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Good air quality and stock market returns

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  • Su, Yuandong
  • Lu, Xinjie
  • Zeng, Qing
  • Huang, Dengshi

Abstract

This paper examines whether an extreme good air quality index (GAQI) is the superior predictor of stock market returns in China based on ordinary least squares method. This GAQI index is constructed based on data series from China Stock Market & Accounting Research Database. The results demonstrate that good air quality can increase stock market returns’ forecasting accuracy more than most popular variables, thereby confirming the prediction validity of the GAQI. The GAQI further exhibits superior portfolio performance when considering different risk appetites and transaction costs, thereby revealing that risk-seeking investors use GAQI information to obtain better portfolio performance over risk-averse investors. The findings offer new insights for stock market returns’ prediction based on air quality on the condition that air quality is undeniably a sharp focus in society.

Suggested Citation

  • Su, Yuandong & Lu, Xinjie & Zeng, Qing & Huang, Dengshi, 2022. "Good air quality and stock market returns," Research in International Business and Finance, Elsevier, vol. 62(C).
  • Handle: RePEc:eee:riibaf:v:62:y:2022:i:c:s0275531922001118
    DOI: 10.1016/j.ribaf.2022.101723
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    as
    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. James Archsmith & Anthony Heyes & Soodeh Saberian, 2018. "Air Quality and Error Quantity: Pollution and Performance in a High-Skilled, Quality-Focused Occupation," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 5(4), pages 827-863.
    3. Wu, Qinqin & Chou, Robin K. & Lu, Jing, 2020. "How does air pollution-induced fund-manager mood affect stock markets in China?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    4. Wu, Qinqin & Hao, Ying & Lu, Jing, 2018. "Air pollution, stock returns, and trading activities in China," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 342-365.
    5. William N. Goetzmann & Dasol Kim & Alok Kumar & Qin Wang, 2015. "Weather-Induced Mood, Institutional Investors, and Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 73-111.
    6. Dong, Rui & Fisman, Raymond & Wang, Yongxiang & Xu, Nianhang, 2021. "Air pollution, affect, and forecasting bias: Evidence from Chinese financial analysts," Journal of Financial Economics, Elsevier, vol. 139(3), pages 971-984.
    7. Suk Joon Byun & Bart Frijns & Tai‐Yong Roh, 2018. "A comprehensive look at the return predictability of variance risk premia," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(4), pages 425-445, April.
    8. Jiekun Huang & Nianhang Xu & Honghai Yu, 2020. "Pollution and Performance: Do Investors Make Worse Trades on Hazy Days?," Management Science, INFORMS, vol. 66(10), pages 4455-4476, October.
    9. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    10. Liang, Chao & Li, Yan & Ma, Feng & Wei, Yu, 2021. "Global equity market volatilities forecasting: A comparison of leverage effects, jumps, and overnight information," International Review of Financial Analysis, Elsevier, vol. 75(C).
    11. Wu, Qinin & Lu, Jing, 2020. "Air pollution, individual investors, and stock pricing in China," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 267-287.
    12. Dashan Huang & Fuwei Jiang & Jun Tu & Guofu Zhou, 2015. "Investor Sentiment Aligned: A Powerful Predictor of Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 791-837.
    13. David Hirshleifer & Tyler Shumway, 2003. "Good Day Sunshine: Stock Returns and the Weather," Journal of Finance, American Finance Association, vol. 58(3), pages 1009-1032, June.
    14. Anthony Heyes & Matthew Neidell & Soodeh Saberian, 2016. "The Effect of Air Pollution on Investor Behavior: Evidence from the S&P 500," NBER Working Papers 22753, National Bureau of Economic Research, Inc.
    15. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    16. Shuai Chen & Paulina Oliva & Peng Zhang, 2018. "Air Pollution and Mental Health: Evidence from China," NBER Working Papers 24686, National Bureau of Economic Research, Inc.
    17. Liang, Chao & Ma, Feng & Li, Ziyang & Li, Yan, 2020. "Which types of commodity price information are more useful for predicting US stock market volatility?," Economic Modelling, Elsevier, vol. 93(C), pages 642-650.
    18. Ma, Feng & Wang, Ruoxin & Lu, Xinjie & Wahab, M.I.M., 2021. "A comprehensive look at stock return predictability by oil prices using economic constraint approaches," International Review of Financial Analysis, Elsevier, vol. 78(C).
    19. Yihao Zhang & Yu Jiang & Yongji Guo, 2017. "The effects of haze pollution on stock performances: evidence from China," Applied Economics, Taylor & Francis Journals, vol. 49(23), pages 2226-2237, May.
    20. Liang, Chao & Tang, Linchun & Li, Yan & Wei, Yu, 2020. "Which sentiment index is more informative to forecast stock market volatility? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 71(C).
    21. Q. Li & C.H. Peng, 2016. "The stock market effect of air pollution: evidence from China," Applied Economics, Taylor & Francis Journals, vol. 48(36), pages 3442-3461, August.
    22. Bortot, P. & Coles, S.G. & Sisson, S.A., 2007. "Inference for Stereological Extremes," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 84-92, March.
    23. Li, Yan & Liang, Chao & Ma, Feng & Wang, Jiqian, 2020. "The role of the IDEMV in predicting European stock market volatility during the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 36(C).
    24. Newey, Whitney K & West, Kenneth D, 1987. "Hypothesis Testing with Efficient Method of Moments Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 28(3), pages 777-787, October.
    25. Liu, Fengqi & Kang, Yuxin & Guo, Kun & Sun, Xiaolei, 2021. "The relationship between air pollution, investor attention and stock prices: Evidence from new energy and polluting sectors," Energy Policy, Elsevier, vol. 156(C).
    26. Ender Demir & Oguz Ersan, 2016. "When Stock Market Investors Breathe Polluted Air," Eurasian Studies in Business and Economics, in: Mehmet Huseyin Bilgin & Hakan Danis (ed.), Entrepreneurship, Business and Economics - Vol. 2, edition 1, pages 705-720, Springer.
    27. Brian M. Lucey & Michael Dowling, 2005. "The Role of Feelings in Investor Decision‐Making," Journal of Economic Surveys, Wiley Blackwell, vol. 19(2), pages 211-237, April.
    28. Lepori, Gabriele M., 2016. "Air pollution and stock returns: Evidence from a natural experiment," Journal of Empirical Finance, Elsevier, vol. 35(C), pages 25-42.
    29. Li, Jennifer (Jie) & Massa, Massimo & Zhang, Hong & Zhang, Jian, 2021. "Air pollution, behavioral bias, and the disposition effect in China," Journal of Financial Economics, Elsevier, vol. 142(2), pages 641-673.
    30. Wang, Lu & Ma, Feng & Niu, Tianjiao & He, Chengting, 2020. "Crude oil and BRICS stock markets under extreme shocks: New evidence," Economic Modelling, Elsevier, vol. 86(C), pages 54-68.
    31. Stephen Keef & Melvin Roush, 2007. "Daily weather effects on the returns of Australian stock indices," Applied Financial Economics, Taylor & Francis Journals, vol. 17(3), pages 173-184.
    32. Lu, Jing & Chou, Robin K., 2012. "Does the weather have impacts on returns and trading activities in order-driven stock markets? Evidence from China," Journal of Empirical Finance, Elsevier, vol. 19(1), pages 79-93.
    33. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    34. Hong, Yongmiao & Liu, Yanhui & Wang, Shouyang, 2009. "Granger causality in risk and detection of extreme risk spillover between financial markets," Journal of Econometrics, Elsevier, vol. 150(2), pages 271-287, June.
    35. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2018. "Forecasting the prices of crude oil using the predictor, economic and combined constraints," Economic Modelling, Elsevier, vol. 75(C), pages 237-245.
    36. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    37. Wang, Yudong & Pan, Zhiyuan & Liu, Li & Wu, Chongfeng, 2019. "Oil price increases and the predictability of equity premium," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 43-58.
    38. Zhang, Yaojie & Ma, Feng & Shi, Benshan & Huang, Dengshi, 2018. "Forecasting the prices of crude oil: An iterated combination approach," Energy Economics, Elsevier, vol. 70(C), pages 472-483.
    39. Levy, Tamir & Yagil, Joseph, 2011. "Air pollution and stock returns in the US," Journal of Economic Psychology, Elsevier, vol. 32(3), pages 374-383, June.
    40. Xu, Minya & Wang, Yaqiong & Tu, Yundong, 2021. "Uncovering the invisible effect of air pollution on stock returns: A moderation and mediation analysis," Finance Research Letters, Elsevier, vol. 39(C).
    41. Anderson, Robert M. & Bianchi, Stephen W. & Goldberg, Lisa R., 2012. "Will My Risk Parity Strategy Outperform?," Department of Economics, Working Paper Series qt23t2s950, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    42. Lin, Qi, 2018. "Technical analysis and stock return predictability: An aligned approach," Journal of Financial Markets, Elsevier, vol. 38(C), pages 103-123.
    43. Heyes, Anthony & Zhu, Mingying, 2019. "Air pollution as a cause of sleeplessness: Social media evidence from a panel of Chinese cities," Journal of Environmental Economics and Management, Elsevier, vol. 98(C).
    44. Ding, Xiaoya & Guo, Mengmeng & Yang, Tao, 2021. "Air pollution, local bias, and stock returns," Finance Research Letters, Elsevier, vol. 39(C).
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    More about this item

    Keywords

    Extreme air quality; Stock returns; Macroeconomic variables; Risk appetites; Trading cost;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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