Supervised classification-based stock prediction and portfolio optimization

TL;DR

This paper presents a machine learning approach for stock prediction and portfolio optimization using extensive financial parameters, achieving better-than-market growth in out-of-sample tests.

Contribution

It introduces a supervised classification method leveraging large-scale financial data and support vector machines for automated stock selection and portfolio growth.

Findings

3% higher growth than the market in 3 months

Effective use of company fundamentals and high-dimensional data

Support vector machine enhances classification accuracy

Abstract

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.