Testing the Weak Form Market Efficiency: Empirical Evidence from the Italian Stock Exchange

Abstract

This paper investigates the use of feed forward neural networks for testing the weak form market efficiency. In contrast to approaches that compare out-of-sample predictions of non-linear models to those generated by the random walk model, we directly focus on testing for unpredictability by considering the null hypothesis that a given set of past lags has no effect on current returns. To avoid the data-snooping problem the testing procedure is based on the StepM approach in order to control the familiwise error rate. The procedure is used to test for predictive power in FTSE-MIB index of the italian stock market.