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Historical high and stock index returns: Application of the regression kink model

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  • Chang, Shu-Lien
  • Chien, Cheng-Yi
  • Lee, Hsiu-Chuan
  • Lin, Ching

Abstract

Motivated by Kahneman and Tversky (1979) and Yuan (2015), this paper investigates whether the influence of the historical high ratio on subsequent stock index returns varies with the distance of the current stock index from its historical high. To explore this issue, a regression kink model with an unknown threshold proposed by Hansen (2017) is used for analysis. Using data from visible stock indices for G7 countries, our empirical evidence shows the presence of threshold effects for most of the G7 stock indices. Moreover, the evidence indicates that the historical high ratio has a strong negative effect on subsequent stock index returns, as the current stock index price is far from its historical high. Overall, these findings support the prospect theory developed by Kahneman and Tversky (1979), in that investors are instinctively risk-seeking (averse) when they face a loss (gain).

Suggested Citation

  • Chang, Shu-Lien & Chien, Cheng-Yi & Lee, Hsiu-Chuan & Lin, Ching, 2018. "Historical high and stock index returns: Application of the regression kink model," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 48-63.
    • Handle: RePEc:eee:intfin:v:52:y:2018:i:c:p:48-63
    DOI: 10.1016/j.intfin.2017.08.004
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    References listed on IDEAS

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    Cited by:

    1. Hsiu-Chuan Lee & Donald Lien & Her-Jiun Sheu, 2023. "Hedging performance of volatility index futures: a partial cointegration approach," Review of Quantitative Finance and Accounting, Springer, vol. 61(1), pages 265-294, July.
    2. Li Shuangjie & Hu Xuefeng & Wang Liming, 2020. "Could the Stock Market Adjust Itself? An Empirical Study Based on Mean Reversion Theory," Journal of Systems Science and Information, De Gruyter, vol. 8(2), pages 97-115, April.
    3. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari, 2021. "Trimmed fuzzy clustering of financial time series based on dynamic time warping," Annals of Operations Research, Springer, vol. 299(1), pages 1379-1395, April.

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