Apply Genetic Algorithm and Fuzzy Inference to Forecast Stock Price Index

• Main Authors: CHEN,WEI, 陳葦
• Other Authors: CHOU,TSUNG-NAN
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/z2wk7j

碩士 === 朝陽科技大學 === 財務金融系 === 107 === In the present study, we explore the impact of company’s financial indicators on the companys stock price using the daily data drawn from the database of the Taiwan Economic Journal. Through employing fuzzy genetic algorithm, C4.5 decision tree and random forests, we investigate the relationship between company financial indicators and the companys stock price. We find that fuzzy genetic algorithm predicts an overall accuracy rate of 64.6% or more among them. The prediction accuracy of t on the TSMC’s stock price reached as high as 77.0% by using the fuzzy algorithm. The second best prediction for TSMCs is from the random forest. The accuracy rate is 65.5% overall. As for the prediction of Hon Hais stock price, the fuzzy genetic algorithm could have the highest accuracy rate of 64.6%. Therefore, it seems that the prediction accuracy of model would improve with fuzzifying the variables and the evolution of the genes. Furthermore, the volatility of the stock prices would have the impact on the accuracy of prediction model.