Forecasting Performance of Nonlinear Models for Intraday Stock Returns

J.M. Matías · Juan C. Reboredo

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

Abstract

ABSTRACT We studied the predictability of intraday stock market returns using both linear and nonlinear time series models. For the S&P 500 index we compared simple autoregressive and random walk linear models with a range of nonlinear models, including smooth transition, Markov switching, artificial neural network, nonparametric kernel regression and support vector machine models for horizons of 5, 10, 20, 30 and 60 minutes. The empirical results indicate that nonlinear models outperformed linear models on the basis of both statistical and economic criteria. Specifically, although return serial correlation receded by around 10 minutes, return predictability still persisted for up to 60 minutes according to nonlinear models, even though profitability decreases as time elapses. More flexible nonlinear models such as support vector machines and artificial neural network did not clearly outperform other nonlinear models. Copyright © 2011 John Wiley & Sons, Ltd.

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1989A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle 1994Comparing Predictive Accuracy 2004The Adaptive Markets Hypothesis 1992A Simple Nonparametric Test of Predictive Performance 1989Testing for Nonlinear Dependence in Daily Foreign Exchange Rates 2000Firm Size and Cyclical Variations in Stock Returns 2006Liquidity and Autocorrelations in Individual Stock Returns 2005Evidence on the speed of convergence to market efficiency

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