A Weighted Portfolio Optimization Model Based on the Trend Ratio, Emotion Index, and ANGQTS

Abstract

A financial plan is crucial due to inflation, retirement, insurance, etc., and many people choose stock trading as one part of their overall investment portfolio. Recently, the COVID-19 pandemic has affected the economy and has had a significant impact on the stock market. The task of optimizing the portfolio to have a stable return and lower its overall risk becomes an important and emerging topic in today’s stock market. Therefore, this paper proposes a novel weighted portfolio optimization model based on the trend ratio and emotion index to comprehensively consider the volatility of the portfolio and more accurately evaluate the performance of portfolios than the classical indicator, the Sharpe ratio. Then, global-best guided quantum-inspired tabu search with a self-adaptive strategy and quantum-NOT gate (ANGQTS) which has better search ability than traditional optimization algorithm, is proposed to construct portfolios with stable upside trends efficiently and automatically. In order to dynamically suit such changeable stock markets, the proposed model adopts the sliding window mechanism. The proposed method is applied to the U.S. stock market. Compared with traditional methods and Dow Jones Industrial Average index, the proposed model shows more promising experimental results. Moreover, the proposed method derives better performance in both the downward crisis at the first outbreak of COVID-19 and the soaring trend in the stock market.