Applying Least Squares Support Vector Machines to Mean‐Variance Portfolio Analysis

Abstract

Portfolio selection problem introduced by Markowitz has been one of the most important research fields in modern finance. In this paper, we propose a model (least squares support vector machines (LSSVM)-mean-variance) for the portfolio management based on LSSVM. To verify the reliability of LSSVM-mean-variance model, we conduct an empirical research and design an algorithm to illustrate the performance of the model by using the historical data from Shanghai stock exchange. The numerical results show that the proposed model is useful when compared with the traditional Markowitz model. Comparing the efficient frontier and total wealth of both models, our model can provide a more measurable standard of judgment when investors do their investment.

Efficient Solution of Portfolio Optimization Problems via Dimension Reduction and Sparsification

“…In addition, advancements in machine learning and other data science techniques have improved the ability to more accurately calculate µ and Σ, and are studied in recent literature [7][8][9][10].…”