A natural candidate for analysis was the behavior of stock market prices over time. Assuming that stock prices reflect the prospects of the firm, recurrent patterns of peaks and troughs in economic perfor- mance ought to show up in those prices. Maurice Kendall examined this proposition in 1953.1 He found to his great surprise that he could identify no predictable patterns in stock prices. Prices seemed to evolve randomly. They were as likely to go up as they were to go down on any particu- lar day, regardless of past performance. The data provided no way to predict price movements. At first blush, Kendalls results were disturbing to some financial economists. They seemed to imply that the stock market is dominated by erratic market psychology, or "animal spirits"-that it follows no logical rules. In short, the results ap- peared to confirm the irrationality of the market. On further reflection, however, economists came to reverse their inter- pretation of Kendalls study. It soon became apparent that random price movements indicated a well-functioning or efficient market, not an irrational one. In this chapter we explore the reasoning behind what may seem a surprising conclusion. We show how competition among analysts leads naturally to market efficiency, and we examine the implications of the efficient market hypothesis for investment policy. We also consider empirical evidence that sup- ports and contradicts the notion of market efficiency.

340 1 Maurice Kendall, "The Analysis of Economic Time Series, Part I: Prices," Journal of the Royal Statistical Society 96 (1953).

III. Equilibrium In Capital

Markets

12. Market Efficiency The McGraw−Hill

Companies, 2001

CHAPTER 12 Market Efficiency 341

12.1 RANDOM WALKS AND THE EFFICIENT MARKET HYPOTHESIS

Suppose Kendall had discovered that stock prices are predictable. What a gold mine this would have been for investors! If they could use Kendalls equations to predict stock prices, investors would reap unending profits simply by purchasing stocks that the computer model implied were about to increase in price and by selling those stocks about to fall in price.

Amoments reflection should be enough to convince yourself that this situation could not persist for long. For example, suppose that the model predicts with great confidence that XYZ stock price, currently at $100 per share, will rise dramatically in three days to $110. What would all investors with access to the models prediction do today?