Portfolio Optimization Problem Based on Novel Return and Risk Assessment Strategy Improved by Linear Regression Model with Evolutionary Algorithm

• Main Authors: LIU, I-I, 劉懿誼
• Other Authors: CHOU, YAO-HSIN
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/10869050189213117260

碩士 === 國立暨南國際大學 === 資訊工程學系 === 104 === Stock selection is an important and primary issue while investing in the stock market. However, it is worth investigating in the problem of considering not only low risk but also high return on investment while selecting portfolio. From all researches, the Sharpe ratio is the most common criterion of stock selection, and its core idea is that investors would choose and hold portfolios that maximize returns under the given risk or minimize investment risks with the same amount of returns. The standard deviation is used to indicate the risk of portfolio, which is the funds standardization with the average line. From the advantages above, the Sharpe ratio makes investors avoid investing the portfolio with a fallen trend, but also considers the trend that rises stably with high risk. However, the Sharpe ratio considers the portfolio is better with high risk when return is negative. We proposed a new criterion of stock selection, called trend value. With the same core idea as Sharpe ratio, the system consults the trend of portfolio to improve return and risk assessment strategy. The trend line, found by linear regression model, presents the trend of portfolio. The slope of the trend line presented as return, and the risk is the funds standardization of the line. Besides, we revise the formula when return is negative. This assessment finds a portfolio that rises stably with lower risk. An Evolutionary Algorithm is used to compose the portfolio with the low risk and stable returns in component stocks of Taiwan 50 ETF, and the portfolio is without number constraint. Moreover, Over-fitting is a common problem in the stock market, and this paper uses sliding windows to replace portfolio periodically to avoid the problem. The experiment results show that the proposed method, compared with the Sharpe ratio, is able to identify the optimal portfolio and performs efficiently and outstandingly.