淡江大學機構典藏:Item 987654321/112495
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/112495


    Title: Actionable stock portfolio mining by using genetic algorithms
    Authors: C. H. Chen;C. Y. Hsieh
    Keywords: data mining;domain-driven data mining;genetic algorithms;minimum transaction lots;stock portfolio optimization
    Date: 2016-11
    Issue Date: 2017-12-22 02:10:16 (UTC+8)
    Publisher: Institute of Information Science
    Abstract: 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.
    Relation: Journal of Information Science and Engineering 32(6), p.1657-1678
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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