Supervised classification-based stock prediction and portfolio optimization
This work addresses stock prediction and portfolio optimization for investors, but it is incremental as it builds on existing methods with more data.
The paper tackles automated stock picking by applying machine learning to a larger set of financial parameters than previous studies, resulting in a portfolio that shows 3% greater average growth than the market over a 3-month period in out-of-sample tests.
As the number of publicly traded companies as well as the amount of their financial data grows rapidly, it is highly desired to have tracking, analysis, and eventually stock selections automated. There have been few works focusing on estimating the stock prices of individual companies. However, many of those have worked with very small number of financial parameters. In this work, we apply machine learning techniques to address automated stock picking, while using a larger number of financial parameters for individual companies than the previous studies. Our approaches are based on the supervision of prediction parameters using company fundamentals, time-series properties, and correlation information between different stocks. We examine a variety of supervised learning techniques and found that using stock fundamentals is a useful approach for the classification problem, when combined with the high dimensional data handling capabilities of support vector machine. The portfolio our system suggests by predicting the behavior of stocks results in a 3% larger growth on average than the overall market within a 3-month time period, as the out-of-sample test suggests.