學術產出-Theses
Article View/Open
Publication Export
-
題名 企業ESG評分對其股價連動效應以及ESG投資策略之探討
Discussion on the co-movement effect of corporate ESG scores on its stock price and the performance of ESG investment strategy作者 陳信全
Chen, Hsin-Chuan貢獻者 楊曉文
Yang, Sheau-Wen
陳信全
Chen, Hsin-Chuan關鍵詞 連動性
共整合
ESG
Co-movement
Co-integration
ESG日期 2022 上傳時間 1-Aug-2022 17:27:26 (UTC+8) 摘要 近幾年ESG的興起及國際之間永續規範制定，使得ESG投資逐漸成為一種偏好。許多研究已經探討ESG投資組合績效相關議題，但其結果仍不一致。產生不同結果可能為其中兩個原因，一為企業投入ESG後回報期間效果較長，無法在短期當中顯現。另一原因為在現行規範下，ESG投資容易有過度集中而無法充分分散投資組合風險的可能。因此本研究論文採用Bloomberg資料庫ESG分數進行ESG投資組合構建，並使用共整合方法設計連動性集中度，依此建構出相互連動（MC）與非相互連動（NMC）的投資組合族群。研究樣本以2007-2017年美國S&P 500成分股為例，透過ESG資料取得的滯後性搭配共整合檢定，檢驗2010-2020年的投資組合報酬績效是否有較佳的長期報酬。實證結果顯示，ESG Good族群相對ESG Bad族群擁有較高的連動性集中度，並且在ESG連動性投資組合上，ESG Good族群其特雷諾比率（TR）低於ESG Bad族群，代表ESG Good 族群MC的投資組合承擔較高的系統性風險。另外，在NMC的投資組合下，ESG連動性投資組合上績效指標皆優於MC，表示未受關注的股票池確實擁有較高的績效，其中又以ESG Good族群差異最為明顯，表示ESG Good族群相對ESG Bad族群在市場上的期望與需求較高，資訊成本效應下改善其績效表現。最後，透過共整合應用多空策略於連動性投資組合上，ESG整體組合達至Fama-French五因子的5%顯著Alpha，其主要效應來自MC的負向效果，在NMC的投資組合上可能需增加額外的因素考量來獲得顯著Alpha。
In recent years, ESG investing has gradually become an investment style preference. While many studies have explored the issue of ESG and performance, the results have remained inconsistent. There are two possible reasons for this: At first, company invests in ESG for a long-term to return, which cannot be manifested in the short-term. Another reason is that under the current norms, ESG investment is easy to be too concentrated and unable to fully diversify the risk of the portfolio. Therefore, this study uses the Bloomberg ESG score to construct ESG portfolios, and uses the co-integration approach to design co-movement density (CD), thereby constructing the mutual co-movement (MC) and no mutual co-movement (NMC) portfolio groups. The research sample takes the US S&P 500 constituent stocks from 2007 to 2017 as an example, and tests whether the portfolio has better long-tern performance from 2010 to 2020.The empirical results show that the ESG Good group has a higher CD than the ESG Bad group. In the ESG MC portfolio, the Treynor ratio of the ESG Good group is lower than that of the ESG Bad group, representing the portfolio of the ESG Good group MC have higher systemic risk. In addition, under NMC`s portfolio, the performance indicators of ESG co-movement portfolios are all better than MC`s, indicating that the stock pools that have not received attention do have higher performance. Finally, through the co-integration to create long-short strategies portfolio, the ESG_All portfolio has achieved a 5% significant Alpha of the Fama-French five factors model. However, its main effect comes from the negative effect of MC. Therefore, need to other factors to obtain a significant alpha to NMC portfolio.參考文獻 Anton, M., & Polk, C. (2014). Connected Stocks. Journal of Finance, 69(3), 1099-1127. https://doi.org/10.1111/jofi.12149Amon, J., Rammerstorfer, M., & Weinmayer, K. (2021). Passive ESG Portfolio Management—The Benchmark Strategy for Socially Responsible Investors. Sustainability, 13(16), 9388. https://www.mdpi.com/2071-1050/13/16/9388Barberis, N., & Shleifer, A. (2003). Style investing. Journal of Financial Economics, 68(2), 161-199. https://doi.org/10.1016/s0304-405x(03)00064-3Barberis, N., Shleifer, A., & Wurgler, J. (2005). Comovement. Journal of Financial Economics, 75(2), 283-317. https://doi.org/10.1016/j.jfineco.2004.04.003Black, F. (1972). CAPITAL MARKET EQUILIBRIUM WITH RESTRICTED BORROWING. Journal of Business, 45(3), 444-455. https://doi.org/10.1086/295472Boginski, V., Butenko, S., & Pardalos, P. M. (2006). Mining market data: A network approach. Computers & Operations Research, 33(11), 3171-3184. https://doi.org/10.1016/j.cor.2005.01.027Brav, A., & Lehavy, R. (2003). An empirical analysis of analysts` target prices: Short-term informativeness and long-term dynamics. Journal of Finance, 58(5), 1933-1967. https://doi.org/10.1111/1540-6261.00593Cerqueti, R., Ciciretti, R., Dalò, A., & Nicolosi, M. (2021). ESG investing: A chance to reduce systemic risk. Journal of Financial Stability, 54, 100887. https://doi.org/10.1016/j.jfs.2021.100887Dunis, C. L., & Ho, R. (2005). Cointegration portfolios of European equities for index tracking and market neutral strategies. Journal of Asset Management, 6(1), 33-52.Ehrmann, M., & Jansen, D.-J. (2020). Stock Return comovement when investors are distracted: More, and more homogeneous.Engle, R. F., & Granger, C. W. J. (1987). COINTEGRATION AND ERROR CORRECTION - REPRESENTATION, ESTIMATION, AND TESTING. Econometrica, 55(2), 251-276. https://doi.org/10.2307/1913236Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22. https://doi.org/10.1016/j.jfineco.2014.10.010Friede, G., Busch, T., & Bassen, A. (2015). ESG and financial performance: aggregated evidence from more than 2000 empirical studies. Journal of Sustainable Finance & Investment, 5(4), 210-233.Galenko, A., Popova, E., & Popova, I. (2012). Trading in the presence of cointegration. The Journal of Alternative Investments, 15(1), 85-97.Granger, C. W. J. (1981). SOME PROPERTIES OF TIME-SERIES DATA AND THEIR USE IN ECONOMETRIC-MODEL SPECIFICATION. Journal of Econometrics, 16(1), 121-130. https://doi.org/10.1016/0304-4076(81)90079-8Greenwood, R. (2008). Excess comovement of stock returns: Evidence from cross-sectional variation in Nikkei 225 weights. Review of Financial Studies, 21(3), 1153-1186. https://doi.org/10.1093/rfs/hhm052Hameed, A., Morck, R., Shen, J. F., & Yeung, B. (2015). Information, Analysts, and Stock Return Comovement. Review of Financial Studies, 28(11), 3153-3187. https://doi.org/10.1093/rfs/hhv042Huang, W.-Q., Zhuang, X.-T., & Yao, S. (2009). A network analysis of the Chinese stock market. Physica A: Statistical Mechanics and its Applications, 388(14), 2956-2964. https://doi.org/10.1016/j.physa.2009.03.028Lintner, J. (1965). THE VALUATION OF RISK ASSETS AND THE SELECTION OF RISKY INVESTMENTS IN STOCK PORTFOLIOS AND CAPITAL BUDGETS. Review of Economics and Statistics, 47(1), 13-37. https://doi.org/10.2307/1924119Majapa, M., & Gossel, S. J. (2016). Topology of the South African stock market network across the 2008 financial crisis. Physica A-statistical Mechanics and Its Applications, 445, 35-47.Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B - Condensed Matter and Complex Systems, 11(1), 193-197. https://doi.org/10.1007/s100510050929Markowitz, H. (1952). PORTFOLIO SELECTION. Journal of Finance, 7(1), 77-91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.xNaffa, H., & Fain, M. (2022). A factor approach to the performance of ESG leaders and laggards. Finance Research Letters, 44, 102073. https://doi.org/10.1016/j.frl.2021.102073Obrien, P. C., & Bhushan, R. (1990). ANALYST FOLLOWING AND INSTITUTIONAL OWNERSHIP. Journal of Accounting Research, 28, 55-76. https://doi.org/10.2307/2491247Pindyck, R. S., & Rotemberg, J. J. (1993). THE COMOVEMENT OF STOCK-PRICES. Quarterly Journal of Economics, 108(4), 1073-1104. https://doi.org/10.2307/2118460Sharpe, W. F. (1964). CAPITAL-ASSET PRICES - A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK. Journal of Finance, 19(3), 425-442. https://doi.org/10.2307/2977928Stock, J. H., & Watson, M. W. (1993). A SIMPLE ESTIMATOR OF COINTEGRATING VECTORS IN HIGHER ORDER INTEGRATED SYSTEMS. Econometrica, 61, 783-820.Tse, C. K., Liu, J., & Lau, F. C. M. (2010). A network perspective of the stock market. Journal of Empirical Finance, 17(4), 659-667. https://doi.org/10.1016/j.jempfin.2010.04.008Tu, C. (2014). Cointegration-based financial networks study in Chinese stock market. Physica A-statistical Mechanics and Its Applications, 402, 245-254.Tumminello, M., Di Matteo, T., Aste, T., & Mantegna, R. N. (2007). Correlation based networks of equity returns sampled at different time horizons. The European Physical Journal B, 55(2), 209-217. https://doi.org/10.1140/epjb/e2006-00414-4Veldkamp, L. L. (2006). Information markets and the comovement of asset prices. Review of Economic Studies, 73(3), 823-845. https://doi.org/10.1111/j.1467-937X.2006.00397.xVijh, A. M. (1994). STANDARD-AND-POOR-500 TRADING STRATEGIES AND STOCK-BETAS. Review of Financial Studies, 7(1), 215-251. https://doi.org/10.1093/rfs/7.1.215Wang, L., & Maxfield, S. (2018). The Impact of Socially Responsible Investing: What Can We Learn from Different Performance Measures? Unpublished Dissertation. School of Business, Providence College, IN. USA.Yu, J.-W., Xie, W.-J., & Jiang, Z.-Q. (2018). Early warning model based on correlated networks in global crude oil markets. Physica A: Statistical Mechanics and its Applications, 490, 1335-1343. 描述 碩士
國立政治大學
金融學系
108352006資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108352006 資料類型 thesis dc.contributor.advisor 楊曉文 zh_TW dc.contributor.advisor Yang, Sheau-Wen en_US dc.contributor.author (Authors) 陳信全 zh_TW dc.contributor.author (Authors) Chen, Hsin-Chuan en_US dc.creator (作者) 陳信全 zh_TW dc.creator (作者) Chen, Hsin-Chuan en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Aug-2022 17:27:26 (UTC+8) - dc.date.available 1-Aug-2022 17:27:26 (UTC+8) - dc.date.issued (上傳時間) 1-Aug-2022 17:27:26 (UTC+8) - dc.identifier (Other Identifiers) G0108352006 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141054 - 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) 108352006 zh_TW dc.description.abstract (摘要) 近幾年ESG的興起及國際之間永續規範制定，使得ESG投資逐漸成為一種偏好。許多研究已經探討ESG投資組合績效相關議題，但其結果仍不一致。產生不同結果可能為其中兩個原因，一為企業投入ESG後回報期間效果較長，無法在短期當中顯現。另一原因為在現行規範下，ESG投資容易有過度集中而無法充分分散投資組合風險的可能。因此本研究論文採用Bloomberg資料庫ESG分數進行ESG投資組合構建，並使用共整合方法設計連動性集中度，依此建構出相互連動（MC）與非相互連動（NMC）的投資組合族群。研究樣本以2007-2017年美國S&P 500成分股為例，透過ESG資料取得的滯後性搭配共整合檢定，檢驗2010-2020年的投資組合報酬績效是否有較佳的長期報酬。實證結果顯示，ESG Good族群相對ESG Bad族群擁有較高的連動性集中度，並且在ESG連動性投資組合上，ESG Good族群其特雷諾比率（TR）低於ESG Bad族群，代表ESG Good 族群MC的投資組合承擔較高的系統性風險。另外，在NMC的投資組合下，ESG連動性投資組合上績效指標皆優於MC，表示未受關注的股票池確實擁有較高的績效，其中又以ESG Good族群差異最為明顯，表示ESG Good族群相對ESG Bad族群在市場上的期望與需求較高，資訊成本效應下改善其績效表現。最後，透過共整合應用多空策略於連動性投資組合上，ESG整體組合達至Fama-French五因子的5%顯著Alpha，其主要效應來自MC的負向效果，在NMC的投資組合上可能需增加額外的因素考量來獲得顯著Alpha。 zh_TW dc.description.abstract (摘要) In recent years, ESG investing has gradually become an investment style preference. While many studies have explored the issue of ESG and performance, the results have remained inconsistent. There are two possible reasons for this: At first, company invests in ESG for a long-term to return, which cannot be manifested in the short-term. Another reason is that under the current norms, ESG investment is easy to be too concentrated and unable to fully diversify the risk of the portfolio. Therefore, this study uses the Bloomberg ESG score to construct ESG portfolios, and uses the co-integration approach to design co-movement density (CD), thereby constructing the mutual co-movement (MC) and no mutual co-movement (NMC) portfolio groups. The research sample takes the US S&P 500 constituent stocks from 2007 to 2017 as an example, and tests whether the portfolio has better long-tern performance from 2010 to 2020.The empirical results show that the ESG Good group has a higher CD than the ESG Bad group. In the ESG MC portfolio, the Treynor ratio of the ESG Good group is lower than that of the ESG Bad group, representing the portfolio of the ESG Good group MC have higher systemic risk. In addition, under NMC`s portfolio, the performance indicators of ESG co-movement portfolios are all better than MC`s, indicating that the stock pools that have not received attention do have higher performance. Finally, through the co-integration to create long-short strategies portfolio, the ESG_All portfolio has achieved a 5% significant Alpha of the Fama-French five factors model. However, its main effect comes from the negative effect of MC. Therefore, need to other factors to obtain a significant alpha to NMC portfolio. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究動機與目的 1第二節 研究架構 2第二章 文獻回顧 3第一節 資產連動（Comovement）與相關性 3第二節 投資組合族群策略建構 - 相關性與共整合 4第三節 ESG投資組合長期績效與集中度 5第三章 研究方法 7第一節 研究設計 7第二節 投資組合構建方式 11第三節 投資組合策略建構 13第四節 投資組合績效衡量與因子模型 13第四章 實證分析與結果 15第一節 研究資料 15第二節 連動性集中度（CD）分析 19第三節 連動性投資組合分析 21第四節 ESG連動性投資組合分析 22第五節 投資組合策略分析 24第六節 投資組合因子模型分析 26第五章 結論 28第六章 參考文獻 30第七章 附錄 34 zh_TW dc.format.extent 1821133 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108352006 en_US dc.subject (關鍵詞) 連動性 zh_TW dc.subject (關鍵詞) 共整合 zh_TW dc.subject (關鍵詞) ESG zh_TW dc.subject (關鍵詞) Co-movement en_US dc.subject (關鍵詞) Co-integration en_US dc.subject (關鍵詞) ESG en_US dc.title (題名) 企業ESG評分對其股價連動效應以及ESG投資策略之探討 zh_TW dc.title (題名) Discussion on the co-movement effect of corporate ESG scores on its stock price and the performance of ESG investment strategy en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Anton, M., & Polk, C. (2014). Connected Stocks. Journal of Finance, 69(3), 1099-1127. https://doi.org/10.1111/jofi.12149Amon, J., Rammerstorfer, M., & Weinmayer, K. (2021). Passive ESG Portfolio Management—The Benchmark Strategy for Socially Responsible Investors. Sustainability, 13(16), 9388. https://www.mdpi.com/2071-1050/13/16/9388Barberis, N., & Shleifer, A. (2003). Style investing. Journal of Financial Economics, 68(2), 161-199. https://doi.org/10.1016/s0304-405x(03)00064-3Barberis, N., Shleifer, A., & Wurgler, J. (2005). Comovement. Journal of Financial Economics, 75(2), 283-317. https://doi.org/10.1016/j.jfineco.2004.04.003Black, F. (1972). CAPITAL MARKET EQUILIBRIUM WITH RESTRICTED BORROWING. Journal of Business, 45(3), 444-455. https://doi.org/10.1086/295472Boginski, V., Butenko, S., & Pardalos, P. M. (2006). Mining market data: A network approach. Computers & Operations Research, 33(11), 3171-3184. https://doi.org/10.1016/j.cor.2005.01.027Brav, A., & Lehavy, R. (2003). An empirical analysis of analysts` target prices: Short-term informativeness and long-term dynamics. Journal of Finance, 58(5), 1933-1967. https://doi.org/10.1111/1540-6261.00593Cerqueti, R., Ciciretti, R., Dalò, A., & Nicolosi, M. (2021). ESG investing: A chance to reduce systemic risk. Journal of Financial Stability, 54, 100887. https://doi.org/10.1016/j.jfs.2021.100887Dunis, C. L., & Ho, R. (2005). Cointegration portfolios of European equities for index tracking and market neutral strategies. Journal of Asset Management, 6(1), 33-52.Ehrmann, M., & Jansen, D.-J. (2020). Stock Return comovement when investors are distracted: More, and more homogeneous.Engle, R. F., & Granger, C. W. J. (1987). COINTEGRATION AND ERROR CORRECTION - REPRESENTATION, ESTIMATION, AND TESTING. Econometrica, 55(2), 251-276. https://doi.org/10.2307/1913236Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22. https://doi.org/10.1016/j.jfineco.2014.10.010Friede, G., Busch, T., & Bassen, A. (2015). ESG and financial performance: aggregated evidence from more than 2000 empirical studies. Journal of Sustainable Finance & Investment, 5(4), 210-233.Galenko, A., Popova, E., & Popova, I. (2012). Trading in the presence of cointegration. The Journal of Alternative Investments, 15(1), 85-97.Granger, C. W. J. (1981). SOME PROPERTIES OF TIME-SERIES DATA AND THEIR USE IN ECONOMETRIC-MODEL SPECIFICATION. Journal of Econometrics, 16(1), 121-130. https://doi.org/10.1016/0304-4076(81)90079-8Greenwood, R. (2008). Excess comovement of stock returns: Evidence from cross-sectional variation in Nikkei 225 weights. Review of Financial Studies, 21(3), 1153-1186. https://doi.org/10.1093/rfs/hhm052Hameed, A., Morck, R., Shen, J. F., & Yeung, B. (2015). Information, Analysts, and Stock Return Comovement. Review of Financial Studies, 28(11), 3153-3187. https://doi.org/10.1093/rfs/hhv042Huang, W.-Q., Zhuang, X.-T., & Yao, S. (2009). A network analysis of the Chinese stock market. Physica A: Statistical Mechanics and its Applications, 388(14), 2956-2964. https://doi.org/10.1016/j.physa.2009.03.028Lintner, J. (1965). THE VALUATION OF RISK ASSETS AND THE SELECTION OF RISKY INVESTMENTS IN STOCK PORTFOLIOS AND CAPITAL BUDGETS. Review of Economics and Statistics, 47(1), 13-37. https://doi.org/10.2307/1924119Majapa, M., & Gossel, S. J. (2016). Topology of the South African stock market network across the 2008 financial crisis. Physica A-statistical Mechanics and Its Applications, 445, 35-47.Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B - Condensed Matter and Complex Systems, 11(1), 193-197. https://doi.org/10.1007/s100510050929Markowitz, H. (1952). PORTFOLIO SELECTION. Journal of Finance, 7(1), 77-91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.xNaffa, H., & Fain, M. (2022). A factor approach to the performance of ESG leaders and laggards. Finance Research Letters, 44, 102073. https://doi.org/10.1016/j.frl.2021.102073Obrien, P. C., & Bhushan, R. (1990). ANALYST FOLLOWING AND INSTITUTIONAL OWNERSHIP. Journal of Accounting Research, 28, 55-76. https://doi.org/10.2307/2491247Pindyck, R. S., & Rotemberg, J. J. (1993). THE COMOVEMENT OF STOCK-PRICES. Quarterly Journal of Economics, 108(4), 1073-1104. https://doi.org/10.2307/2118460Sharpe, W. F. (1964). CAPITAL-ASSET PRICES - A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK. Journal of Finance, 19(3), 425-442. https://doi.org/10.2307/2977928Stock, J. H., & Watson, M. W. (1993). A SIMPLE ESTIMATOR OF COINTEGRATING VECTORS IN HIGHER ORDER INTEGRATED SYSTEMS. Econometrica, 61, 783-820.Tse, C. K., Liu, J., & Lau, F. C. M. (2010). A network perspective of the stock market. Journal of Empirical Finance, 17(4), 659-667. https://doi.org/10.1016/j.jempfin.2010.04.008Tu, C. (2014). Cointegration-based financial networks study in Chinese stock market. Physica A-statistical Mechanics and Its Applications, 402, 245-254.Tumminello, M., Di Matteo, T., Aste, T., & Mantegna, R. N. (2007). Correlation based networks of equity returns sampled at different time horizons. The European Physical Journal B, 55(2), 209-217. https://doi.org/10.1140/epjb/e2006-00414-4Veldkamp, L. L. (2006). Information markets and the comovement of asset prices. Review of Economic Studies, 73(3), 823-845. https://doi.org/10.1111/j.1467-937X.2006.00397.xVijh, A. M. (1994). STANDARD-AND-POOR-500 TRADING STRATEGIES AND STOCK-BETAS. Review of Financial Studies, 7(1), 215-251. https://doi.org/10.1093/rfs/7.1.215Wang, L., & Maxfield, S. (2018). The Impact of Socially Responsible Investing: What Can We Learn from Different Performance Measures? Unpublished Dissertation. School of Business, Providence College, IN. USA.Yu, J.-W., Xie, W.-J., & Jiang, Z.-Q. (2018). Early warning model based on correlated networks in global crude oil markets. Physica A: Statistical Mechanics and its Applications, 490, 1335-1343. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200731 en_US
