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Stock Returns and Investors’ Mood: Good Day Sunshine or Spurious Correlation?

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  • Kim, Jae

Abstract

This paper examines the validity of statistical significance reported in the seminal studies of the weather effect on stock return. It is found that their research design is statistically flawed and seriously biased against the null hypothesis of no effect. This, coupled with the test statistics inflated by massive sample sizes, strongly suggests that the statistical significance is spurious as an outcome of Type I error. The alternatives to the p-value criterion for statistical significance soundly support the null hypothesis of no weather effect. As an application, the effect of daily sunspot numbers on stock return is examined. Under the same research design as that of a seminal study, the number of sunspots is found to be highly statistically significant although its economic impact on stock return is negligible.

Suggested Citation

  • Kim, Jae, 2016. "Stock Returns and Investors’ Mood: Good Day Sunshine or Spurious Correlation?," MPRA Paper 70692, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:70692
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    2. Wang, Hanjie & Feil, Jan-Henning & Yu, Xiaohua, 2021. "Disagreement on sunspots and soybeans futures price," Economic Modelling, Elsevier, vol. 95(C), pages 385-393.
    3. Liu, Huajin & Zhang, Wei & Zhang, Xiaotao & Liu, Jia, 2021. "Temperature and trading behaviours," International Review of Financial Analysis, Elsevier, vol. 78(C).
    4. Samahita, Margaret & Holm, Håkan J., 2020. "Mining for Mood Effect in the Field," Working Papers 2020:2, Lund University, Department of Economics.
    5. Lucian Liviu ALBU & Radu LUPU & Adrian Cantemir CĂLIN & Iulia LUPU, 2019. "Nonlinear Modeling of Financial Stability Using Default Probabilities from the Capital Market," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 19-37, March.
    6. Jae H. Kim & Kamran Ahmed & Philip Inyeob Ji, 2018. "Significance Testing in Accounting Research: A Critical Evaluation Based on Evidence," Abacus, Accounting Foundation, University of Sydney, vol. 54(4), pages 524-546, December.
    7. Ngoc Bao Vuong & Yoshihisa Suzuki, 2022. "The Moderating Effect of Market-Specific Factors on the Return Predictability of Investor Sentiment," SAGE Open, , vol. 12(3), pages 21582440221, July.
    8. Zhou Tianbao & Li Xinghao & Zhao Junguang, 2022. "Solar Term Anomaly in China Stock Market: Evidence from Shanghai Index," Papers 2203.12603, arXiv.org, revised Feb 2023.
    9. Jae H. Kim & Andrew P. Robinson, 2019. "Interval-Based Hypothesis Testing and Its Applications to Economics and Finance," Econometrics, MDPI, vol. 7(2), pages 1-22, May.
    10. Polyzos, Stathis & Samitas, Aristeidis & Katsaiti, Marina-Selini, 2020. "Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability," International Review of Financial Analysis, Elsevier, vol. 72(C).

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    More about this item

    Keywords

    Anomaly; Behavioural finance; Data mining; Market efficiency; Sunspot numbers; Type I error; Weather;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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