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Forecasting Performance of Nonlinear Models for Intraday Stock Returns
Authors:José M. Matías  Juan C. Reboredo
Affiliation:1. Department of Statistics, University of Vigo, , Vigo, Spain;2. Department of Economics, University of Santiago de Compostela, , Santiago de Compostela, Spain
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.
Keywords:forecasting  time series  intraday stock returns  stock price forecasting  high‐frequency data  smooth transition regression  Markov switching regression  neural networks  nonparametric kernel regression  support vector machine regression
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