Stock Forecasting with Feedforward Neural Networks and Gradual Data Sub-Sampling

We use feed-forward neural networks to forecast and trade the future index prices of the Standard and Poor’s 500 (S&P 500). The effect of training the network with the most recent data, together with gradually sub-sampled past index data, has been studied in this research. We also study the effect of past NASDAQ 100 data on the prediction of future S&P 500. A daily trading strategy has been used, to buy/sell, according to the predicted prices, and hence calculate the directional efficiency and the rate of returns for different periods. We are able to obtain significantly higher returns compared to earlier work. There are numerous exchange traded funds (ETFs), which attempt to replicate the performance of S&P 500 by holding the same stocks in same proportion as the index, and therefore, giving the same percentage returns as S&P 500. Therefore, this study can be used to invest in any of the various ETFs, which replicates the performance of S&P 500.