TAILIEUCHUNG - Entropy and Predictability of Stock Market Returns¤

The relationship between macroeconomic variables and stock market returns is, by now, well-documented in the literature. However, a void in the literature relates to examining the cointegration between macroeconomic variables and stock market’s sector indices rather than the composite index. Thus in this paper we examine the long-term equilibrium relationships between selected macroeconomic variables and the Singapore stock market index (STI), as well as with various Singapore Exchange Sector indices—the finance index, the property index, and the hotel index. The study concludes that the Singapore’s stock market and the property index form cointegrating relationship with changes in the short. | Entropy and Predictability of Stock Market Returnsn Esfandiar Maasoumi Department of Economics Southern Methodist University Dallas TX 75275-0496 USA maasoumi @mail .smu .edu Je Racine Department of Economics University of South Florida Tampa FL 33620 USA j racine@. edu First version July 2000 This version December 2000 Abstract We examine the predictability of stock market returns by employing a new metric entropy measure of dependence with several desirable properties. We compare our results with a number of traditional measures. The metric entropy is capable of detecting nonlinear dependence within the returns series and is also capable of detecting nonlin-ear a nity between the returns and their predictions obtained from various models thereby serving as a measure of out-of-sample goodness-of- t or model adequacy. Several models are investigated including the linear and neural-network models as well as nonparametric and recursive unconditional mean models. We nd signi cant evi- dence of small nonlinear unconditional serial dependence within the returns series but fragile evidence of superior conditional predictability pro t opportunity when using market-switching versus buy-and-hold strategies. Keywords Entropy stock returns nonparametric neural-networks prediction dependence n onlinear. JEL Classi cation C14 - Semiparametric and Nonparametric Methods. nWe would like to thank but not implicate Min Qi for her many useful comments and for providing both her data and the recursive residuals from the neural-network model used in Qi 1999 . Special thanks to Amos Golan and two referees for extensive comments and suggestions. 1 Introduction Much of the theoretical literature in nance is based on market e ciency arguments which imply unpredictability of returns or no pro t opportunity . The empirical evidence however is mixed. In the existing literature the most common paradigm is a linear-in-mean model with searches for variables that may provide signi cant .

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