Authors: Jie Du, Roy Rada
Addresses: Department of Information Systems, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA. ' Department of Information Systems, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
Abstract: 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.
Keywords: financial ratios; NLN; neural logic networks; electronic finance; asset valuation; learning; financial prediction; e-finance; financial returns; neural networks; stock market returns; financial statements; knowledge management.
International Journal of Electronic Finance, 2010 Vol.4 No.1, pp.19 - 38
Published online: 05 Jan 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article