Abstract
We examine the problem of discrete stock price prediction using a synthesis of linguistic, financial and statistical techniques to create the Arizona Financial Text System (AZFinText). The research within this paper seeks to contribute to the AZFinText system by comparing AZFinText’s predictions against existing quantitative funds and human stock pricing experts. We approach this line of research using textual representation and statistical machine learning methods on financial news articles partitioned by similar industry and sector groupings. Through our research, we discovered that stocks partitioned by Sectors were most predictable in measures of Closeness, Mean Squared Error (MSE) score of 0.1954, predicted Directional Accuracy of 71.18% and a Simulated Trading return of 8.50% (compared to 5.62% for the S&P 500 index). In direct comparisons to existing market experts and quantitative mutual funds, our system’s trading return of 8.50% outperformed well-known trading experts. Our system also performed well against the top 10 quantitative mutual funds of 2005, where our system would have placed fifth. When comparing AZFinText against only those quantitative funds that monitor the same securities, AZFinText had a 2% higher return than the best performing quant fund.
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