教師資料查詢 | 類別: 期刊論文 | 教師: 葉怡成 YEH, I-CHENG (瀏覽個人網頁)

標題:Using Mixture Design and Neural Networks to Build Stock Selection Decision Support Systems
學年105
學期2
出版(發表)日期2017/03/01
作品名稱Using Mixture Design and Neural Networks to Build Stock Selection Decision Support Systems
作品名稱(其他語言)
著者Yi-Cheng Liu; I-Cheng Yeh
單位
出版者
著錄名稱、卷期、頁數Neural Computing and Applications 28(3), p.521-535
摘要There are three disadvantages of weighted scoring stock selection models. First, they cannot identify the relations between weights of stock-picking concepts and performances of portfolios. Second, they cannot systematically discover the optimal combination for weights of concepts to optimize the performances. Third, they are unable to meet various investors’ preferences. This study aims to more efficiently construct weighted scoring stock selection models to overcome these disadvantages. Since the weights of stock-picking concepts in a weighted scoring stock selection model can be regarded as components in a mixture, we used the simplex centroid mixture design to obtain the experimental sets of weights. These sets of weights are simulated with US stock market historical data to obtain their performances. Performance prediction models were built with the simulated performance data set and artificial neural networks. Furthermore, the optimization models to reflect investors’ preferences were built up, and the performance prediction models were employed as the kernel of the optimization models so that the optimal solutions can now be solved with optimization techniques. The empirical values of the performances of the optimal weighting combinations generated by the optimization models showed that they can meet various investors’ preferences and outperform those of S&P’s 500 not only during the training period but also during the testing period.
關鍵字Neural networks;design of experiments;multi-factor;weighted scoring;stock selection
語言英文
ISSN0941-0643;1433-3058
期刊性質國外
收錄於SCI;
產學合作
通訊作者I-Cheng Yeh
審稿制度
國別英國
公開徵稿
出版型式,電子版,紙本
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