Investors’ Weather-Related Moods and the Stock Market
Article Sidebar
Published:
Dec 6, 2018
Keywords:
principal component; return behavior; sentiment index; weather effects
Main Article Content
Abstract
To test for weather effects on stock returns and volatility, weather-related mood indexes were constructed from the principal components (PCs) of weather variables to obtain clean measures of investors’ moods. Using the daily data on the Stock Exchange of Thailand portfolio index and Bangkok weather variables from February 17, 1992, to December 30, 2016, significant weather effects were found on both stock returns and volatility. Returns were driven by the rainfall-related, fourth PC, while the fifth PC—most sensitive to ground visibility and wind speed—drove volatility. However, the first PC was not significant.
Article Details
Section
Research Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright: Asia-Pacific International University reserve exclusive rights to publish, reproduce and distribute the manuscript and all contents therein.
References
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspective, 21(2), 129‒151.
Beer, F., & Zouaoui, M. (2013). Measuring stock market investor sentiment. Journal of Applied Business Research, 29(1), 51‒68
Brown, G., & Cliff, M. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1‒27.
Charry, J., & Hawkinshire, F. (1981). Effects of atmospheric electricity on some substrates of disordered social behavior. Journal of Personality and Social Psychology, 41(1), 185‒197.
Chen, H., Leung, T., & She, Y. (2014). A principal component approach to measuring investor sentiment in China. Quantitative Finance, 14(4), 573‒579.
Finter, P., Ruenzi, A., & Ruenzi, S. (2012). The impact of investor sentiment on the German stock market. Zeitschrift für Betriebswirtschaft, 82(2), 133‒163.
Furhwirth, M., & Sogner, L. (2015). Weather and SAD related effects on the financial market. Quarterly Review of Economics and Finance, 57(August), 11‒31.
Hansen, L. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50(3), 1029–1054.
Hirshleifer, D., & Shumway, T. (2003). Good day sunshine: Stock returns and the weather. Journal of Finance, 58(3), 1009‒1032.
Howarth, E., & Hoffman M. (1984). A multidimensional approach to the relationship between mood and weather. British Journal of Psychology, 75(1), 15‒23.
Khanthavit, A. (2017). Instrumental-variable estimation of Bangkok-weather effects in the Stock Exchange of Thailand. Asian Academy of Management Journal of Accounting and Finance, 13(1), 83–111.
Lee, W., Jiang, C., & Indro, D. (2002). Stock market volatility, excess returns, and the role of investor sentiment. Journal of Banking and Finance, 26(12), 2277–2299.
Loewenstein, G. (2000). Emotions in economic theory and economic behavior. American Economic Review, 90(2), 426‒432
Mehra, R., & Sah, R. (2002). Mood fluctuations, projection bias, and volatility of equity prices. Journal of Economic Dynamics and Control, 26(5), 869‒887.
Newey, W., & West, K. (1994). Automatic lag selection in covariance matrix estimation. Review of Economic Studies, 61(4), 631–653.
Ocampo, S., & Rodriguez, N. (2012). An introductory review of a structural VAR-X estimation and applications. Revista Colombianna de Estadistica, 35(3), 479‒508.
Pal, M. (1980). Consistent moment estimators of regression coefficients in the presence of errors in variables. Journal of Econometrics, 14(3), 349–364.
Racicot, F., & Theoret, R. (2010). Optimal instrumental variables generators based on improved Hausman regression, with an application to hedge fund returns. Journal of Wealth Management, 13(1), 103–123.
Rogers, L., & Satchell, S. (1991). Estimating variance from high, low, and closing prices. Annals of Applied Probability, 1(4), 504–512.
Shalen, C. (1993). Volume, volatility, and the dispersion of beliefs. Review of Financial Studies, 6(2), 405–434.
Stambaugh, R., Yu, J., & Yuan Y. (2012). The short of it: Investor sentiment and anomalies. Journal of Financial Economics, 104(2), 288–302.
Statman, M., Thorley, S., & Vorkink, K. (2006). Investor overconfidence and trading volume. Review of Financial Studies, 19(4), 1531–1565.
Vlady, S. and Tufan, E. (2011). Causality of weather conditions in Australian stock equity returns. Young Economists Journal, 9(17), 184–197.
Watson, D. (2000). Mood and Temperament. London: Guilford Press.
Wurtman, R., & Wurtman, J. (1989). Carbohydrates and depression. Scientific American, 260(1), 68-75.
Zivot, E., and Wang, J. (2006). Vector Autoregressive Models for Multivariate Time Series: Modeling Financial Time Series with S-Plus®, 2nd ed., New York, Springer.
Beer, F., & Zouaoui, M. (2013). Measuring stock market investor sentiment. Journal of Applied Business Research, 29(1), 51‒68
Brown, G., & Cliff, M. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1‒27.
Charry, J., & Hawkinshire, F. (1981). Effects of atmospheric electricity on some substrates of disordered social behavior. Journal of Personality and Social Psychology, 41(1), 185‒197.
Chen, H., Leung, T., & She, Y. (2014). A principal component approach to measuring investor sentiment in China. Quantitative Finance, 14(4), 573‒579.
Finter, P., Ruenzi, A., & Ruenzi, S. (2012). The impact of investor sentiment on the German stock market. Zeitschrift für Betriebswirtschaft, 82(2), 133‒163.
Furhwirth, M., & Sogner, L. (2015). Weather and SAD related effects on the financial market. Quarterly Review of Economics and Finance, 57(August), 11‒31.
Hansen, L. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50(3), 1029–1054.
Hirshleifer, D., & Shumway, T. (2003). Good day sunshine: Stock returns and the weather. Journal of Finance, 58(3), 1009‒1032.
Howarth, E., & Hoffman M. (1984). A multidimensional approach to the relationship between mood and weather. British Journal of Psychology, 75(1), 15‒23.
Khanthavit, A. (2017). Instrumental-variable estimation of Bangkok-weather effects in the Stock Exchange of Thailand. Asian Academy of Management Journal of Accounting and Finance, 13(1), 83–111.
Lee, W., Jiang, C., & Indro, D. (2002). Stock market volatility, excess returns, and the role of investor sentiment. Journal of Banking and Finance, 26(12), 2277–2299.
Loewenstein, G. (2000). Emotions in economic theory and economic behavior. American Economic Review, 90(2), 426‒432
Mehra, R., & Sah, R. (2002). Mood fluctuations, projection bias, and volatility of equity prices. Journal of Economic Dynamics and Control, 26(5), 869‒887.
Newey, W., & West, K. (1994). Automatic lag selection in covariance matrix estimation. Review of Economic Studies, 61(4), 631–653.
Ocampo, S., & Rodriguez, N. (2012). An introductory review of a structural VAR-X estimation and applications. Revista Colombianna de Estadistica, 35(3), 479‒508.
Pal, M. (1980). Consistent moment estimators of regression coefficients in the presence of errors in variables. Journal of Econometrics, 14(3), 349–364.
Racicot, F., & Theoret, R. (2010). Optimal instrumental variables generators based on improved Hausman regression, with an application to hedge fund returns. Journal of Wealth Management, 13(1), 103–123.
Rogers, L., & Satchell, S. (1991). Estimating variance from high, low, and closing prices. Annals of Applied Probability, 1(4), 504–512.
Shalen, C. (1993). Volume, volatility, and the dispersion of beliefs. Review of Financial Studies, 6(2), 405–434.
Stambaugh, R., Yu, J., & Yuan Y. (2012). The short of it: Investor sentiment and anomalies. Journal of Financial Economics, 104(2), 288–302.
Statman, M., Thorley, S., & Vorkink, K. (2006). Investor overconfidence and trading volume. Review of Financial Studies, 19(4), 1531–1565.
Vlady, S. and Tufan, E. (2011). Causality of weather conditions in Australian stock equity returns. Young Economists Journal, 9(17), 184–197.
Watson, D. (2000). Mood and Temperament. London: Guilford Press.
Wurtman, R., & Wurtman, J. (1989). Carbohydrates and depression. Scientific American, 260(1), 68-75.
Zivot, E., and Wang, J. (2006). Vector Autoregressive Models for Multivariate Time Series: Modeling Financial Time Series with S-Plus®, 2nd ed., New York, Springer.