Noise trader sentiment and the behavior of futures prices
Sanders, Dwight Robert
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Permalink
https://hdl.handle.net/2142/20133
Description
Title
Noise trader sentiment and the behavior of futures prices
Author(s)
Sanders, Dwight Robert
Issue Date
1995
Doctoral Committee Chair(s)
Leuthold, Raymond M.
Department of Study
Agricultural and Consumer Economics
Discipline
Agricultural Economics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Economics, Agricultural
Economics, Finance
Language
eng
Abstract
In this research, the noise trader sentiment model of De Long, Schleifer, Summers, and Waldmann is modified and applied to futures markets. The theoretical model predicts that overly optimistic (pessimistic) noise traders result in futures prices that are greater (less) than fundamental value. More specifically, the model indicates that futures prices contain a systematic bias proportional to the average level of sentiment, and market returns are predictable with the level of noise trader sentiment.
Couched within Muth's rational forecasting framework, the model's predictions are tested on data from twenty-eight futures markets. Noise trader sentiment is proxied with commercial market sentiment indices. These data allow the formation of well-defined alternative hypotheses and, hence, more powerful statistical tests than found in previous research. The analysis reveals that the behavior of the commercial sentiment indices is consistent with noise trader sentiment as a theoretical construct. Fama-MacBeth cross-sectional regressions indicate that noise traders do not create a systematic bias in futures prices. Return predictability is evaluated with the Cumby-Modest market timing test and Granger causality tests. Sentiment is useful for predicting returns in only a few isolated cases. Overall, there is little evidence that noise trader sentiment impacts future prices.
This research documents the existence of noise trader sentiment in futures markets and provides a comprehensive test for its effect. The empirical evidence provides only weak support for theoretical noise trader models. Importantly, the research establishes a unique data set for examining noise trader effects, thereby laying a groundwork for future research.
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