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| 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 |
| Creator | 葉宇辰 Ye, Yu-Chen |
| Contributor | 林士貴<br>蔡銘峰 葉宇辰 Ye, Yu-Chen |
| Key Words | 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 |
| Date Issued | 1-Jul-2020 13:41:26 (UTC+8) |
| Summary | 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. |
| 參考文獻 | 1.Avramov, D., & Zhou, G. (2010). Bayesian portfolio analysis. Annual Review of Financial Economics., 2(1), 25-47. 2.Baskin, J. (1989). Dividend policy and the volatility of common stocks. Journal of portfolio Management, 15(3), 19. 3.Black, F. (1992). Beta and return. Journal of portfolio management, 1. 4.Black, F., Jensen, M. C., & Scholes, M. (1972). The capital asset pricing model: Some empirical tests. Studies in the theory of capital markets, 81(3), 79-121. 5.Blume, M. E. (1980). Stock returns and dividend yields: Some more evidence. The Review of Economics and Statistics, 567-577. 6.Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. 7.Cura, T. (2009). Particle swarm optimization approach to portfolio optimization. Nonlinear analysis: Real world applications, 10(4), 2396-2406. 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. 9.Deng, G. F., Lin, W. T., & Lo, C. C. (2012). Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization. Expert Systems with Applications, 39(4), 4558-4566. 10.Eberhart, R., & Kennedy, J. (1995, November). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (Vol. 4, pp. 1942-1948). Citeseer. 11.Gombola, M. J., & Liu, F. Y. L. (1993). Dividend yields and stock returns: Evidence of time variation between bull and bear markets. Financial Review, 28(3), 303-327. 12.Gordon, R. H., & Bradford, D. F. (1980). Taxation and the stock market valuation of capital gains and dividends: Theory and emphirical results. Journal of Public Economics, 14(2), 109-136. 13.Gwilym, O. A., Morgan, G., & Thomas, S. (2000). Dividend stability, dividend yield and stock returns: UK evidence. Journal of Business Finance & Accounting, 27(3‐4), 261-281. 14.Larivière, B., & Van den Poel, D. (2005). Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Systems with Applications, 29(2), 472-484. 15.Litzenberger, R. H., & Ramaswamy, K. (1979). The effect of personal taxes and dividends on capital asset prices: Theory and empirical evidence. Journal of financial economics, 7(2), 163-195. 16.Liu, M., Wang, M., Wang, J., & Li, D. (2013). Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar. Sensors and Actuators B: Chemical, 177, 970-980. 17.Markowitz, H. (1959). Portfolio selection: Efficient diversification of investments (Vol. 16). New York: John Wiley. 18.Markowitz, H. M. (2010). Portfolio theory: as I still see it. Annual Review of Financial Economics, 2(1), 1-23. 19.Michaud, R. O. (1989). The Markowitz optimization enigma: Is ‘optimized’optimal? Financial Analysts Journal, 45(1), 31-42. 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. 21.Sherwood, M. W., & Pollard, J. L. (2018). The risk-adjusted return potential of integrating ESG strategies into emerging market equities. Journal of Sustainable Finance & Investment, 8(1), 26-44. 22.Verheyden, T., Eccles, R. G., & Feiner, A. (2016). ESG for all? The impact of ESG screening on return, risk, and diversification. Journal of Applied Corporate Finance, 28(2), 47-55. |
| Description | 碩士 國立政治大學 金融學系 107352014 |
| 資料來源 | http://thesis.lib.nccu.edu.tw/record/#G0107352014 |
| Type | thesis |
| dc.contributor.advisor | 林士貴<br>蔡銘峰 | zh_TW |
| dc.contributor.author (Authors) | 葉宇辰 | zh_TW |
| dc.contributor.author (Authors) | Ye, Yu-Chen | en_US |
| dc.creator (作者) | 葉宇辰 | zh_TW |
| dc.creator (作者) | Ye, Yu-Chen | en_US |
| dc.date (日期) | 2020 | en_US |
| dc.date.accessioned | 1-Jul-2020 13:41:26 (UTC+8) | - |
| dc.date.available | 1-Jul-2020 13:41:26 (UTC+8) | - |
| dc.date.issued (上傳時間) | 1-Jul-2020 13:41:26 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0107352014 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/130543 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 金融學系 | zh_TW |
| dc.description (描述) | 107352014 | zh_TW |
| dc.description.abstract (摘要) | 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。 | zh_TW |
| dc.description.abstract (摘要) | 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. | en_US |
| dc.description.tableofcontents | 第一章 緒論........................................... 1 1.1 研究動機與目的.................................. 1 1.2 研究流程與架構.................................. 2 第二章 文獻探討....................................... 4 2.1 股票殖利率、Beta與股票報酬....................... 4 2.2 Markowitz 投資組合理論.......................... 6 2.3 股票殖利率、Beta與股票報酬....................... 7 2.4 粒子群最佳化.................................... 8 2.5 ESG程度與投資組合報酬............................ 9 第三章 研究方法 ...................................... 11 3.1 隨機森林....................................... 11 3.2 投資組合最佳化問題與粒子群最佳化.................. 13 3.2 以PSO方法處理CCMPO問題.......................... 17 第四章 研究結果 ...................................... 21 4.1 資料描述....................................... 21 4.2 隨機森林預測結果................................ 24 4.3 PSO配置績效.................................... 31 第五章 結論 ......................................... 38 參考文獻 ............................................ 40 附件 ................................................ 43 | zh_TW |
| dc.format.extent | 4771972 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0107352014 | en_US |
| dc.subject (關鍵詞) | 隨機森林 | zh_TW |
| dc.subject (關鍵詞) | 投資組合理論 | zh_TW |
| dc.subject (關鍵詞) | 粒子群最佳化 | zh_TW |
| dc.subject (關鍵詞) | Random Forests | en_US |
| dc.subject (關鍵詞) | Particle Swarm Optimization | en_US |
| dc.subject (關鍵詞) | Portfolio Theory | en_US |
| dc.subject (關鍵詞) | ESG | en_US |
| dc.title (題名) | 建構ESG股息波動投資組合：隨機森林與PSO方法的結合 | zh_TW |
| dc.title (題名) | Constructing ESG Portfolio with the Characteristic of Dividend Yields and Volatility: Combining Random Forests and PSO Method | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | 1.Avramov, D., & Zhou, G. (2010). Bayesian portfolio analysis. Annual Review of Financial Economics., 2(1), 25-47. 2.Baskin, J. (1989). Dividend policy and the volatility of common stocks. Journal of portfolio Management, 15(3), 19. 3.Black, F. (1992). Beta and return. Journal of portfolio management, 1. 4.Black, F., Jensen, M. C., & Scholes, M. (1972). The capital asset pricing model: Some empirical tests. Studies in the theory of capital markets, 81(3), 79-121. 5.Blume, M. E. (1980). Stock returns and dividend yields: Some more evidence. The Review of Economics and Statistics, 567-577. 6.Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. 7.Cura, T. (2009). Particle swarm optimization approach to portfolio optimization. Nonlinear analysis: Real world applications, 10(4), 2396-2406. 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. 9.Deng, G. F., Lin, W. T., & Lo, C. C. (2012). Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization. Expert Systems with Applications, 39(4), 4558-4566. 10.Eberhart, R., & Kennedy, J. (1995, November). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (Vol. 4, pp. 1942-1948). Citeseer. 11.Gombola, M. J., & Liu, F. Y. L. (1993). Dividend yields and stock returns: Evidence of time variation between bull and bear markets. Financial Review, 28(3), 303-327. 12.Gordon, R. H., & Bradford, D. F. (1980). Taxation and the stock market valuation of capital gains and dividends: Theory and emphirical results. Journal of Public Economics, 14(2), 109-136. 13.Gwilym, O. A., Morgan, G., & Thomas, S. (2000). Dividend stability, dividend yield and stock returns: UK evidence. Journal of Business Finance & Accounting, 27(3‐4), 261-281. 14.Larivière, B., & Van den Poel, D. (2005). Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Systems with Applications, 29(2), 472-484. 15.Litzenberger, R. H., & Ramaswamy, K. (1979). The effect of personal taxes and dividends on capital asset prices: Theory and empirical evidence. Journal of financial economics, 7(2), 163-195. 16.Liu, M., Wang, M., Wang, J., & Li, D. (2013). Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar. Sensors and Actuators B: Chemical, 177, 970-980. 17.Markowitz, H. (1959). Portfolio selection: Efficient diversification of investments (Vol. 16). New York: John Wiley. 18.Markowitz, H. M. (2010). Portfolio theory: as I still see it. Annual Review of Financial Economics, 2(1), 1-23. 19.Michaud, R. O. (1989). The Markowitz optimization enigma: Is ‘optimized’optimal? Financial Analysts Journal, 45(1), 31-42. 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. 21.Sherwood, M. W., & Pollard, J. L. (2018). The risk-adjusted return potential of integrating ESG strategies into emerging market equities. Journal of Sustainable Finance & Investment, 8(1), 26-44. 22.Verheyden, T., Eccles, R. G., & Feiner, A. (2016). ESG for all? The impact of ESG screening on return, risk, and diversification. Journal of Applied Corporate Finance, 28(2), 47-55. | zh_TW |
| dc.identifier.doi (DOI) | 10.6814/NCCU202000556 | en_US |