教師資料查詢 | 類別: 期刊論文 | 教師: 陳俊豪 CHUN-HAO CHEN (瀏覽個人網頁)

標題:Actionable stock portfolio mining by using genetic algorithms
學年105
學期1
出版(發表)日期2016/11/01
作品名稱Actionable stock portfolio mining by using genetic algorithms
作品名稱(其他語言)
著者C. H. Chen; C. Y. Hsieh
單位
出版者
著錄名稱、卷期、頁數Journal of Information Science and Engineering 32(6), p.1657-1678
摘要Financial markets have many financial instruments and derivatives, including stocks, futures, and options. Investors thus have many choices when creating a portfolio. For stock portfolio selection, many approaches that focus on optimizing the weights of assets using evolutionary algorithms have been proposed. Since investors may have various requests, an approach that takes these requests into consideration is needed. Based on the domain-driven data mining concept, this paper proposes a domain-driven stock portfolio optimization approach that can satisfy an investor's requests for mining an actionable stock portfolio. A set of stocks are first encoded into a chromosome. Two real numbers that represent whether to buy a stock and the number of purchased units, respectively, are utilized to represent each stock. In the fitness evaluation, each chromosome is evaluated in terms of the investor's objective and subjective interestingness. Objective interestingness includes return on investment and value at risk. Subjective interestingness contains a portfolio penalty and an investment capital penalty, which reflect the satisfactions of the investor's requests. Experiments on real datasets are conducted to show the effectiveness of the proposed approach.
關鍵字data mining;domain-driven data mining;genetic algorithms;minimum transaction lots;stock portfolio optimization
語言英文
ISSN1016-2364
期刊性質國內
收錄於SCI;
產學合作
通訊作者
審稿制度
國別中華民國
公開徵稿
出版型式,紙本
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