arxiv: 2605.05814 · v1 · submitted 2026-05-07 · 💱 q-fin.GN

Recognition: unknown

Does social media information affect individual investor disposition effect? Evidence from Xueqiu

Fei Ren, Siliu Chen

Pith reviewed 2026-05-08 03:12 UTC · model grok-4.3

classification 💱 q-fin.GN

keywords disposition effectsocial media informationindividual investorsnegative informationbehavioral biasXueqiu platformtrading behavior

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The pith

Social media information reduces the disposition effect of individual investors, primarily by delivering negative information that encourages more rational position adjustments.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates whether social media can help individual investors overcome the disposition effect, the tendency to sell winning investments too early and hold losing ones too long. Using posts and trading records from the Xueqiu platform, the analysis shows that greater exposure to social media content is linked to lower levels of this bias. The reduction is driven specifically by negative information, which leads investors to adjust holdings more rationally over time. Individual differences such as investment experience, number of followed users, region, and gender also shape how effectively this information mitigates the bias.

Core claim

Social media information can significantly reduce the disposition effect. Furthermore, it is through negative information that social media information reduces the disposition effect. When presented with negative information, individual investors will gradually become more rational in adjusting their positions. At the individual level, factors such as investment experience, users followed, region, and gender can all influence the effectiveness of the information acquired by individual investors in reducing the disposition effect.

What carries the argument

The differential impact of negative versus positive social media posts on trading behavior, measured by linking post exposure on Xueqiu to actual buy and sell records of individual investors.

If this is right

Investors who encounter more negative posts on the platform adjust losing positions more promptly.
The reduction in disposition effect strengthens with greater investment experience and number of followed accounts.
Regional and gender differences moderate how much social media information improves trading rationality.
Positive information alone shows little or no effect on reducing the disposition bias.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the pattern holds, platforms could test designs that surface negative information more prominently to support better decisions.
The same exposure mechanism might apply to other investor biases such as overconfidence or herding when similar data become available.
Longer-term tracking could reveal whether reduced disposition effect leads to higher net returns after transaction costs.

Load-bearing premise

That greater exposure to social media information directly causes lower disposition effect rather than simply reflecting other unmeasured traits or market conditions that make certain investors behave more rationally.

What would settle it

A controlled comparison showing no difference in how quickly investors sell losing positions after receiving negative social media information versus positive information, once other investor characteristics are held fixed.

Figures

Figures reproduced from arXiv: 2605.05814 by Fei Ren, Siliu Chen.

**Figure 1.** Figure 1: Time Series Plot of Xueqiu Real Portfolio Returns and CSI 300 Index Returns view at source ↗

read the original abstract

The irrational behavior of investors selling profitable assets too early while holding onto losing assets for too long is known as the disposition effect. Due to the development of the Internet, the information environment for individual investors has been greatly improved. As an important source of information for individual investors, whether social media can improve investors' behavioral biases and return to rational expectations is a question worth studying. Based on the post data and actual trading data of the social investment platform Xueqiu.com, this paper studies the impact of social media information on the disposition effect of individual investors. The research results show that social media information can significantly reduce the disposition effect. Furthermore, it is through negative information that social media information reduces the disposition effect. When presented with negative information, individual investors will gradually become more rational in adjusting their positions. At the individual level, factors such as investment experience, users followed, region, and gender can all influence the effectiveness of the information acquired by individual investors in reducing the disposition effect.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Xueqiu trading data links negative social media posts to reduced disposition effect, but endogeneity leaves the causal claim tentative.

read the letter

The paper's core result is that social media information on Xueqiu, especially negative posts, correlates with lower disposition effect in individual investors' trades, with some heterogeneity by experience, gender, region, and followed users. It uses linked post and trading records from the platform, which is a step beyond pure survey work on behavioral biases. That data connection is the main thing it does well and gives the empirical patterns some grounding in actual behavior rather than stated intentions. The negative-information channel is presented as the driver, which fits with existing ideas about how losses draw attention. The sample is China-specific and platform-specific, so the patterns add a data point for behavioral finance researchers tracking social trading effects. The soft spot is identification. The abstract and stress-test note both point to the same gap: no clear investor fixed effects, instruments, or exogenous variation to separate information exposure from investor traits that already shape trading discipline. Without those, the reduction could reflect selection or reverse causality instead of information causing rationality. The paper reports heterogeneity but does not appear to close that loop with robustness checks that would satisfy a referee on causality. This is the sort of paper that belongs in a behavioral finance or fintech outlet. Readers working on investor information environments or platform data would find the descriptive results useful as an extension, even if they treat the causal language as provisional. It is worth sending to peer review because the data linkage is real and the question is live; referees can push on the endogeneity fixes and external validity without the whole exercise being pointless.

Referee Report

2 major / 2 minor

Summary. The paper uses post and trading data from Xueqiu.com to examine whether social media information affects individual investors' disposition effect. It claims that exposure to such information significantly reduces the disposition effect, with negative posts as the key channel that promotes more rational position adjustments. Heterogeneity analyses indicate that the effect varies by investment experience, number of users followed, region, and gender.

Significance. If the causal identification holds, the study contributes to behavioral finance by linking real-world social media exposure to reduced trading biases, using a novel dataset that connects platform posts directly to investor trades. This approach strengthens ecological validity compared to survey-based studies and could inform platform design or investor education policies aimed at improving decision-making.

major comments (2)

[Abstract and Results] Abstract and empirical results section: The central causal claim that social media information (particularly negative) reduces the disposition effect lacks any described identification strategy. No investor fixed effects, instrumental variables, difference-in-differences exploiting exogenous information shocks, or other methods to address self-selection or reverse causality are mentioned, despite the abstract noting heterogeneity by experience and followed users. This is load-bearing, as unmeasured investor traits could drive both information consumption and lower disposition effect.
[Results] Channel analysis (negative information mechanism): The assertion that negative information is the operative channel requires explicit supporting tests, such as separate regressions for positive vs. negative posts, mediation analysis, or robustness to alternative information measures. Without these, the channel claim rests on correlational patterns that may not isolate causality.

minor comments (2)

[Abstract] The abstract should briefly note the sample period, number of investors/trades, and core econometric specification (e.g., panel regression with controls) to allow readers to assess the results without reading the full text.
[Data and Methodology] Clarify how disposition effect is measured at the individual level (e.g., proportion of gains realized vs. losses) and how social media exposure is quantified (e.g., number of posts read, sentiment scores).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments, which help clarify the identification and mechanism claims in our study. We address each major point below and outline revisions to strengthen the manuscript.

read point-by-point responses

Referee: [Abstract and Results] Abstract and empirical results section: The central causal claim that social media information (particularly negative) reduces the disposition effect lacks any described identification strategy. No investor fixed effects, instrumental variables, difference-in-differences exploiting exogenous information shocks, or other methods to address self-selection or reverse causality are mentioned, despite the abstract noting heterogeneity by experience and followed users. This is load-bearing, as unmeasured investor traits could drive both information consumption and lower disposition effect.

Authors: We agree that the identification strategy requires clearer exposition. The current specifications include investor characteristics, stock fixed effects, and time fixed effects to mitigate some confounding, but investor fixed effects were not explicitly highlighted. In the revision we will add investor fixed effects to the main regressions to absorb time-invariant unobserved heterogeneity. We will also include a dedicated identification section discussing remaining endogeneity concerns (self-selection into information consumption and reverse causality), robustness using lagged information exposure, and limitations. We note that the panel structure of the Xueqiu data permits within-investor variation, which supports the reported heterogeneity results, but we will not claim full causality without additional instruments or shocks. revision: yes
Referee: [Results] Channel analysis (negative information mechanism): The assertion that negative information is the operative channel requires explicit supporting tests, such as separate regressions for positive vs. negative posts, mediation analysis, or robustness to alternative information measures. Without these, the channel claim rests on correlational patterns that may not isolate causality.

Authors: We accept that the negative-information channel needs more rigorous tests. The original analysis shows differential effects by post sentiment, but we will expand this in revision by: (1) reporting separate regressions for positive and negative posts, (2) conducting a formal mediation analysis with sentiment as the mediator, and (3) adding robustness checks using alternative sentiment classifiers and information-volume measures. These additions will be placed in a new subsection on mechanisms. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical observational study

full rationale

The paper conducts a data-driven empirical analysis of trading records and social media posts from Xueqiu.com to estimate associations between information exposure and disposition-effect measures. No equations, first-principles derivations, fitted parameters renamed as predictions, or self-referential definitions appear in the abstract or described methodology. Results are statistical associations conditioned on observed covariates; they do not reduce to inputs by construction. Self-citations, if present, are not load-bearing for any central claim. The analysis is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical study with no explicit mathematical model; relies on standard econometric assumptions for identifying causal effects from observational data.

pith-pipeline@v0.9.0 · 5465 in / 935 out tokens · 47152 ms · 2026-05-08T03:12:59.823739+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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