Academic journal article Journal of Money, Credit & Banking

Do Noise Traders Influence Stock Prices?

Academic journal article Journal of Money, Credit & Banking

Do Noise Traders Influence Stock Prices?

Article excerpt

This paper tests a smart money-noise trader model directly by comparing its

prediction with the behavior of actual investors. It assumes that

individual probability of

being a noise trader is diminishing in income: high-income households are

smart

money, lower-income households are noise traders, with passive investors in

between. Market data behave as predicted: high participation by the

general population

is a negative predictor of one-year returns, and is associated with low

participation by

very high-income groups. The implications for the equity premium puzzle of

the low

returns earned by noise traders are discussed.

Few will disagree with Black's (1985) assessment that real financial markets differ from their textbook counterparts in comprising noise traders as well as perfectly informed, Bayesian, expected utility maximizers. Although the idea that a subset of agents trade on the basis of extraneous information with no bearing on fundamentals have been formalized in a variety of intuitively reasonable models, most of the empirical evidence offered in support of these models is indirect.(1) It is argued that various return anomalies are more simply explained by noise trading than by efficient market stories of time varying discount rates or the higher fundamental risk of some assets.(2)

By definition, smart money-noise trader models are concerned with the behavior of different groups of investors. A natural and direct way to test such models is therefore to see how well their behavioral predictions match what investors actually do.(3) Suppose that the market comprises smart money, noise traders, and passive investors. Two behavioral predictions of noise trader models will be tested here. First, market participation by smart money and noise traders should be negatively correlated, and neither should be related to market participation by passive investors. Secondly, high market participation by noise traders (who buy high and sell low) should be a negative predictor of future stock returns,(4) while participation by smart money should be a positive one, whereas the participation of passive investors should have no predictive power either way.

To test these predictions, the three groups must be identified. To do this, it is assumed that an individual's probability of being a noise trader is declining in income, and conversely for the probability of being smart money. This assumption may be justified by the greater incentives for wealthier investors to acquire reliable information about the market. It follows that low-income groups will be predominantly noise traders, very high-income groups will consist mostly of smart money, and passive investors will dominate intermediate-income groups.

Stock ownership is measured using dividend income data from income tax returns. Two measures of market participation by different income groups are used: the share of dividends going to each group, and the fraction of each group owning any stock. For both measures, the market participation of different income groups behaves almost exactly as the noise trader model predicts. High participation by the general population is a strong negative predictor of one-year stock returns, and is associated with low participation by very high-income households. On the other hand, market participation by intermediate income groups has no power to forecast returns. The paper concludes by looking at the very low returns experienced by households who enter the market during periods of high noise, and the possible implications of this for the equity premium puzzle.

1. SMART MONEY VERSUS NOISE TRADERS

The basic idea behind smart money-noise trader models of financial markets is that some subset of agents trades in response to extraneous variables that convey no information about future dividends or discount rates. …

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