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The effectiveness of the sunshine effect in Taiwan's stock market before and after the 1997 financial crisis

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  • Lee, Yuan-Ming
  • Wang, Kuan-Min

Abstract

This study constructs a variety of GARCH models with the consideration of the generalized error distribution to analyze the relationship between the cloud cover and stock returns in Taiwan in the whole sample period (1986 to 2007) and in the two sub-sample periods (1986 to 1996 and 1997 to 2007). The data include Taiwan Stock Exchange Capitalization Weighted Stock Index, the primary eight stock sector indices, and the U.S. Dow Jones Industrial Average index to proxy the impact of U.S. stock market on Taiwan's stock market performance. The empirical finding of this study could be used to reconfirm the existence of the so-called sunshine effect. In addition, by comparing the long-run impulse multiplier effects of the cloud cover on the stock return in the two sub-sample periods; this study could examine the transition of the sunshine effect in Taiwan's stock market. The empirical results suggest that cloud cover has a significant negative impact on Taiwan's stock market, especially in the low cloud cover periods. Moreover, the pre-determined distribution of the error term plays an important role on the significance of the sunshine effect. The empirical result shows that most long-run multipliers are negative and the multiplier is more effective in the low cloud cover periods than in the high cloud cover periods.

Suggested Citation

  • Lee, Yuan-Ming & Wang, Kuan-Min, 2011. "The effectiveness of the sunshine effect in Taiwan's stock market before and after the 1997 financial crisis," Economic Modelling, Elsevier, vol. 28(1-2), pages 710-727, January.
    • Handle: RePEc:eee:ecmode:v:28:y:2011:i:1-2:p:710-727
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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. Kim, Jae H., 2017. "Stock returns and investors' mood: Good day sunshine or spurious correlation?," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 94-103.
    3. 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.
    4. 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).
    5. Waldemar Tarczyński & Urszula Mentel & Grzegorz Mentel & Umer Shahzad, 2021. "The Influence of Investors’ Mood on the Stock Prices: Evidence from Energy Firms in Warsaw Stock Exchange, Poland," Energies, MDPI, vol. 14(21), pages 1-25, November.
    6. 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.

    More about this item

    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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