Profitability of alternative methods of combining the signals from technical trading systems

Abstract

Past efforts determining the profitability of technical analysis reached varied conclusions. We test the profitability of a composite prediction that uses buy and sell signals from technical indicators as inputs. Both machine learning methods, like neural networks, and statistical methods, like logistic regression, are used to get predictions. Inputs are signals from trend-following and mean-reversal technical indicators in addition to the variance of prices. Four representative commodities from agricultural, livestock, financial, and foreign exchange futures markets are selected to determine profitability. Special care is taken to avoid data snooping error. Both neural networks and statistical methods did not show consistent profitability.