Predicting Prices Of S&P 500 Index Using Classical Methods and Recurrent Neural Networks

This study implements algorithmic investment strategies based on classical methods and a recurrent neural network model. The research compares the performance of investment algorithms on time series of the S&P 500 Index covering 20 years of data from 2000 to 2020. We present an approach for the dynamic optimization of parameters during the backtesting process by using a rolling training-testing window. Each method was tested in terms of robustness to changes in parameters and evaluated by appropriate performance statistics, such as the Information Ratio and Maximum Drawdown. The combination of signals from different methods was stable and outperformed the benchmark of the Buy&Hold strategy, doubling its returns while maintaining the same level of risk. Detailed sensitivity analysis revealed that classical methods utilizing a rolling training-testing window were significantly more robust to changes in parameters than the LSTM model.