Beware of chimps bearing darts

Is KASE as chaotic as it seems?


Kazakhstan’s stock market is as changeable as Almaty’s weather. In May, a sudden drop of two-thirds in the price of Kazakhtelecom stock precipitated a swoon in the KASE index of 12%, reported Bloomberg News. The problem may be general. In the first half of this year, the volume of KASE trading had fallen nearly 13% since early 2011, reported the business weekly Kursiv’.

One can interpret such mercurial price movements in one of two ways. First: The stock market here is not efficient. Trades are few and far between, so the price adjustments that would occur daily, and on a small scale, in a more active market tend to pile up in ours. Second: The market here is efficient, in the sense that it responds right away to new information. The price changes are large not because the market tarries in digesting news but because we know so little about Kazakhstan’s emerging economy that recent events can change radically our perception of it.

KASE is not the only stock market to defy characterization. Three or four decades ago, financial economists thought that markets for stocks and bonds absorbed information quickly (the “efficient markets hypothesis”). If, in 2009, you heard that General Motors was about to go bankrupt – and who didn’t – then you need not have bothered to sell its stock short; other investors had already acted on the information, selling the stock and forcing down its price right away. Only unanticipated events – “news” – can affect current stock prices. Since news, by definition, occurs at random, so do changes in current stock prices. These changes will be independent of previous prices.

In that case, technical analysis, which seeks visual patterns in past stock prices, can’t predict the price. One financial economist, Burton Malkiel, called technical analysis “alchemy.” In a 2000 study, Andrew Lo, Harry Mamaysky and Jiang Wang conceded that “the presence of geometric shapes in historical price charts is often in the eyes of the beholder.” To detect patterns more objectively, they permitted random price movements in either direction to cancel out. Working with a 31-year sample, they concluded that “several technical indicators do provide incremental information and may have some practical value,” especially for a U.S. stock exchange favored by computer firms, Nasdaq.


The chimp paradigm


In lieu of technical analysis, one could comb financial information in search of stocks that are under- or over-valued, given the firm’s apparent strength. But such “fundamental analysis” won’t work if stock prices already reflect enduring information about the firm. You might as well buy shares in a good index fund as to speculate. Malkiel writes: “A blindfolded chimpanzee throwing darts at the Wall Street Journal could select a portfolio that would do as well as the experts.”

Malkiel concludes that stock investors cannot earn unusually high returns without accepting unusually high risks. One thinks of the student who espies a $100 bill on the ground and is about to pick it up. “Don’t bother,” says his economics professor. “If it were really a $100 bill, it wouldn’t be there.” Malkiel argues that there are no $100 bills on the trading floor. Market bubbles may sometimes occur; but in the long run, “true value will win out.”

Recently other economists have questioned the efficient markets hypothesis. Robert Shiller, who popularized the phrase “irrational exuberance,” may be the best-known of these critics. They maintain that patterns in past stock prices can enable investors to earn unusually high rates of return, even when adjusted for risk.

Early work on the efficient markets hypothesis looked for correlation across time in stock prices. Such a connection can enable us to predict tomorrow’s price. Many studies of the 1960s and 1970s failed to find correlation.

But some new studies do. They argue that stock prices move in the same direction often enough not to qualify as “random walks,” in which a price is as likely to rise as to fall. In Irrational exuberance, Shiller offered an explanation: “Investors under-react to new information.” If the full impact of news unfolds only over time, then we may observe positive correlation in stock prices: The higher prices of yesterday link to the higher ones of today.


Fiascos, fads and finance


Malkiel doubts these arguments. The correlations observed in stock prices are too small to yield an unusually good return to the investor. Lo and coauthors say that technical analysis provides “incremental information.” As for the behavioral economists, while fads may pop up from time to time, little evidence suggests that they occur systematically. Malkiel cites a 1998 survey of studies, in which Eugene Fama concluded that investors under-reacted to news about as often as they over-reacted. Also, the returns observed depended on how the model was specified: A study that equally weighted the returns of various stocks after some announcement led to different results than did a study that weighted the returns by value.

In his 2003 article, Malkiel asserts that stock-price correlations appearing in the late 1990s had disappeared by 2000. Some patterns may disappear shortly after publications disclose them because investors take advantage of the information. For example, the “January effect,” in which stock prices rose in early January, no longer seemed to exist.

In the long run, high stock returns fall, and low returns rise. Why? In a widely-cited 1985 study, Werner DeBondt and Richard Thaler looked at whether stock investors over-reacted to “unexpected and dramatic news events,” as people seemed to do in experiments by psychologists. The two authors concluded that investors may over-react to recent events. Stock prices rise or fall, then return to the average. “Portfolios of prior ‘losers’ are found to outperform prior ‘winners’.” Maybe you should buy dogs and avoid beach beauties.

In a study of stocks from 1957 to 1971, Sanjoy Basu found that those with low price-earnings ratios generally earned higher returns (adjusted for risk) than did stocks with high P/E ratios. “Contrary to the growing belief that publicly available information is instantaneously impounded in security prices,” Basu wrote in 1977, “there seem to be lags and frictions in the adjustment process.” In 1983, Basu added: “While neither E/P nor [firm] size can be considered to cause expected returns, the evidence lends credence to the view that, most likely, both variables are just proxies for more fundamental determinants of expected returns for common stocks.”

In principle, the price that one would pay for a stock depends on the size of its expected dividends. (Yes, you may sell your stock before collecting dividends, but the price that you receive will depend on the dividends that the new owner expects.  Even if you do not sell the stock, an increase in its price reflects the market's expectation of higher dividends.) Via market competition, the P/E ratio should tend to be the same for all stocks bearing the same risk. A stock with a low P/E ratio is presumably under-valued, so it should attract buyers. They will push up its price. The initial under-valuation may be an over-reaction to temporarily bad news. And likewise for over-valuation: Behavioral economists think that the stock market is subject to fads. For example, initial public offerings of dot.coms skyrocketed in the U.S. stock markets of the technophilic late Nineties.

Malkiel believed that the strongest evidence for a negative correlation in stock prices over time came from the Great Depression, which might not be a typical period. The negative correlations may result from fluctuations in the interest rate, which also reverts to the mean. When the interest rate rises, you would expect stock prices to fall, since investors may sell their stocks in favor of bonds that deliver a relatively higher return than before. When the interest rate falls back to the mean, demand will shift back to stocks and push up their price. – Leon Taylor, tayloralmaty@yahoo.com




Good reading

Burton G. Malkiel. A random walk down Wall Street: The time-tested strategy for successful investing. W. W. Norton. 2003.

Burton G. Malkiel. The efficient market hypothesis and its critics. Journal of Economic Perspectives 17. Winter 2003.

Robert J. Shiller. From Efficient Markets Theory to behavioral finance. Journal of Economic Perspectives 17. Winter 2003.

Robert J. Shiller. Irrational exuberance. Crown Business. 2006.



References


Sanjoy Basu. Investment performance of common stocks in relation to their price-earnings ratios: A test of the Efficient Market Hypothesis. Journal of Finance 32. June 1977.

Sanjoy Basu. The relationship between earnings’ yield, market value and return for NYSE market stocks: Further evidence. Journal of Financial Economics 12. June 1983.

Werner F. M. de Bondt and Richard Thaler. Does the stock market overreact?  Journal of Finance 40.  July 1985.

Eugene Fama. Market efficiency, long-term returns, and behavioral finance.  Journal of Financial Economics 49. September 1998.

Nariman Gizitdinov and Ksenia Galouchko. Kazakhtelecom retreats most ever as dividend date passes. Bloomberg News. May 16, 2012. www.businessweek.com

Andrew W. Lo, Harry Mamaysky and Jiang Wang, Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. Journal of Finance 55. August 2000.

Yurii Valykov. “Punkt naznacheniya” KASE. Kursiv’. July 19, 2012.