Portfolio Optimization in Both Long and Short Selling Trading Using Trend Ratios and Quantum-Inspired Evolutionary Algorithms

Abstract

Stock selection is the first problem that investors encounter when investing in the stock market and is paramount. The Sharpe ratio is a common assessment strategy. However, the Sharpe ratio considers an uptrend portfolio high risk because it assesses portfolio risk using standard deviations. Hence, we propose a novel investment strategy, namely, the trend ratio, to assess portfolio risk more accurately by the portfolio trend line. Thus, the uptrend portfolio is not considered high risk and is more consistent with the psychology of investors. In addition to normal trading (long selling), short selling is another common trading method. Short selling is borrowing stocks from stock vendors to sell and then repaying the stock at a lower price to make a profit. This paper proposes investing simultaneously in normal trading and short selling by a trend ratio, which can further increase investment profits and spread risks. This paper also adds certificates of deposit as a portfolio choice to ensure that investors can still make profits. This paper utilizes the global quantum-inspired tabu search algorithm with a quantum NOT-gate (GNQTS) to effectively find the best combination of stocks. To avoid the overfitting problem, this paper employs a sliding window. Specifically, this paper combines the trend ratio, GNQTS, short selling with certificates of deposit, and sliding windows to perform the stock selection. The experimental results are promising, with our proposed method having better performance than the Sharpe ratio. Furthermore, the experimental results show that both long selling and short selling investments can increase the performance.

Keywords

• Stock selection
• portfolio optimization
• trend ratio
• short selling
• sharpe ratio
• sliding window