Abstract

This paper tests whether retail investor trading activity increases stock market volatility, using the De Long, Shleifer, Summers, and Waldmann (1990) noise trader framework as its theoretical basis. Since retail sentiment is unobservable, the study proxies for it with daily volume shocks: the log deviation of trading volume from its twenty-day moving average, which capture the observable footprint of correlated retail demand. The sample covers seven U.S. equities: four meme stocks (GME, AMC, BB, KOSS), two market benchmarks (SPY, RUT), and Apple (AAPL) from January 2018 through December 2025, sourced from Yahoo Finance.

Panel regressions estimate the association between volume shocks and five-day realized volatility and range-based volatility (Alizadeh, Brandt, and Diebold, 2002, pp. 1050–1055), controlling for lagged volatility, lagged returns, baseline volume levels, and stock fixed effects, with standard errors clustered at the stock level. The results support the noise trader prediction: a one-standard-deviation volume shock is associated with an 8% increase in realized volatility and a 122% increase in range-based volatility. The lagged volume shock, ensuring temporal precedence of the trading surge predicts a 14% increase in next-day absolute returns, and the relationship holds in the within-stock fixed effects specification, ruling out a purely cross-sectional explanation. Magnitudes fall below the causal estimates of Foucault, Sraer, and Thesmar (2011, pp. 1394–1396), consistent with attenuation from proxy imprecision and a non-crisis sample. The study contributes a transparent, publicly replicable test of the noise trader mechanism using accessible market data

Advisor

Amyaz, Moledina

Department

Economics

Disciplines

Econometrics | Economic Theory | Finance | International Economics

Keywords

Retail Trading, Volatility, Noise Traders, Market Microstructure, Investor Sentiment, Abnormal Volume, Financial Markets

Publication Date

2026

Degree Granted

Bachelor of Arts

Document Type

Senior Independent Study Thesis

Download

Share

COinS
 

© Copyright 2026 Kovid B. Pathak