---
title: "Investor Memory: Godker, Jiao & Smeets (2025)"
description: >-
  Distilled: Three lab and online experiments document a positive memory bias
  in investment outcomes: subjects overremember gains and underremember losses,
  which translates into overly optimistic beliefs, excess reinvestment, and
  overconfidence about stock-picking ability. Review of Financial Studies 2025,
  paywalled. Eight core results with source locators, datasets used, the
  experimental model, and the estimating specifications.
sidebar:
  label: Godker-Jiao-Smeets 2025
  order: 1
tags: [paper-summary, behavioral-finance, investor-beliefs, memory-bias,
       overconfidence, reinvestment, experimental-economics,
       peer-reviewed, unreplicated]
paper:
  authors: Katrin Gödker, Peiran Jiao, Paul Smeets
  authorList:
    - { family: Gödker, given: Katrin, affiliation: Bocconi University }
    - { family: Jiao, given: Peiran, orcid: "0000-0001-5207-0287", affiliation: Maastricht University }
    - { family: Smeets, given: Paul, orcid: "0000-0002-9414-1749", affiliation: University of Amsterdam }
  year: 2025
  venue: The Review of Financial Studies 38(6), 2025, 1595–1640
  venueShort: Rev. Fin. Stud. 2025
  doi: 10.1093/rfs/hhaf006
  jel:
    codes: [D01, G4]
    assignedBy: claude-sonnet-4-6
    date: 2026-06-06
  topics: ["Private Equity and Venture Capital", "State Capitalism and Financial Governance"]
  dataAccess: hand-collected
  introducesData: true
  outcome:
    - recalled number of positive and negative investment outcomes (memory bias)
    - subjective posterior belief about stock quality
    - probability of suboptimal reinvestment
    - points bet suboptimally (overconfidence measure)
  outcomeClass: [household-finance, expectations]
  license: >-
    Oxford standard publication reuse rights (confirmed via Crossref DOI metadata:
    content-version vor, URL https://academic.oup.com/pages/standard-publication-reuse-rights,
    delay-in-days 0, start 2025-03-03); paywalled, no open-access licence.
  licenseShort: paywalled
  access: paywalled
  machineAccess: "blocked-paywall (OUP site; Crossref licence confirms no OA; 2026-06-06)"
  redistribution: extract-only
  resultsCount: 8
  citedByCount: 15
  methods:
    role: applies-method
    family: descriptive
    buildsFrom: [randomized-survey-experiment, panel-regression, probit-regression, logit-regression]
    identification: randomized
  contributionType: [new-fact, new-data]
  mechanisms: [behavioral-bias, learning]
  scope:
    region: Germany, UK, US
    assetClass: experimental risky stocks (lab)
    period: 2019..2023
    frequency: mixed
    dataType: [experimental, survey]
    granularity: [individual]
    n: "229 subjects (Exp. 1), 498 subjects (Exp. 2), 487 subjects (Exp. 3)"
  findings:
    - ref: R1
      outcome: recalled number of positive investment outcomes (memory bias)
      metric: coefficient
      value: "Delay condition: mean bias +0.89 positive outcomes overremembered (p = .000); Immediate condition: mean bias +0.27 (p = .058); difference p = .018 (Table 2, p. 1614)"
      direction: positive
      vsBenchmark: Delay significantly larger than Immediate control
    - ref: R2
      outcome: recalled number of negative investment outcomes (memory bias)
      metric: coefficient
      value: "Delay condition: mean bias -0.73 negative outcomes underremembered (p = .000); Immediate: -0.28 (p = .037); difference p = .038 (Table 2, p. 1614)"
      direction: negative
      vsBenchmark: Delay significantly more negative than Immediate control
    - ref: R3
      outcome: subjective posterior belief about stock quality
      metric: pp-effect
      value: "Delay beliefs on average 8.16 percentage points too optimistic vs objective Bayesian posterior (t-test p = .004, Figure 2, p. 1616)"
      direction: positive
      vsBenchmark: 8.16 pp above objective Bayesian benchmark
    - ref: R4
      outcome: subjective posterior belief about stock quality
      metric: coefficient
      value: "Each overremembered positive outcome raises belief by 6.20pp (p < .01); each underremembered negative outcome lowers belief by 7.96pp (p < .01) (Table 3 cols 1-2, p. 1617)"
      direction: positive
      vsBenchmark: memory bias directly predicts belief distortion
    - ref: R5
      outcome: probability of suboptimal reinvestment
      metric: probability
      value: "41.9% of Delay subjects reinvest suboptimally; 20.5% in Immediate condition; difference significant at 1% (Figure 3, p. 1619; Table 4 col. 2)"
      direction: positive
      vsBenchmark: Delay suboptimal reinvestment rate roughly double Immediate control
    - ref: R6
      outcome: probability of suboptimal reinvestment
      metric: coefficient
      value: "Each overremembered positive outcome raises probability of suboptimal reinvestment by 59.9% (odds ratio, Table 4 col. 3, p. 1620); each underremembered loss decreases suboptimal reinvestment probability by 38.3% (Table 4 col. 4)"
      direction: positive
      vsBenchmark: memory bias magnitude predicts suboptimal reinvestment
    - ref: R7
      outcome: points bet suboptimally (overconfidence measure)
      metric: coefficient
      value: "Delay subjects bet on average 46.3 points suboptimally; Reminder condition 31.5 points; difference +14.8 points (p < .01) (Table 5 col. 2, p. 1622)"
      direction: positive
      vsBenchmark: "47% more suboptimal betting in Delay vs Reminder"
    - ref: R8
      outcome: points bet suboptimally (overconfidence measure)
      metric: coefficient
      value: "Each overremembered positive outcome increases suboptimal betting by 2.8 points (p < .05, Table 5 col. 3); HighStakes treatment eliminates memory bias for high-SDE subjects (Table 7 col. 1: coeff -0.469, p < .05) (pp. 1622-1625)"
      direction: positive
      vsBenchmark: memory suppression explains mechanism (motivated memory)
  resultType: new-finding
  replicationCode:
    url: "https://doi.org/10.7910/DVN/7K6ZPK"
    status: available
  relatesTo:
    - { cite: "Benabou and Tirole (2002)", doi: '10.1162/003355302760193913', relation: builds-on, note: "theoretical framework for motivated memory suppression: agents trade off utility from accurate memory vs utility from maintaining positive self-image" }
    - { cite: "Zimmermann (2020)", doi: '10.1257/aer.20180728', relation: tests, note: "applies same high-incentive manipulation to test memory suppression vs genuine forgetting in financial context" }
    - { cite: "Kaustia and Knupfer (2008)", doi: '10.1111/j.1540-6261.2008.01411.x', relation: extends, note: "explains their IPO reinvestment asymmetry (gains vs losses) via investor selective recall" }
    - { cite: "Strahilevitz, Odean, and Barber (2011)", doi: '10.1509/jmkr.48.spl.s102', relation: extends, note: "explains repurchase bias (investors more likely to repurchase stocks sold for a gain) via overremembering gains and underremembering losses" }
    - { cite: "Barber and Odean (2001)", doi: '10.1162/003355301556400', relation: cites, note: "overconfidence and trading; memory bias provides microfoundation for investor overconfidence" }
    - { cite: "Kuhnen (2015)", doi: '10.1111/jofi.12223', relation: tests, note: "asymmetric belief updating from financial information; paper adds memory channel on top of immediate belief formation" }
  openQuestions:
    - "Whether the positive memory bias extends to extreme outcome events (very large gains or losses), where psychologists document enhanced emotional memory (p. 1628)."
    - "How the memory bias and its behavioral consequences evolve over longer time horizons beyond 1 week; experiments studied memory 1 week after observation (p. 1629)."
    - "How the memory bias operates in field settings for uninvolved investors (not personally invested), and how social transmission of memory interacts with market prices (pp. 1628-1629)."
    - "Whether debiasing interventions (reminders of actual outcomes) are effective in field settings and whether they should target description vs experience learning (p. 1601)."
  proposedVocab:
    - { axis: topic, term: behavioral-finance, def: "Research on how psychological biases, heuristics, and deviations from rationality affect investor behavior and asset prices.", aliases: [investor-psychology, behavioral-economics-finance] }
    - { axis: topic, term: investor-beliefs, def: "Formation, updating, and distortion of individual investor beliefs about asset quality or return distributions.", aliases: [belief-formation, subjective-beliefs] }
    - { axis: topic, term: memory-bias, def: "Systematic distortion in how agents recall past outcomes; in finance, overremembering gains and underremembering losses.", aliases: [selective-recall, positive-memory-bias] }
    - { axis: topic, term: overconfidence, def: "Investors' tendency to overestimate their own investment ability or the precision of their information relative to an objective benchmark.", aliases: [investor-overconfidence] }
    - { axis: topic, term: reinvestment, def: "Decision to reinvest in a previously chosen stock after observing its outcomes; studied as a revealed measure of reinforcement learning.", aliases: [stock-repurchase, reinvestment-decision] }
    - { axis: topic, term: experimental-economics, def: "Incentivized laboratory or online experiments eliciting preferences, beliefs, or choices from human subjects under controlled conditions.", aliases: [lab-experiment, online-experiment] }
  extraction:
    - by: paper-distiller (claude-sonnet-4-6)
      date: 2026-06-06
      role: extracted
      note: >-
        Full text read (pp. 1595-1640 plus appendices A-E). Eight results
        extracted from Tables 2-7 and Figures 1-4. Not human-verified.
        Not reproduced. Replication code and data available at Harvard Dataverse
        (https://doi.org/10.7910/DVN/7K6ZPK) but not run here.
    - by: paper-verifier (claude-sonnet-4-6)
      date: 2026-06-06
      role: verified
      note: >-
        Locators and reported magnitudes re-checked against the source PDF; all
        eight Core-results rows confirmed against Tables 2-7 and Figures 1-4.
        Equations (1) and (2) verified term-by-term against PDF pp. 1604 and
        1611. Fixed JEL codes: distiller had [D01, G41, G11] but PDF lists
        only D01, G4; corrected to [D01, G4].
  licenceVerification:
    - source: "Crossref REST API works/10.1093/rfs/hhaf006"
      checked: 2026-06-06
      by: paper-distiller (claude-sonnet-4-6)
      found: "license[].content-version=vor, URL=https://academic.oup.com/pages/standard-publication-reuse-rights, delay-in-days=0, start=2025-03-03; no Creative Commons licence; paywalled."
  rightsSignalConflict: false
---

**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](https://doi.org/10.1093/rfs/hhaf006) or download the replication
data from [Harvard Dataverse](https://doi.org/10.7910/DVN/7K6ZPK).

## 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 mechanism is motivated memory suppression (not genuine
forgetting), consistent with the framework of Benabou and Tirole (2002): raising
the financial incentive for accurate recall eliminates the bias for subjects prone
to self-deception, and the bias disappears when subjects did not actively choose
their investment (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; effect is absent in Immediate condition |
| 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 raises probability of suboptimal reinvestment by 59.9% | 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 | **Motivated memory suppression drives the bias**: high recall incentives eliminate 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) |

**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. The Immediate condition holds constant all non-memory
factors (information acquisition, salience, attention) that could influence
recall.

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):

$$
\text{payoff}_i = \frac{b_i}{\frac{\sum_{l=1}^{10} x_l b_l}{\sum_{l=1}^{10} b_l}} + (100 - b_i) \tag{2}
$$

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 isolates the effect of memory from attention, salience, and
information processing, which are held constant because both groups observe the
same outcomes with the same level of engagement. A NoRecall condition in
experiment 1 (no memory task) confirms that the memory elicitation task itself
does not affect subsequent beliefs or investment decisions.

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

**Memory bias (R1, R2).** The memory bias is regressed on a constant and tested
with a t-test against zero for each condition (Table 2, p. 1614):

$$
\text{MemoryBias}_{i,s} = \alpha + \beta \cdot \mathbf{1}[\text{Delay}_i] + \gamma \cdot \text{Session}_{is} + \varepsilon_{is}
$$

where $$\text{MemoryBias}_{is}$$ is the subject's recalled minus actually observed
count of positive (or negative) outcomes, and $$\text{Session}_{is}$$ is a session
fixed effect. Column 1 of Table 2 reports the t-test of the Delay group mean
against zero; column 3 tests the Delay-Immediate difference.

**Memory-based beliefs (R3, R4).** OLS regressions with session fixed effects (Table 3, p. 1617):

$$
\text{SubjProb}_{is} = \alpha + \beta_1 \cdot \text{MemBias}_{\text{pos},is} + \beta_2 \cdot \text{MemBias}_{\text{neg},is} + \gamma \cdot \text{ObjProb}_{is} + \delta \cdot \text{Session}_{is} + \varepsilon_{is}
$$

Dependent variable: subjective probability that the stock is good (1-100).
Columns 3 and 4 use belief distortion (difference between posterior log-likelihood
ratios of subjective and objective probabilities) as the dependent variable.
N = 188, R2 = 0.31-0.55. Standard errors in parentheses.

**Reinvestment behavior (R5, R6).** Logit regressions with session fixed effects (Table 4, p. 1620):

$$
\Pr(\text{Invest}_{is}) = F\left(\alpha + \beta \cdot \text{Delay}_i + \gamma_1 \cdot \text{MemBias}_{\text{pos},is} + \gamma_2 \cdot \text{MemBias}_{\text{neg},is} + \delta \cdot \text{Session}_{is}\right)
$$

Dependent variable: a dummy equal to 1 if the subject reinvested in the stock
(Inv.) or a dummy equal to 1 if the subject reinvested suboptimally from a
Bayesian perspective (Inv. (Subopt.)). Odds ratios reported. N = 152, pseudo-R2 = 0.05-0.13.
Sample restricted to subjects who invested in the stock.

**Overconfidence / suboptimal betting (R7, R8).** OLS regressions (Table 5, p. 1622):

$$
\text{PointsBet}_{is} = \alpha + \beta \cdot \text{Delay}_i + \gamma_1 \cdot \text{MemBias}_{\text{pos},is} + \varepsilon_{is}
$$

Dependent variable: number of points bet suboptimally (Points bet (subopt.)
= points bet when the stock chosen had fewer than 6 positive outcomes, i.e.,
was suboptimally chosen from a Bayesian perspective). N = 191 (col. 2),
N = 83 (col. 3 for Delay subjects only). R2 = 0.02-0.06. No session fixed
effects in experiment 3 (collected in one online session).

**Mechanism tests (R8).** OLS regressions split by median self-deceptive
enhancement (SDE) score (Table 7, p. 1625); Treatment (HighStake) is the
dummy for the high-incentive condition. The memory-suppression prediction is
that HighStake reduces bias for high-SDE subjects (column 1: coeff -0.469,
p < .05) but not for low-SDE subjects (column 2: 0.115, p = .60).

## 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](https://doi.org/10.1093/rfs/hhaf006) 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](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 extracted by an LLM on 2026-06-06 and is
**not human-verified or 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.