Non-linear forecast of returns at BOVESPA: trading volume in a smooth transition auto regressive model

Abstract

In this study, the predictive power of a logistic smooth transition auto regression model (LSTAR) in generating statistically significant returns is evaluated when the transition variable is trading volume and the lagged return itself, for the São Paulo Stock Exchange's Ibovespa Index, with the analysis based on daily data between 1996 and 2006. The reason for the inclusion of trading volume is found in some market characteristics and behavioral finance results, which indicate the existence of a negative relationship between trading volume and future returns. The model shows a good adjustment to the data, although it does not have the ability to generate additional profits if the transaction costs are of 0.5% per trade. For lower costs there is some predictive power, though lower than an AR(1) model and a buy and hold strategy. Considering the risk, for transaction costs of 0.035% per trade, the autoregressive model permitted a Sharpe index 20% larger than the buy and hold strategy.

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