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Mood and Judgment of Subjective Probabilities: Evidence from the U.S. Index Option Market

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  • Doron Kliger
  • Ori Levy

Abstract

Numerous psychological studies show that weather conditions affect people's mood and that mood states are correlated with people's subjective evaluation of future probabilities. In this paper, a new approach is developed and asset market data are employed to test the mood-subjective probability relation. Cloud cover and precipitation volume serve as two mood proxies. Our statistical analysis suggests that bad mood states are characterized by investors placing higher probabilities on adverse events. JEL classification codes: D81.

Suggested Citation

  • Doron Kliger & Ori Levy, 2003. "Mood and Judgment of Subjective Probabilities: Evidence from the U.S. Index Option Market," Review of Finance, European Finance Association, vol. 7(2), pages 235-248.
    • Handle: RePEc:oup:revfin:v:7:y:2003:i:2:p:235-248.
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    File URL: http://hdl.handle.net/10.1023/A:1024555107167
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    Citations

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    Cited by:

    1. Nicholas Apergis & Alexandros Gabrielsen & Lee Smales, 2016. "(Unusual) weather and stock returns—I am not in the mood for mood: further evidence from international markets," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 30(1), pages 63-94, February.
    2. Apergis, Nicholas & Gupta, Rangan, 2017. "Can (unusual) weather conditions in New York predict South African stock returns?," Research in International Business and Finance, Elsevier, vol. 41(C), pages 377-386.
    3. Liang, Chao & Xia, Zhenglan & Lai, Xiaodong & Wang, Lu, 2022. "Natural gas volatility prediction: Fresh evidence from extreme weather and extended GARCH-MIDAS-ES model," Energy Economics, Elsevier, vol. 116(C).
    4. Kliger, Doron & Qadan, Mahmoud, 2019. "The High Holidays: Psychological mechanisms of honesty in real-life financial decisions," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 78(C), pages 121-137.
    5. Gelman, Sergey & Kliger, Doron, 2016. "Time-Induced Stress Effect on Financial Decision Making in Real Markets: The Case of Traffic Congestion," VfS Annual Conference 2016 (Augsburg): Demographic Change 145915, Verein für Socialpolitik / German Economic Association.
    6. Qadan, Mahmoud & Kliger, Doron, 2016. "The short trading day anomaly," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 62-80.
    7. Abudy, Menachem (Meni) & Mugerman, Yevgeny & Shust, Efrat, 2022. "The Winner Takes It All: Investor Sentiment and the Eurovision Song Contest," Journal of Banking & Finance, Elsevier, vol. 137(C).
    8. Daglis, Theodoros & Konstantakis, Konstantinos N. & Michaelides, Panayotis G. & Papadakis, Theodoulos Eleftherios, 2020. "The forecasting ability of solar and space weather data on NASDAQ’s finance sector price index volatility," Research in International Business and Finance, Elsevier, vol. 52(C).
    9. Yang, Bi & Mattila, Anna S., 2020. "Consumer responses to savings message framing," Annals of Tourism Research, Elsevier, vol. 84(C).
    10. Nicholas Apergis & Rangan Gupta, 2016. "Can Weather Conditions in New York Predict South African Stock Returns?," Working Papers 201634, University of Pretoria, Department of Economics.
    11. Kliger, Doron & Gilad, Dalia, 2012. "Red light, green light: Color priming in financial decisions," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 41(5), pages 738-745.
    12. Kliger, Doron & Kudryavtsev, Andrey, 2013. "Volatility expectations and the reaction to analyst recommendations," Journal of Economic Psychology, Elsevier, vol. 37(C), pages 1-6.
    13. Symeonidis, Lazaros & Daskalakis, George & Markellos, Raphael N., 2010. "Does the weather affect stock market volatility?," Finance Research Letters, Elsevier, vol. 7(4), pages 214-223, December.
    14. Muhammad Fayyaz Sheikh & Syed Zulfiqar Ali Shah & Shahid Mahmood, 2017. "Weather Effects on Stock Returns and Volatility in South Asian Markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 24(2), pages 75-107, June.

    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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