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    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/130543
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/130543


    Title: 建構ESG股息波動投資組合:隨機森林與PSO方法的結合
    Constructing ESG Portfolio with the Characteristic of Dividend Yields and Volatility: Combining Random Forests and PSO Method
    Authors: 葉宇辰
    Ye, Yu-Chen
    Contributors: 林士貴
    蔡銘峰

    葉宇辰
    Ye, Yu-Chen
    Keywords: 隨機森林
    投資組合理論
    粒子群最佳化
    Random Forests
    Particle Swarm Optimization
    Portfolio Theory
    ESG
    Date: 2020
    Issue Date: 2020-07-01 13:41:26 (UTC+8)
    Abstract: 本文使用台灣證券市場在2012到2019年的資料,利用隨機森林模型與PSO方法探討以下議題:(一) 觀察會影響股息波動特性預測的重要變數;(二) 以PSO方法解決基數限制下Markowitz最佳化問題並建構投資組合;(三) 比較不同股息波動特性下的投資組合績效;(四) 比較具備高ESG排名的個股,其股息波動特性對投資組合績效的影響。本文的實證結果可以歸納如下:(一) 影響股息波動特性的主要因素為過去三年與股息或波動有直接或間接關係的變數,例如現金殖利率、Beta值、周轉率與成交量;(二) PSO方法解決基數限制的Markowitz最佳化問題有助於投資組合的配置,八組投資組合裡有六組都優於使用一般Markowitz最佳化的投資組合。(三) 在建構投資組合上股息波動特性對於投資組合報酬有正向關係;(四) ESG、股息與波動之間可能會相互牴觸,當投資組合結合過多的特性時,對於投資組合而言不全然都是正面影響。
    This article uses the data of the Taiwan securities market from 2012 to 2019, using the random forest model and the PSO method to discuss the following topics: (1) Observe the important variables that affect the predictability of dividend and volatility characteristics; (2) Use the PSO method solves cardinality constrained Markowitz portfolio optimization problems and construct portfolio; (3) Compare portfolio performance under different dividend and volatility characteristics; (4) Compare the impact of dividend and volatility characteristics on portfolio performance when stocks have higher rank of ESG. The empirical results of this paper can be summarized as follows: (1) The main factors that affect the characteristics of dividend and volatility are variables that have a direct or indirect relationship with dividends or volatility in the past three years, such as cash yield rate, Beta value, turnover rate and trading volume; (2) The PSO method solves cardinality constrained Markowitz portfolio optimization problems, which is helpful for the returns of the portfolio. Six of the eight groups of portfolios are superior to the general Markowitz optimized portfolio. (3) Dividends and volatility in the construction of a portfolio have a positive relationship with portfolio returns; (4) ESG, dividends and volatility may conflict with each other. When the portfolio combines too many characteristics, there are not always have positive effects for the portfolio.
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    8.Czerwińska, T., & Kaźmierkiewicz, P. (2015). ESG rating in investment risk analysis of companies listed on the public market in Poland. Economic Notes: Review of Banking, Finance and Monetary Economics, 44(2), 211-248.
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    20.Nugrahaeni, R. A., & Mutijarsa, K. (2016, August). Comparative analysis of machine learning KNN, SVM, and random forests algorithm for facial expression classification. In 2016 International Seminar on Application for Technology of Information and Communication (ISemantic) (pp. 163-168). IEEE.
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    Description: 碩士
    國立政治大學
    金融學系
    107352014
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0107352014
    Data Type: thesis
    DOI: 10.6814/NCCU202000556
    Appears in Collections:[金融學系] 學位論文

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