| Summary: | 碩士 === 國立暨南國際大學 === 資訊工程學系 === 105 === The first problem which investors face is stock selection when they invest in stock market. Therefore, stock selection is of paramount importance in stock investment. Generally, selecting stock considers return and risk simultaneously. The common assessment strategy is the Sharpe ratio. However, the Sharpe ratio uses the standard deviation as portfolio risk, so portfolio in uptrend has high risk. It defies investor psyche of most investors. Therefore, this paper proposes a novel assessment strategy, trend ratio. The trend ratio uses the simple linear regression which is improved with the initial funds to find the trend of portfolio. Slope of trend line is daily expected return, and difference between the trend line and the portfolio funds standardization is daily risk. It changes the way of the Sharpe ratio which assesses portfolio by return ratio and standard deviation. Hence, the trend ratio not only can find the portfolio which is in uptrend but also solves the problem of the Sharpe ratio. Besides, this paper does not limit stock number in a portfolio, so it uses the quantum-inspired tabu search algorithm which is improved by the current best-known solution and the quantum not gate (NQTS) to find the best portfolio. In the over-fitting problem, this paper uses the sliding window to avoid. Hence, this paper combines the trend ratio, NQTS and the sliding window to solve the problem of stock selection. The experiment results show our method can find the better portfolio than the Sharpe ratio, and it has better performance than Taiwan 50 ETF which is recommended by the government.
|
|---|
