Stock Selection and Timing—A New Look At Market Efficiency

Abstract

In this paper it is shown that stock returns do not conform to a random walk model, nor to the more general martingale model, but nevertheless the stock market is weak-form efficient. This result is not surprising when we recall that risk-averse investors are typically concerned with more than the first moment of a security return’s distribution, and market efficiency is appropriately determined in terms of (expected) utility - not profits alone. Extant tests of market efficiency have ignored this point. By examining the first two moments of return distributions, and by not assuming any form of equilibrium pricing model, we provide tests of weakform market efficiency which are more powerful than prior studies. A market is considered informationally efficient if market prices ‘fully reflect’ information. The (sub)set of information presumed to be reflected determines the particular form of market efficiency. The market is said to be efficient in the weak-forms if market prices fully reflect the past realizations of market prices. Extant tests of the weak-form of market efficiency (e.g., Fama, 1970; Fama and Blume, 1966; and Mandelbrot, 1966) examine whether information on past price movements can be exploited to enhance profit. These tests assume that investors looking to profit from ‘trend’ data are risk-neutral. However, the normative theory of portfolio selection due to Markowitz (1952), and positive theories of capital asset pricing (e.g., Sharpe, 1964) are developed assuming that investors are risk-averse, This paper fills a gap in the literature by testing the informational efficiency of the stock market by exploring whether or not gains in CXpGGtcd utility are attainable by utilizing the time series of past stock prices. In particular, since Sharpe (1963), a simple algorithm has been available for risk-averse investors to use in selecting optimal portfolios. Normative Portfolio Theory of Markowitz (1952) (N.2“) suggests the use of historical data to obtain estimates of expected return and risk, and then apply a mathematical programming algorithm to build Mean-Variance (E- V) efficient portfolios. Surprisingly, studies that have tackled the timing question have done so in a vacuum vis-B-vis optimal (E- V) portfolio selection.