Do Noise Traders Move Markets?

Abstract:     
We study the trading behavior of individual investors using the Trade and Quotes (TAQ) and Institute for the Study of Security Markets (ISSM) transaction data over the period 1983 to 2001. We document four results: (1) Order imbalance based on buyer- and sellerinitiated small trades from the TAQ/ISSM data correlates well with the order imbalance based on trades of individual investors from brokerage firm data. This indicates trade size is a reasonable proxy for the trading of individual investors. (2) Order imbalance based on TAQ/ISSM data indicates strong herding by individual investors. Individual investors predominantly buy (sell) the same stocks as each other contemporaneously. Furthermore, they predominantly buy (sell) the same stocks one week (month) as they did the previous week (month). (3) When measured over one year, the imbalance between purchases and sales of each stock by individual investors forecasts cross-sectional stock returns the subsequent year. Stocks heavily bought by individuals one year underperform stocks heavily sold by 4.4 percentage points in the following year. For stocks for which it is most difficult to arbitrage mispricings, the spread in returns between stocks bought and stocks sold is 13.1 percentage points the following year. (4) Over shorter periods such as a week or a month, a different pattern emerges. Stocks heavily bought by individual investors one week earn strong returns in the subsequent week, while stocks heavily sold one week earn poor returns in the subsequent week. This pattern persists for a total of three to four weeks and then reverses for the subsequent several weeks. In addition to examining the ability of small trades to forecast returns, we also look at the predictive value of large trades. In striking contrast to our small trade results, we find that stocks heavily purchased with large trades one week earn poor returns in the subsequent week, while stocks heavily sold one week earn strong returns in the subsequent week.

Number of Pages in PDF File: 47

Keywords: Behavioral Finance, Asset Pricing, Market Efficiency

JEL Classification: G13, G15

working papers series