Optimizing a Pseudo Financial Factor Model with Support Vector Machines and Genetic Programming

Abstract

We compare the effectiveness of Support Vector Machines (SVM) and Tree-based Genetic Programming (GP) to make accurate predictions on the movement of the Dow Jones Industrial Average (DJIA). The approach is facilitated though a novel representation of the data as a pseudo financial factor model, based on a linear factor model for representing correlations between the returns in different assets. To demonstrate the effectiveness of the data representation the results are compared to models developed using only the monthly returns of the inputs. Principal Component Analysis (PCA) is initially used to translate the data into PC space to remove excess noise that is inherent in financial data. The results show that the algorithms were able to achieve superior investment returns and higher classification accuracy with the aid of the pseudo financial factor model. As well, both models outperformed the market benchmark, but ultimately the SVM methodology was superior in terms of accuracy and investment returns.