Abstract
Examining the daily Dow Jones Industrial Average (DJI) we find evidence both of higher-order anomalies and predictability. While most researchers are only aware of the relatively harmless anomalies that occur just in the mean, the first part of this article provides empirical evidence of more dangerous kinds of anomalies occurring in higher-order moments. This evidence casts some doubt on the common practice of fitting standard time series models (e.g., ARMA models, GARCH models, or stochastic volatility models) to financial time series and carrying out tests based upon autocorre lation coefficients without making proper provision for these anomalies. The second part of this article provides evidence in favor of the predictability of the returns on the DJI and, more interestingly, against the efficient market hypothesis. The special value of this evidence is due to the simplicity of the involved methods.
Highlights
It is well known that stock prices do not follow a pure random walk
It is well known that series of daily stock returns exhibit significant weekly and seasonal patterns
These findings suggest that the common approach of calculating autocorrelation coefficients, carrying out tests based upon autocorrelation coefficients, and estimating ARCH/GARCH models (Engle, 1982, Bollerslev, 1986) or stochastic volatility models (Taylor, 1986) is untenable in the analysis of daily financial data
Summary
It is well known that stock prices do not follow a pure random walk. Price changes are neither independent nor identically distributed. The majority of researchers dealing with this problem do not first and foremost try to construct profitable trading strategies but rather focus on a related question, namely whether or not stock returns are predictable (early exceptions to this rule are Alexander, 1961, Fama and Blume, 1966, Levy, 1967, and Stevenson and Bear, 1970, Leuthold, 1972; more recent ones are Taylor, 1986, Jegadeesh and Titman, 1993, Pesaran and Timmerman, 1995, Gencay, 1998, Qi, 1999, and Sullivan et al, 1999). Gencay (1998) and Qi (1999) presented evidence in favor of nonlinear predictability of stock returns Both authors used neural network models and compared the performance of their models with conventional linear models. The trading decisions are solely based on the past history of the risky asset itself
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