Momentum and contrarian effects in the Ukrainian stock market: case of daily overreactions
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Author(s)Alex PlastunLink to Linkedin: https://www.linkedin.com/pub/plastun-alexey/57/7bb/444/Link to ORCID Index: https://orcid.org/0000-0001-8208-7135
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Nataliya StrochenkoLink to ORCID Index: https://orcid.org/0000-0002-4721-9523
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Olga ZhmaylovaLink to ORCID Index: https://orcid.org/0000-0002-1157-1453
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Liudmyla SliusarevaLink to ORCID Index: https://orcid.org/0000-0002-6845-9307
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Sergiy BashlayLink to ORCID Index: https://orcid.org/0000-0002-2247-5440
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DOIhttp://dx.doi.org/10.21511/imfi.17(1).2020.03
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Article InfoVolume 17 2020, Issue #1, pp. 24-34
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- 1004 Views
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129 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
This paper examines momentum and contrarian effects in the Ukrainian stock market after one-day abnormal returns. To do this, UX futures data over the period 2010–2018 are used. The following hypotheses are tested: H1) hourly returns on overreaction days differ from hourly returns on normal days, H2) there are price patterns on overreaction days, and H3) to test these hypotheses, visual inspection and average analysis are used, as well as t-tests, cumulative abnormal returns, and trading simulation approaches. The results suggest that there are statistically significant differences between intraday dynamics during the usual days and the overreactions day. There is a strong momentum effect present on the day of overreaction: prices tend to change only in the direction of the overreaction during the whole day. The fact of the overreaction becomes clear after 13:00-14:00. This gives a lot of time to explore the momentum effect in the day of overreaction. On the day after the overreaction, prices tend to go in the opposite direction: contrarian pattern is detected, which is in line with the overreaction hypothesis. Based on detected price patterns, rules of trading and trading strategies for the Ukrainian stock market are developed. Momentum Strategy (based on price patterns on the day of overreaction) generates several successful trades; close to with 90%, and their number being is profitable (trading results differ from the random ones – confirmed by t-tests). Contrarian Strategy (based on price patterns on the day after the overreaction) demonstrates low efficiency, and results do not differ from random trading.
- Keywords
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JEL Classification (Paper profile tab)G12, G17, C63
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References28
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Tables8
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Figures6
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- Figure 1. Average hourly returns on overreaction and normal days: the case of positive overreactions, UX-C
- Figure 2. Average hourly returns on overreaction and normal days: the case of negative overreactions, UX-C
- Figure 3. Dynamics of cumulative abnormal returns: case of UX-C
- Figure 4. Average hourly returns on the day after the overreaction and normal days: case of positive overreactions, UX-C
- Figure 5. Average hourly returns on the day after the overreaction and normal days: the case of negative overreactions, UX-C
- Figure 6. Dynamics of cumulative abnormal returns, UX-C
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- Table 1. T-test of hourly returns on overreaction and normal days: the case of positive overreactions, UX-C
- Table 2. T-test of hourly returns on overreaction and normal days: the case of negative overreactions, UX-C
- Table 3. Cumulative abnormal returns: case of positive and negative overreactions, UX-C
- Table 4. Trading simulation results for the day of overreaction: case of the Momentum Strategy
- Table 5. T-test of hourly returns on the day after the overreaction and normal days: case of positive overreactions, UX-C
- Table 6. T-test of hourly returns on the day after the overreaction and normal days: case of negative overreactions, UX-C
- Table 7. Cumulative abnormal returns: case of positive and negative overreactions, UX-C
- Table 8. Trading simulation results for the day after the overreaction: case of the Contrarian Strategy
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Force majeure events and stock market reactions in Ukraine
Guglielmo Maria Caporale , Alex Plastun ,
Inna Makarenko
doi: http://dx.doi.org/10.21511/imfi.16(1).2019.26
Investment Management and Financial Innovations Volume 16, 2019 Issue #1 pp. 334-345 Views: 1424 Downloads: 137 TO CITE АНОТАЦІЯThis paper examines reactions in the Ukrainian stock market to force majeure events, which are divided into four groups: economic force majeure, social force majeure, terrorist acts, natural and technological disasters. More specifically, using daily data for the main Ukrainian stock market index (namely PFTS) over the period from January 1, to December 31, 2018 this study investigates whether or not force majeure events create (temporary) inefficiencies and there exist profitable trading strategies based on exploiting them. For this purpose, cumulative abnormal returns and trading simulation approaches are used in addition to Student’s t-tests. The results suggest that the Ukrainian stock market absorbs new information rather fast. Negative returns in most cases are observed only on the day of the event. The only exception is technological disasters, the market needing up to ten days to react fully in this case. Despite the presence of a detectable pattern in price behavior after force majeure events (namely, a price decrease on the day of the event) no profitable trading strategies based on it are found as their outcomes do not differ from those generated by random trading.
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Competitiveness in the Ukrainian stock market and local crisis of 2013–2015
Alex Plastun ,
Inna Makarenko ,
Ievgen Balatskyi
doi: http://dx.doi.org/10.21511/imfi.15(2).2018.03
Investment Management and Financial Innovations Volume 15, 2018 Issue #2 pp. 29-39 Views: 1335 Downloads: 173 TO CITE АНОТАЦІЯThis paper investigates competitiveness in the Ukrainian stock market during local crisis of 2013–2015. The following hypothesis is tested: crisis decreases competitiveness in the stock market. The analysis is carried out for the most liquid stocks in the Ukrainian Exchange (UX) over the period from 2010 to 2017 using both traditional measurements of market concentration (Hirschman Index, Lerner Index, Comprehensive Concentration Index, Entropy Index, Gini coefficient, etc.) and some alternative methods like regression analysis with dummy variables and Kruskal-Wallis test. The results suggest that the current degradation of the Ukrainian stock market is closely related with significant changes in the market concentration which are caused by the local crisis.
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Spatial tale of G-7 and BRICS stock markets during COVID-19: An event study
Sanket Ledwani ,
Suman Chakraborty ,
Sandeep S. Shenoy
doi: http://dx.doi.org/10.21511/imfi.18(2).2021.03
Investment Management and Financial Innovations Volume 18, 2021 Issue #2 pp. 20-36 Views: 1184 Downloads: 496 TO CITE АНОТАЦІЯThe unprecedented outbreak of COVID-19 has affected every aspect of the human life, be it health, social, or economic dimensions. The anxiety and uncertainty wobbled the economies of affected countries worldwide. This study attempts to quantify the impact of COVID-19 on the performance of major stock markets of G-7 nations vis-à-vis BRICS nations. An event study methodology is employed to capture the effect of the systematic event in the form of Buy and Hold Abnormal Returns (BHAR) and Average Buy and Hold Abnormal Returns (ABHAR). The study considers a 90-day observation window, consisting of six sub-event windows after the COVID-19 news up-doves the world, and 120 days prior to the selected event date to estimate average expected returns. BHAR values in the four event windows are statistically significant, covering stock markets from panic and nosedive to their correction and recovery. ABHAR values reported are significantly negative in the event window ranging from –0.15% to –38.43% for G-7 and –0.06% to –37.12% for BRICS nations. Despite similar ABHAR trends, the BHAR values and correlation matrix exhibit a diverse reaction in BRICS nations compared to the highly synchronized reaction in the G-7 group of nations in the COVID period.
