A comparison of a factor-based investment strategy and machine learning for predicting excess returns on the JSE

This study investigated the application of Machine Learning to portfolio selection by comparing the application of a Factor Based Investment strategy to one using a Support Vector Machine performing a classification task. The Factor Based Strategy uses regression in order to identify factors correlated to returns, by regressing excess returns against the factor values using historical data from the JSE. A portfolio-sort method is used to construct portfolios. The machine learning model was trained on historical share data from the Johannesburg Stock Exchange. The model was tasked with classifying whether a share over or under performed relative to the market. Shares were ranked according to probability of over-performance and divided into equally weighted quartiles. The excess return of the top and bottom quartiles was used to calculate portfolio payoff, which is the basis for comparison. The experiments were divided into time periods to assess the consistency of the factors over different market conditions. The time periods were defined as pre-financial crisis, during the financial crisis, post financial crisis and over the full period. The study was conducted in the context of the Johannesburg Stock Exchange. Historical data was collected for a 15-year period - from May 2003 to May 2018 - on the constituents of the All Share Index (ALSI). A rolling window methodology was used where the training and testing window was shifted with each iteration over the data. This allowed for a larger number of predictions to be made and for a greater period of comparison with the factorbased strategy. Fourteen factors were used individually as the basis for portfolio construction. While combinations of factors into Quality, Value and Liquidity and Leverage categories was used to investigate the effect of additional inputs into the model. Furthermore, experiments using all factors together were performed. It was found that a single factor FBI can consistently outperform the market, a multi factor FBI also provided consistent excess returns, but the SVM provided consistently larger excess returns with a wide range of factor inputs and beat the FBI in 12 of the 14 different experiments over different time periods.

Grantor

Department of Computer Science

Year dc:date.issued

2018

Author dc:creator

• Drue, Stefan

Advisor dc:contributor.advisor

• Moodley, Deshendran

• Information Technology

Handle dc:identifier.uri

http://hdl.handle.net/11427/31386

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/31386