Social Issues

In the last table you saw the results from the training errors from 1000 RPROP iterations of each of the S&P 500. Some companies trained better than others. Perhaps the patterns in their price movements were more repetitive. The next step is to take these neural networks, that were trained with historical data, and use them to attempt to predict recent price fluctuations. We will use all 500 companies again and see how they can predict the last 60 days of stock prices. This table summarizes this.

This table shows what the training error was for training a neural network over 10 years of data, from 1999 to 2008. 1000 training iterations of RPROP training was used on a neural network with an input window of 10, predict window of 1, using a 2-hidden layer feedforward neural network. Hidden layer 1 had 29 neurons, hidden layer 2 had 13 neurons. It is important to note that the percent is the training error, ideally you want to get this as low as possible. The companies with the higher percentages are more difficult to train for.