bias}_{i,k}=\text{Recalled}_{i,k}-\text{Observed}_{i,k},\qquad H_0:E[\text{Bias}_{i,k}\mid c]=0,\qquad H_0:E[\text{Bias}_{i,k}\mid Delay]=E[\text{Bias}_{i,k}\mid Immediate]$$

Here $k$ indexes positive or negative outcomes and $c$ the treatment condition. The table reports t-tests, not a regression: stock investors only, N = 74 in Delay and N = 78 in Immediate. No fixed effects are used; inference is by the reported t-tests against zero and for the difference in means.

Beliefs and belief distortion (R3-R4). Table 3 reports separate OLS specifications for each memory-bias measure and outcome (p. 1617):

$$\text{SubjProb}_{i}=\alpha+\beta\,\text{MemBias}_{i,k} +\gamma\,\text{ObjProb}_{i}+\lambda_{s(i)}+\varepsilon_i$$ $$\text{BeliefDist}_{i}=\alpha+\beta\,\text{MemBias}_{i,k} +\lambda_{s(i)}+\varepsilon_i$$

$k$ is positive bias in column 1 / 3 and negative bias in column 2 / 4; $\lambda_{s(i)}$ are session fixed effects. Objective probability enters only the subjective-probability specifications. N = 188 for subjective probability and N = 182 for belief distortion. Standard errors are in parentheses; no cluster adjustment is stated.

Reinvestment (R5-R6, R17). Table 4 uses logit regressions, reporting odds ratios, for reinvestment and suboptimal reinvestment (p. 1620):

$$\Pr(\text{Invest}_{i}=1)=F(\alpha+\beta\,\text{Delay}_{i}+\lambda_{s(i)})$$ $$\Pr(\text{InvestSubopt}_{i}=1)=F(\alpha+\beta\,X_i+\lambda_{s(i)})$$

For the first specification, $X_i=\text{Delay}_i$; the suboptimal-investment specification uses Delay in column 2, positive memory bias in column 3, and negative memory bias in column 4. Column 1 estimates overall reinvestment on Delay. Session fixed effects are included in all columns. N = 152, restricted to subjects investing in the stock. Standard errors are in parentheses and odds ratios are reported; no cluster adjustment is stated.

Experiment 3 betting (R7, R9). Table 5 reports separate OLS regressions for all points bet, suboptimal points bet by treatment, and suboptimal points bet on positive memory bias (p. 1622):

$$\text{PointsBet}_{i}=\alpha+\beta\,\text{Delay}_{i}+\varepsilon_i$$ $$\text{PointsBetSubopt}_{i}=\alpha+\beta\,\text{Delay}_{i}+\varepsilon_i$$ $$\text{PointsBetSubopt}_{i}=\alpha+\beta\,\text{MemBiasPos}_{i}+\varepsilon_i$$

The comparison group is Reminder. No session fixed effects are included (one online session); N = 487 for all bets, N = 191 for suboptimal bets by treatment, and N = 83 for the memory-bias regression, restricted to Delay subjects with suboptimal choices. Standard errors are in parentheses; no cluster adjustment is stated.

Mechanism tests (R8, R10-R11, R18). Table 6 reports t-tests of mean positive memory bias against zero by condition and Delay-versus-Immediate mean comparisons (p. 1623):

$$\text{Bias}_{i}=\text{RecalledPos}_{i}-\text{ObservedPos}_{i},\qquad H_0:E[\text{Bias}\mid c,Delay]=0,\qquad H_0:E[\text{Bias}\mid c,Delay]=E[\text{Bias}\mid c,Immediate]$$

The conditions are Baseline, HighStakes, and NoChoice. Table 6 gives the total condition Ns (185, 154, and 159); it reports t-test p-values, with no regression or fixed effects. Table 7 then estimates the HighStakes treatment coefficient separately above and below the median SDE score (p. 1625):

$$\text{BiasPos}_{i}=\alpha+\beta\,\text{HighStakes}_{i}+\varepsilon_i$$

N = 172 for high-SDE and N = 167 for low-SDE subjects. No fixed effects are reported; standard errors are in parentheses, with no cluster adjustment stated.

Placebo and robustness tests (R12-R16). The text reports a model-based probability of underremembering negative outcomes near 60% in Delay (p < .001; text p. 1625, Internet Appendix C), with a larger probability than Immediate. The NoRecall placebo tests report no significant Delay-NoRecall difference in beliefs (p = .714) or investment decisions (p = .343; lower-expected-return subsample p = .383; text p. 1626, Internet Appendix G Table 16). Randomized task order has no significant effect on memory bias (p = .314), beliefs (p = .890), or suboptimal reinvesting (p = .428; text p. 1626). The regret robustness test finds no significant association, but its coefficient and p-value are not stated in the main text (text p. 1627, Internet Appendix I).

Datasets used

Dataset	Role in paper	Wiki page
Experiment 1 lab data (Hamburg University, N = 229)	Primary identification of memory bias, beliefs, and reinvestment (Results 1-6)	No page yet
Experiment 2 online lab data (CESS Oxford, Xlab UC Berkeley, N = 498)	Memory suppression and active-choice mechanism tests (Result 8, Table 6-7)	No page yet
Experiment 3 online data (Prolific, UK, N = 487)	Overconfidence measure via parimutuel betting (Results 6-8)	No page yet

All three experiments use primary hand-collected data: incentivized laboratory or online experiments with randomized treatment assignment. No external financial databases are used; the investment outcomes are computer-generated draws from known distributions. Experiment 1 programmed in z-Tree (Fischbacher 2007); experiments 2 and 3 in oTree (Chen, Schonger, and Wickens 2016). Experiment 3 preregistered at AsPredicted under ID 153791.

When to read the full paper

Read the original if you are: studying the microfoundations of investor overconfidence or asymmetric learning from experience; replicating the experimental paradigm (exact outcome distributions in Appendix D, instructions in Appendices A-C); extending the motivated-memory model to field settings or longer horizons; or connecting this to reinforcement learning in finance (Kaustia and Knupfer (2008)) and stock repurchase anomalies (Strahilevitz, Odean, and Barber (2011)). The exact Table/Figure locators above point to the key results. Replication code and data are publicly available at Harvard Dataverse (https://doi.org/10.7910/DVN/7K6ZPK).

Attribution and rights

Source: peer-reviewed, The Review of Financial Studies 38(6), 2025. Published by Oxford University Press on behalf of the Society for Financial Studies. All rights reserved. Commercial re-use requires reprints permission from OUP. This distillation was updated on 2026-10-04 and is not independently reproduced. Extract-only: the OUP standard publication reuse rights do not permit mirroring the verbatim PDF.

Gödker, Katrin, Peiran Jiao, and Paul Smeets. “Investor Memory.” The Review of Financial Studies 38, no. 6 (2025): 1595-1640. DOI: 10.1093/rfs/hhaf006. © 2025 The Author(s). Published by Oxford University Press on behalf of the Society for Financial Studies.