Paper Type

Master's Thesis


College of Computing, Engineering & Construction

Degree Name

Master of Science in Computer and Information Sciences (MS)



NACO controlled Corporate Body

University of North Florida. School of Computing

First Advisor

Dr. Sherif Elfayoumy

Second Advisor

Dr. Ching-Hua Chuan

Third Advisor

Dr. Pieter de Jong

Department Chair

Dr. Sherif Elfayoumy

College Dean

Dr. Mark A. Tumeo


A review of the literature applying Multilayer Perceptron (MLP) based Artificial Neural Networks (ANNs) to market forecasting leads to three observations: 1) It is clear that simple ANNs, like other nonlinear machine learning techniques, are capable of approximating general market trends 2) It is not clear to what extent such forecasted trends are reliably exploitable in terms of profits obtained via trading activity 3) Most research with ANNs reporting profitable trading activity relies on ANN models trained over one fixed interval which is then tested on a separate out-of-sample fixed interval, and it is not clear to what extent these results may generalize to other out-of-sample periods. Very little research has tested the profitability of ANN models over multiple out-of-sample periods, and the author knows of no pure ANN (non-hybrid) systems that do so while being dynamically retrained on new data. This thesis tests the capacity of MLP type ANNs to reliably generate profitable trading signals over rolling training and testing periods. Traditional error statistics serve as descriptive rather than performance measures in this research, as they are of limited use for assessing a system’s ability to consistently produce above-market returns. Performance is measured for the ANN system by the average returns accumulated over multiple runs over multiple periods, and these averages are compared with the traditional buy-and-hold returns for the same periods. In some cases, our models were able to produce above-market returns over many years. These returns, however, proved to be highly sensitive to variability in the training, validation and testing datasets as well as to the market dynamics at play during initial deployment. We argue that credible challenges to the Efficient Market Hypothesis (EMH) by machine learning techniques must demonstrate that returns produced by their models are not similarly susceptible to such variability.