Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/137719

Title: 股票市場與ESG報酬之動態分析:馬可夫狀態轉換回歸法之應用
Dynamic Approach to Equity Markets’ Index and ESG Return: An Application of Markov Regime Switch Regression Method
Authors: 陳華恩
Tan, Timothy Hwa En
Contributors: 林月雲

Lin, Yeh-Yun
Wu, Chi-Ming

Tan, Timothy Hwa En
Keywords: 企業社會責任
Responsible Investment
Asset Pricing
Information and Market Efficiency
Date: 2021
Issue Date: 2021-11-01 12:15:23 (UTC+8)
Abstract: 摘要

本研究就股票市場平均超額報酬(α)與風險因子(β)於景氣與市場循環下所產生的結構性變異(heteroscedasticity)進行深究,並以動態馬可夫狀態轉換回歸法(Markov Regimes Switch Regression Method)取代過往線性回歸法(Ordinary Least Squares Regression method)用於資本資產定價模型(Capital Asset Pricing Model)及因子模型(Risk Factor Model)上,藉由非線性模型客觀的狀態轉換來觀察不同期間超額報酬與相關風險因子的改變。 從市場效率假說(Efficient Market Hypothesis)的角度,高效率的市場應伴隨著趨近於零的超額報酬率,惟本研究的結果卻是指出,當市場結構有所改變時,超額報酬與風險因子亦隨其變動,故,即便長期是依循市場效率假說,但是,伴隨景氣循環,不同風險因子的改變也會讓超額報酬有所改變。

不同於以往的文獻方法,本研究並不額外使用更多的變數,僅藉由馬可夫狀態轉換回歸法乃係「狀態依循(regime-dependent nature」)的特性來檢視傳統變數於不同市場循環(狀態)下的可能改變。 不同於其他時間序列分析(Time-Series Analysis)方法,如:自我回歸模型(autoregressive conditional heteroscedasticity)、門檻回歸模型(Threshold Regression Model)等,動態馬可夫狀態轉換回歸法有著讓允讓「狀態」可連續改變,並依照內生變數而決定所屬「狀態」的特性。

本研究透過動態馬可夫狀態轉換回歸法從時間序列資料中捕捉到動能因子(momentum factor)在不同市場循環階段中的反轉,即是由正轉為負或是由副轉回正,此外其他因子不同市場都有著類似的情況,並此結果具備統計上的顯著性。 此發現,在動能因子的部分與學者Daniel and Moskowitz (2016)等有類似結論,惟本研究乃是以不同方法探知同樣結果。 另外在其他因子依循市場循環而改變的部分,為Fama and French (2020)主張「價值因子」並未於美國市場消失提出另一個方向的論證,茲因為市場狀態的轉換也伴隨價值因子由正轉負而故讓長期平均失去統計上的顯著性。

本研究另外就ESG投資,也以動態馬可夫狀態轉換回歸法改良的資本資產定價模型及因子模型進行分析,確認ESG投資也因著市場狀態的改變而有著不明確的超額報酬。 如,以傳統線性回歸法使用CAPM及其他因子模型,於美國、日本及亞太市場都有著「負報酬」的統計顯著績效,惟當以動態馬可夫狀態轉換回歸法進行分析時,顯著性的「負報酬」往往僅發生在一特定的市場狀態循環下,而另一市場狀態下則是沒有達到統計顯著的報酬。 若以美國KLD指數為例,(最長期數據),其僅在一特定時期(狀態)內才會有著統計顯著的負報酬狀況產生,而此時期僅維持約一個月,相較於其他期間(狀態),ESG投資有著不亞於大盤的績效。

總結,不同於Lins et al. (2017)等學者指出ESG投資僅於金融風暴下,基於市場對於公司的信任(social trust)而產生的超額報酬,本研究結果顯示ESG投資也受到市場狀態循環的影響,風險因子與超額報酬都會依隨市場狀態而有些許改變,故投資於高ESG評價公司的策略應被視為另一種投資風格,或是另一種Smart Beta策略,宜從投資組合角度作為可降低投資風險同時提升投資報酬率的策略性工具使用。

This dissertation demonstrates the heteroscedasticity of mean excess returns (alpha) and risk factors (betas) in the stock market by extending dynamic Markov regime-switching regression (MRSR) to the capital asset pricing model and risk factor model to replace the ordinary least squares (OLS) regression approach. The nonlinearity of abnormal returns indicates that even stock markets in developed countries are in a semistrong efficient form in which a certain period of excess returns is possible. However, when market conditions change, marked by a regime switch, excess returns and risk factors change.

The regime-dependent nature of alpha and betas allows for an alternative approach to examining the change in risk factor premiums over time. Instead of using additional variables, such as short-term and long-term reversal, to examine the impact of momentum on portfolios and the changes in risk factor premiums over time, this paper adds a market regime-switching mechanism to risk factor models. Dynamic MRSR is fundamentally different from other time-series techniques, such as autoregressive conditional heteroscedasticity and threshold regression, which allow for a continuous (but irreversible) regime change. Dynamic MRSR advances the regime-switching mechanism, and endogenous probabilistic conditions determine the switch between regimes (the model determines which regime another regime should be switched according to the Markov chain property).

This dissertation documents momentum reversal under various market conditions (regimes), whereby momentum betas and other risk factors change from positive to negative or vice versa in a statistically significant manner. The results support those of studies on momentum crashes by Daniel and Moskowitz (2016) and others. This study also provides evidence that the worldwide issue of diminishing value premiums is regime-dependent, which supports Fama and French (2020) in that the variation in value premium is too large to confirm its disappearance.

When examining excess returns from ESG investment, traditional CAPM, and other risk factor models when applied with OLS, most of the time would yield a negative result in the US, Japan, and the Asia Pacific; however, when applying MRSR, it is evident that negative excess return is only statistically significant in the specific regime, but not in others regime. KLD (the U.S. Market) is the most extended available dataset globally, and the time probability results suggest only a month or so, during an economic recession, that KLD is underperformed the market index in a statistical significant manner.

In conclusion, this paper provides evidence that the benefits of responsible investment are not limited to small excess returns during financial crises (the benefit of social capital trust reduces a firm’s sensitivity to stock market downturns), as suggested by Lins et al. (2017) and others. Although the benefits of environmental, social, and governance (ESG) factors are small, and those excess returns are sensitive to market risk are regime-dependent and persist through economic crises. The results suggest that investing in firms with high ESG scores (consistent with corporate social responsibility theory) is beneficial from the perspective of investment portfolio construction because responsible investments are not related to the performance of the broad market and occasionally outperform the market.

The heteroscedasticity of alpha and betas warrants further research to develop investment portfolio construction techniques because the assumed homoscedasticity of assets’ expected means and variance does not yield optimized returns.
Reference: 9.0 References
Ahern Kenneth (2009). “Sample selection and event study estimation,” Journal of Empirical Finance 16(3):466-482.

Allen Goss and Gordon S. Roberts, “The impact of corporate social responsibility on the cost of bank loans,” Journal of Banking and Finance, July 2011, Volume 35, Number 7, pp. 1794–810.

Armitage Seth (1995). “Event Study Methods and Evidence on Their Performance,” Journal of Economic Surveys 9 (1): 25-52

Asness Cliff (2014.12.17). “Our Model Goes to Six and Saves Value From Redundancy Along the Way”, AQR: https://www.aqr.com/Insights/Perspectives/Our-Model-Goes-to-Six-and-Saves-Value-From-Redundancy-Along-the-Way (access date:2021. 06.15)

Barberis, N; Shleifer A; Vishny R (1998). "A Model of Investor Sentiment." Journal of Financial Economics. 49 (49): 307–343

Binder John (1998). “The Event Study Methodology since 1969”, Review of Quantitative Finance and Accounting, 11:111-137

Blanco Belen (2012). The use of CAPM and Fama and French Three-Factor
Model: portfolios selection. Public and Municipal Finance, 1(2):61-70

Bogle, John C (1999). Common Sense on Mutual Funds: New Imperatives for the Intelligent Investor, John Wiley & Sons

Brown S. and Warner J. (1985). “Using daily stock returns: The case of event studies”, Journal of Financial Economics, 14(1):3-31

Daniel, K; Hirschleifer D; Subrahmanyam A (1998). "A Theory of Overconfidence, Self-Attribution, and Security Market Under and Over-reactions". Journal of Finance 53:1839-1885

Daniel Kent and Moskowitz J. Tobias (2016), “Momentum Crashes”, Journal of Financial Economics, 122(2): 221-247.

Davidson, James (2004). “Forecasting Markov-switching Dynamic, Conditionally Heteroscedastic Processes,” Statistics & Probability Letters, 68, 137-147.

Diebold, Francis X., Lee, Joon-Haeng, and Gretchen C. Weinbach (1994). “Regime Switching with Time-Varying Transition Probabilities,” in C. Hargreaves (ed.), Nonstationary Time Series Analysis and Cointegration, Oxford: Oxford University Press, 283–302.

Engle, Robert F. (1982). "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation." Econometrica. 50 (4): 987–1007.

Fama and French, (1992). “The Cross-Section of Expected Stock Returns,” Journal of Finance. June 1992:427-465

Fama, E. F.; French, K. R. (1993). "Common risk factors in the returns on stocks and bonds." Journal of Financial Economics. 33: 3–56.

Fama, E. F.; French, K. R. (2012) "Size, Value, and Momentum in International Stock Returns", Journal of Financial Economics. 105(3):457-472

Fama and French (2015). “A Five-Factor Asset Pricing Model,” Journal of Financial Economics. 116: 1–22.

Fama and French (2015), “International Tests of a Five-Factor Asset Pricing Model,” Tuck School of Business Working Paper No. 2622782

Fama and French (2019), “Comparing Cross-Section and Time-Series Factor Models”, The Review of Financial Studies (2020 May), 33(5):1891-1926

Fama and French (2020), “Value Premium”, Chicago Both Paper No. 20-01
Filardo, Andrew J. (1994). “Business-Cycle Phases and Their Transitional Dynamics,” Journal of Business & Economic Statistics, 12, 299-308.

Frühwirth-Schnatter, Sylvia (2006). Finite Mixture and Markov Switching Models, New York: Springer Science+ Business Media LLC.

French, Craig W. (2003). "The Treynor Capital Asset Pricing Model". Journal of Investment Management 1 (2): 60–72.

Goldfeld, Stephen M. and Richard E. Quandt (1973). “A Markov Model for Switching Regressions,” Journal of Econometrics, 1(1):3–15.

Goldfeld, Stephen M. and Richard E. Quandt (1976), Studies in Nonlinear Estimation, Cambridge, MA: Ballinger Publishing Company.

Goss and Roberts (2011), “The Impact of Corporate Social Responsibility on the Cost of Banks Loans,” Journal of Banking and Finance 35(7): 1794-1810

Gunnar Friede et al., “ESG and financial performance: aggregated evidence from more than 2000 empirical studies,” Journal of Sustainable Finance & Investment, October 2015, Volume 5, Number 4, pp. 210–33

Hamilton, James D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle,” Econometrica, 57, 357–384.

Hamilton, James D. (1990). “Analysis of Time Series Subject to Changes in Regime,” Journal of Econometrics, 45, 39–70.

Hamilton, James D. (1994). Time Series Analysis, Chapter 22, Princeton: Princeton University Press.

Hamilton, James D. (1996). “Specification Testing in Markov-switching Time-series Models,” Journal of Econometrics, 70, 127–157.

Hansen, B. E. (1992). “The Likelihood Ratio Test Under Nonstandard Conditions: Testing the Markov Switching Model of GNP,” Journal of Applied Econometrics, 7, S6–S82.

Henisz and McGlinch (2019), “ESG, Material Credit Events, and Credit Risk,” Journal of Applied Corporate Finance 31:105-117

Juan Carlos Matallín-Sáez, Amparo Soler-Domínguez, Diego Víctor de Mingo-López, and Emili Tortosa-Ausina (2018), “Does socially responsible mutual fund performance vary over the business cycle? New insights on the effect of idiosyncratic SR features,” Business Ethics, A European Review (Business Ethics: Environment & Responsibility), 28(1):71-98

Kim, Chang-Jin (1994). “Dynamic Linear Models with Markov-Switching,” Journal of Econometrics, 60, 1–22.

Kim, Chang-Jin and Charles R. Nelson (1999). State-Space Models With Regime Switching, Cambridge: The MIT Press.

Krolzig, Hans-Martin (1997). Markov-Switching Vector Autoregressions: Modelling, Statistical Inference, and Application to Business Cycle Analysis, Berlin: Springer-Verlag.

Khan, Serafeim, and Yoon (2016), “Corporate Sustainability: First Evidence on Materiality,” Accounting Review 91(6):1697-1724

Kuan, Chung-Ming (2002), “Lecture on the Markov Switching Model,” Lecture Note, Institute of Economics, Academia Sinica.

Lins V. Karl, Servaes Henri, and Tamayo Ane (2017), “Social Capital Trust, and Fire Performance: The Value of Corporate Social Responsibility during the Financial Crisis,” The Journal of Finance, 72(4):1785-1824.

Li, Jun (2014), “Explaining Momentum and Value Simultaneously”. (September 8, 2014) Available at SSRN: https://ssrn.com/abstract=2179656

Low, R.K.Y.; Tan, E. (2016). "The Role of Analysts' Forecasts in the Momentum Effect." International Review of Financial Analysis, 48: 67–84

Maddala, G. S. (1986). “Disequilibrium, Self-Selection, and Switching Models,” Handbook of Econometrics, Chapter 28 in Z. Griliches & M. D. Intriligator (eds.), Handbook of Econometrics, Volume 3, Amsterdam: North-Holland.

Maheu, John M., and Thomas H. McCurdy (2000). “Identifying Bull and Bear Markets in Stock Returns,” Journal of Business & Economic Statistics, 18, 100–112.

Malkiel, Burton G. Malkiel (2015). A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing (Eleventh Edition), W. W. Norton & Company

McKinsey & Company (2009). “McKinsey Global Survey Results: Valuing corporate social responsibility.”

McKinsey & Company (2020). “The ESG premium: New perspectives on value and performance.”

McKinsey & Company (2020). Valuation: Measuring and Managing the Value of Companies (7th edition), Wiley & Sons.

Mozaffar Khan, George Serafeim, and Aaron Yoon, “Corporate sustainability: First evidence on materiality,” The Accounting Review, November 2016, Volume 91, Number 6, pp. 1697–724.
Nagy, Kassam and Lee (2015), “Can ESG Add Alpha? An Analysis of ESG Tilt and Momentum Strategies,” whitepaper, MSCI.

Shiller, Robert. (2003), “From Efficient Markets Theory to Behavioral Finance,” Journal of Economic Perspectives, 17(1), pp. 83-104.

Shiller, Robert (2005), Irrational Exuberance, New York, NY: Princeton University Press.

Sara A. Lundqvist and Anders Vilhelmsson, “Enterprise risk management and default risk: Evidence from the banking industry,” Journal of Risk and Insurance, March 2018, Volume 85, Number 1, pp. 127–57.

Smith, Daniel R. (2008), “Evaluating Specification Tests for Markov-switching Time-series Models,” Journal of Time Series Analysis, 29, 629–652.

Meir Statman & Denys Glushkov (2009), “The Wages of Social Responsibility,” Financial Analysts Journal, 65:4, 33-46

Sung C. Bae, Kiyoung Chang, and Ha-Chin Yi, “The impact of corporate social responsibility activities on corporate financing: A case of bank loan covenants,” Applied Economics Letters, February 2016, Volume 23, Number 17, pp. 1234–37,

Sung C. Bae, Kiyoung Chang, and Ha-Chin Yi, “Corporate social responsibility, credit rating, and private debt contracting: New evidence from syndicated loan market,” Review of Quantitative Finance and Accounting, January 2018, Volume 50, Number 1, pp. 261–99.

Witold J. Henisz and James McGlinch, “ESG, material credit events, and credit risk,” Journal of Applied Corporate Finance, July 2019, Volume 31, pp. 105–17.

Zoltán Nagy, Altaf Kassam, and Linda-Eling Lee, “Can ESG add alpha? An analysis of ESG tilt and momentum strategies,” Journal of Investing, Summer 2015, Volume 25, Number 2, pp. 113–24
Description: 博士
Source URI: http://thesis.lib.nccu.edu.tw/record/#G0098265509
Data Type: thesis
Appears in Collections:[International Program in Asia-Pacific Studies (IMAS/IDAS)] 學位論文

Files in This Item:

File Description SizeFormat
550901.pdf4480KbAdobe PDF0View/Open

All items in 學術集成 are protected by copyright, with all rights reserved.

社群 sharing