Research Articles Base: Famaâ€™s Model

Fama’s Model

Research into volatility itself has stimulated research into momentum and financial herding. Grinblatt et al. (1995), and Wermers (1999)[1] came to the conclusion that a large part of herding behavior occurs when investors “momentum-follow,” and Nofsinger and Sias (1999)[2] found evidence that implicates the use of momentum strategies by growth-oriented funds as an important source of herding. What is worthy of note is that momentum and herding have a notable impact on market price that is not related to economic or financial fundamentals. Market price seldom corresponds to intrinsic value and this disequilibrium can continue for extensive periods of time. Moreover, whereas economic and financial fundamentals will affect value, they are not the main movers of stock prices. In this regard, Fama (1981)[3] found that a substantial fraction of return variation couldn’t be explained by macroeconomic news. Roll (1984)[4] found that news about weather conditions, the principal source of variation in the price of orange juice, explains only about 10% of the movement in orange juice futures prices. Roll (1988)[5] further found that it is difficult to account for more than one-third of the monthly variation in individual stock returns on the basis of systematic economic influences. When investigating which factors moved share price, Cutler et al. (1989)[6] found that macroeconomic news explains only about one-fifth of the movement in stock prices, and they state: “The view that movement in stock prices reflect something other than news about fundamental values is consistent with evidence on the correlates of ex post returns (1989: 9). Haugen et al. (1991)[7] established that the main driver of stock returns was changes in volatility, and that fundamental economic and financial factors were not the main drivers of changes in volatility. In fact, they found that as few as one-quarter of the volatility shifts are associated with the release of significant (financial and economic) information. It is in the uncertainty of market behavior brought about the emergence of forecasting techniques such as the efficient market hypothesis (EMH). Nevertheless, debate continues over the validity of the efficient market hypothesis (EMH), which holds that security prices fully reflect all available information at any given time. Fama (1970)[8] has categorized market efficiency into three levels in which the definition of information varies into the weak form, which deals with the information contained in previous prices or price trends; the semi-strong form, which broadens the definition to include all publicly available information; and the strong form, which broadens the definition to include even privately held information. Exhaustive tests have been conducted to determine the level of efficiency of large financial markets. This paper shifts the focus of the debate to a smaller speculative market – sports gambling – where new, more definitive evidence is available that suggests that this more thinly traded market achieves a high degree of weak form efficiency. If the EMH is true, no systematic trading strategy will result in significantly abnormal returns. At the weak level this refers to strategies based on previous prices or price trends. Basically, the efficient market hypothesis (EMH) states simply that it is impossible to consistently outperform the market on a risk-adjusted basis after transaction costs and taxes. It forms the basic benchmark of analysis in financial economics and can be described, less formally. The EMH has been extensively tested since the late 1960s, so much so that Michael Jensen (1978 cited in Fama and French)[9] has said that it may be the most extensively tested proposition in all the social sciences. In short, the EMH suggests that stock markets are ‘rationally’ priced. Fama and French (1988)[10], among others, find that stock returns are somewhat predictable when analyzed over longer terms than the daily or weekly models often used to test the propositions of the EMH. A related problem is the sensitivity of results to the choice of variables included in the empirical model. To illustrate, Davidson and Froyen (1982)[11] report that their result supporting EMH based on the monthly New York Stock Exchange stock price index and M1 is overturned when their estimating equation includes the federal funds rate. Unfortunately, similar misspecification problems are found in many past studies. One pertinent issue examined in many previous studies is whether all available information is incorporated into current stock prices. In other words, do lags of economic variables have an important influence on current and future stock price movements? The proponents of EMH argue that stock prices respond only to unanticipated changes in macroeconomic variables (Davidson and Froyen, 1982; Pearce and Roley, 1983)[12]. Fama (1981)[13] finds a significant expected inflation-stock return relationship, however, when the previous year’s growth rate in the monetary base is included in the regression. Similarly, there are also controversies with regard to the effects of inflation on stock prices. Contrary to traditional belief, Fama and Schwert (1977) and Gultekin (1983)[14] find that unexpected inflation and stock prices are negatively related. Geske and Roll (1983)[15] claim that such a relationship is spurious (Coate and Vanderhoff, 1986)[16] and provide evidence in favor of an expected inflation and stock price relationship. It is in Fama’s model that the modern EMH, as illustrated in this portion of the chapter, has been among the primary indicators of market behavior. The study made by Fama basically  associates between daily price changes for stocks in the Dow Jones Industrial Average (DJIA) over the time period January 1958 through September 1962. He examines interdependency by conducting runs tests and tests for serial correlation using various lags on log daily price relatives. For the one-day lag, a positive correlation is found for 22 of the 30 securities in the sample. The correlation coefficients, however, are generally small. (The average correlation coefficient is only .0262.) Furthermore, although 11 of the securities have correlation coefficients that are significantly different from zero at the .05 levels, two of these 11 securities have negative correlation coefficients. Results from the runs tests find too few runs for 26 of the 30 securities in the sample, indicating a tendency for daily return signs to repeat. For eight of the 30 securities, the deviation from expectation is significant at the .05 levels. From these results Fama stresses that (5, pp. 73, 76, 80) the absolute size of the serial correlation coefficients is always quite small” and that “the percentage differences between the actual and expected number of runs are quite small. He concludes that there is no evidence of important dependence from either an investment or a statistical point of view. A number of other studies during this same time period reached similar conclusions when examining price dependency for daily security returns in other national markets or for different time intervals or different commodities in the United States market. Fama’s article remains the authoritative source on daily price dependency for security returns.   [1] Grinblatt, M., S. Titman and R. Wermers. (1995) Momentum Investment Strategies, Portfolio Performance, and Herding: A Study of Mutual Fund Behaviour. American Economic Review 85: 1088-1105; Wermers, R. (1999) Mutual Fund Herding and the Impact on Stock Prices. Journal of Finance 54: 581-622.   [2] Nofsinger, J. and R. Sias. (1999) Herding and Feedback by Institutional and Individual Investors. Journal of Finance 54: 2263-2295.   [3] Supra Fama (1981). [4] Roll, R. (1984) Orange Juice and the Weather. American Economic Review 74: 861-880.   [5] Roll, R. (1988). [R.sup.2]. Journal of Finance 43: 541-566.   [6] Cutler, D.M.,J.M. Poterba and L.H. Summers. (1989) What Moves Stock Prices? Journal of Portfolio Management 15: 4-12.   [7] Haugen, RA., E. Talmor and W.N. Torous. (1991) The Effect of Volatility Changes on the Level of Stock Prices and Subsequent Expected Returns. Journal of Finance 46: 985-1008.   [8] Fama , E. (1970). Efficient Capital Markets: A review of theory and Empirical Work”, Journal of Finance, vol-25, no-2(May,1970), Pp.383-417.   [9] Supra Fama and French. [10] Ibid. [11] Davidson, L. S. and Froyen, R. (March 1982) Monetary Policy and Stock Returns: Are Stock Markets Efficient? Review, Federal Reserve Bank of St. Louis. pp. 3-12.   [12] Ibid.; Pearce, D. and Roley. V.V.(1985) Stock Prices and Economic News. Journal of Business, 58, pp. 49-67.   [13] Supra Fama and French. [14] Fama, E.D. and Schwert, G.W. (Nov 1977) Asset Returns and Inflation. Journal of Financial Economics, 5 (November 1977), pp. 115-146; Gultekin, M. and Gultekin, B. (1983) Stock market seasonality: International evidence. Journal of Financial Economics 12, 469-481.   [15] Geske, R. and Roll, R. (March 1983) The Fiscal and Monetary Linkage Between Stock Returns and Inflation. Journal of Finance, pp. 1-33.   [16] Coate, D. and Vanderhoff, D. (Oct 1986) Stock Returns, Inflation, and Real Output. Economic Inquiry, 24. pp. 555-561. Credit:ivythesis.typepad.com

Fama’s Model

Research into volatility itself has stimulated research into momentum and financial herding. Grinblatt et al. (1995), and Werm...