Exploring frequency of price overreactions in the Ukrainian stock market

  • Received June 30, 2018;
    Accepted August 10, 2018;
    Published August 17, 2018
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/imfi.15(3).2018.13
  • Article Info
    Volume 15 2018, Issue #3, pp. 157-168
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This work is licensed under a Creative Commons Attribution 4.0 International License

This paper explores the frequency of price overreactions in the Ukrainian stock market by focusing on the PFTS Index over the period 2006–2017 and UX index over the period 2008–2017, as well as some “blue chips” (BAVL, UNAF, MSICH, CEEN) for the period of 2013–2015. Using static approach to detect overreactions, a number of hypotheses are tested: the frequency of price overreactions is informative about crisis events in the economy (H1), can be used for price prediction purposes (H2), and exhibits seasonality (H3). To do this, various statistical tests (both parametric and non-parametric), including correlation analysis, augmented Dickey-Fuller tests (ADF), Granger causality tests, and regression analysis with dummy variables, are carried out. Hypotheses H1 and H2 are confirmed: frequency of price overreactions can be used as a crisis predictor (a sharp increase in the number of overreactions is associated with a crisis period) and could be used to predict stock returns. No seasonality in the overreactions frequency is found. Implications of this research include crisis prediction and stock market prices forecasting and can be used for designing trading strategies.

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    • Figure 1. Frequency distribution for the PFTS index
    • Figure 2. Frequency distribution for the UX index
    • Figure 3. Frequency of overreactions: case of the PFTS index for the period 2006–2017 (annual data)
    • Figure 4. Frequency of overreactions: case of the UX index for the period 2008–2017 (annual data)
    • Figure 5. Frequency of overreactions in the PFTS index over the period 2007–2009 (monthly data)
    • Figure 6. Frequency of overreactions in the UX index over the period 2008–2009 (monthly data)
    • Figure 7. Frequency of overreactions, case of the PFTS index over the period 2013–2015 (monthly data)
    • Figure 8. Frequency of overreactions, case of the “blue chips” of the Ukrainian Stock Exchange over the period 2012–2015 (monthly data)
    • Figure 9. The cross-correlation between PFTS index log returns and frequency of overreactions over the total sample period for different lead and lag intervals
    • Figure 10. Monthly seasonality in overreactions frequency: case of PFTS Index
    • Table 1. Frequency analysis of daily price fluctuations of the PFTS and UX indices
    • Table 2. Results of ANOVA and non-parametric Kruskal-Wallis tests for statistical differences in the frequency of overreactions between different years
    • Table 3. Augmented Dickey-Fuller test: PFST index log returns and overreactions frequency data
    • Table 4. Granger Causality Test: Log returns vs overreactions frequency
    • Table 5. Regression analysis results: case of PFST index log returns as a dependent variable
    • Table 6. Additional regression analysis results
    • Table 7. Parametric ANOVA of monthly seasonality in overreactions frequency
    • Table 8. Non-parametric Kruskal-Wallis test of monthly seasonality in overreactions frequency