Training a Neural Logic Network to predict financial returns: a case study

Companies in the S&P 500 produce quarterly financial statements that are closely studied by investors who forecast the stock market performance of those companies. This paper describes a Neural Logic Network (NLN) for predicting stock market returns based on financial ratios from financial statements. A learning algorithm modifies the NLN in trying to make its output for each stock better correspond with the future market returns of that stock. The input to the NLN is financial ratios taken from quarterly financial statements of companies in the S&P 500. The hypothesis is that the accuracy of financial prediction can be improved by incorporating knowledge into the learning process. This knowledge can be incorporated either as evolutionary knowledge or financial knowledge. Experiments are designed and implemented to test this hypothesis. The results show that evolutionary knowledge improved performance but financial knowledge did not. The specifics of the financial knowledge situation demonstrate some of the challenges of effectively importing financial knowledge into an evolutionary programme.