Beating the S&P 500 index — A successful neural network approach

The systematic trading of equities forms the basis of the asset management industry. Analysts are trying to outperform a passive investment in an index such as the S&P 500 Index. However, statistics have shown that most analysts fail to consistently beat the index. A number of Neural Network based methods for detecting trading opportunities on Futures contracts on the S&P 500 Index have been published in the literature. However, such methods have generally been unable to demonstrate sustained performance over a significant period of time. The authors of this paper show, through the application of over ten years of experience in quantitative modelling and trading, a different type of Neural Network approach to beating the S&P 500 Index. Rather than trading Futures contracts, it is shown that by using Neural Networks to intelligently select just a handful of stocks a performance significantly in excess of a buy and hold position on the S&P 500 Index could have been achieved over a seven year period. The effect of transaction costs is also considered.