Prediction and Portfolio Optimization in Quantitative Trading Using Machine Learning Techniques

Abstract

Quantitative trading is an automated trading system in which the trading strategies and decisions are conducted by a set of mathematical models. Quantitative trading applies a wide range of computational approaches such as statistics, physics, or machine learning to analyze, predict, and take advantage of big data in finance for investment. This work studies core components of a quantitative trading system. Machine learning offers a number of important advantages over traditional algorithmic trading. With machine learning, multiple trading strategies are implemented consistently and able to adapt to real-time market. To demonstrate how machine learning techniques can meet quantitative trading, linear regression and support vector regression models are used to predict stock movement. In addition, multiple optimization techniques are used to optimize the return and control risk in trading. One common characteristic for both prediction models is they effectively performed in short-term prediction with high accuracy and return. However, in short-term prediction, the linear regression model is outperform compared to the support vector regression model. The prediction accuracy is considerably improved by adding technical indicators to dataset rather than adjusted price and volume. Despite the gap between prediction modeling and actual trading, the proposed trading strategy achieved a higher return than the S&P 500 ETF-SPY.