# Investor Overconfidence and Trading Volume: How Past Returns Fuel Overtrading

Cite as: Gorak, R. (2026). Investor Overconfidence and Trading Volume: How Past Returns Fuel Overtrading. Tradicted. https://www.tradicted.com/research/statman-overconfidence-2003/
Paper: Meir Statman and Steven Thorley and Keith Vorkink — *Investor Overconfidence and Trading Volume*
Published in: Review of Financial Studies (2003)
Original: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=168472

Key finding: A one standard deviation market return shock produced an 8.6% increase in NYSE/AMEX market turnover the following month, accumulating to a 30% increase over six months (Statman, Thorley, and Vorkink, 2003).

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Investor overconfidence describes how investors overestimate the precision of their private information after experiencing gains, leading them to trade more frequently. An investor who profits during a rising market may misattribute those gains to skill and trade more in subsequent months. In "Investor Overconfidence and Trading Volume," Statman, Thorley, and Vorkink (2003) analyzed CRSP data on 1,878 NYSE/AMEX securities from August 1962 to December 2002. A one standard deviation market return shock produced an 8.6% increase in market turnover the following month.

## What the Study Found

Market turnover rose 8.6% in the month following a one standard deviation market return shock. It rose 7.3% in the second month, accumulating to a 30% increase over six months. The first-lagged market turnover coefficient was 0.284 (standard error 0.047). The first-lagged market return coefficient was 0.819 (standard error 0.133) in the same regression. At the individual security level, the first-lagged market return coefficient on turnover was 0.990, compared to 0.171 for the security's own first-lagged return. Contemporaneous market volatility carried a coefficient of 1.712 and return dispersion a coefficient of 5.024 in the market turnover regression. Both the overconfidence and disposition effects were more pronounced in small-capitalization stocks and in the earliest subperiod, 1963–1972, than in 1993–2002.

## Methodology

The study used CRSP monthly and daily data on NYSE/AMEX common stocks, excluding closed-end funds, REITs, and ADRs, from August 1962 to December 2002. The sample included 485 months of market-wide data, plus 1,878 individual securities with at least 120 months of contiguous data. That yielded 530,608 monthly observations. The authors estimated vector autoregressions with impulse response functions, using 10 lags of endogenous variables and 2 lags of exogenous variables. Lag lengths were selected by the Schwarz Information Criterion. Market volatility and return dispersion served as exogenous controls; standard errors for individual-security coefficients came from a 5,000-iteration bootstrap.

## Key Statistics

| Metric | Finding | Context |
|---|---|---|
| Market turnover response to 1 SD market return shock (month 1) | 8.6% increase | Full sample, Aug 1962–Dec 2002 |
| Accumulated market turnover response over 6 months | 30% increase | Full sample, Aug 1962–Dec 2002 |
| First-lagged market return coefficient (market VAR) | 0.819 (SE 0.133) | Full sample, Aug 1962–Dec 2002 |
| First-lagged market return coefficient on security turnover | 0.990 | 1,878 securities, full sample |
| First-lagged own-security return coefficient on security turnover | 0.171 | 1,878 securities, full sample |
| General VAR model | Y_t = μ + ΣA_k Y_(t-k) + ΣB_l X_(t-l) + e_t | Equation (1), Section 2.2 |

## Why This Matters

Aggregate trading volume is not solely determined by liquidity needs or information events, but also by shifts in investor confidence following market gains. The lead-lag pattern between returns and turnover implies that strong market performance may foreshadow elevated trading activity. Elevated trading carries transaction costs that can erode investor returns over time. Separating the market-wide overconfidence effect from the security-specific disposition effect gives advisors a framework for diagnosing why clients trade more after gains. Confidence is hard to calibrate without a record of what you predicted. A [trading journal](/trading-journal/) stores your read before the outcome so the two can be compared.

## FAQ

### What percentage increase in trading volume follows a strong market month?

An 8.6% increase in NYSE/AMEX market turnover followed a one standard deviation market return shock in the next month. Turnover rose 7.3% in the second month. Statman, Thorley, and Vorkink (2003) found the accumulated increase reached 30% over six months, evidence consistent with investor overconfidence following gains.

### How strong is the link between lagged market returns and individual stock turnover?

0.990 was the coefficient describing the relationship between lagged market returns and individual security turnover, compared to 0.171 for a stock's own lagged return. Statman, Thorley, and Vorkink (2003) estimated these figures using vector autoregressions on 1,878 NYSE/AMEX securities from August 1962 to December 2002.

### Does the overconfidence-turnover relationship vary by firm size?

1963–1972 and 1973–1982 were the subperiods where the relationship between lagged returns and turnover was strongest, more pronounced in small-capitalization than large-capitalization stocks. Statman, Thorley, and Vorkink (2003) attributed this pattern to a larger share of individual, rather than institutional, investors holding small-cap stocks in earlier decades.

### What is the disposition effect, and how does it differ from overconfidence?

0.171 was the coefficient linking a security's own lagged return to its turnover, which the authors attribute to the disposition effect rather than overconfidence. The disposition effect describes selling winning stocks too early; overconfidence reflects a market-wide overestimation of trading skill after gains.

## Source

Statman, M., Thorley, S., and Vorkink, K. (2003). Investor Overconfidence and Trading Volume. Review of Financial Studies, 19(4), 1531-1565.

[Read the full paper →](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=168472)