A Novel Smart Beta Optimization Based on Probabilistic Forecast

Abstract

Rule-based portfolio construction strategies are rising as investment demand grows, and smart beta strategies are becoming a trend among institutional investors. Smart beta strategies have high transparency, low management costs, and better long-term performance, but are at the risk of severe short-term declines due to a lack of Risk Control tools. Although there are some methods to use historical volatility for Risk Control, it is still difficult to adapt to the rapid switch of market styles. How to strengthen the Risk Control management of the portfolio while maintaining the original advantages of smart beta has become a new issue of concern in the industry. This paper demonstrates the scientific validity of using a probability prediction for position optimization through an optimization theory and proposes a novel natural gradient boosting (NGBoost)-based portfolio optimization method, which predicts stock prices and their probability distributions based on non-Bayesian methods and maximizes the Sharpe ratio expectation of position optimization. This paper validates the effectiveness and practicality of the model by using the Chinese stock market, and the experimental results show that the proposed method in this paper can reduce the volatility by 0.08 and increase the expected portfolio cumulative return (reaching a maximum of 67.1%) compared with the mainstream methods in the industry.