Видання зареєстровані авторами шляхом самоархівування
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Item Persistence in ESG and conventional stock market indices(Springer Nature, 2022) Caporale, G.M.; Gil-Alana, L.; Пластун, Олексій Леонідович; Пластун, Алексей Леонидович; Plastun, Oleksii Leonidovych; Макаренко, Інна Олександрівна; Макаренко, Инна Александровна; Makarenko, Inna OleksandrivnaThis paper uses R/S (Rescaled Range) analysis and fractional integration techniques to examine the persistence of two sets of 12 ESG (Environmental, Social and Governance) and conventional stock price indices from the MSCI (Morgan Stanley Capital International) database over the period 2007–2020 for a large number of both developed and emerging markets. Both sets of results imply that there are no signifcant diferences between the two types of indices in terms of the degree of persistence and its dynamic behaviour. However, higher persistence is found for the emerging markets examined (especially the BRICS, i.e. Brazil, Russia, India, China and South Africa), which suggests that they are less efcient and thus ofer more opportunities for proftable trading strategies. Possible explanations for these fndings include diferent type of companies’ ‘camoufage’ and ‘washing’ (green, blue, pink, social, and Sustainable Development Goals—SDG) in the presence of rather lax regulations for ESG reporting.Item Bitcoin Returns and the Frequency of Daily Abnormal Returns(Pitt Open Library Publishing, 2021) Caporale, G.M.; Пластун, Олексій Леонідович; Пластун, Алексей Леонидович; Plastun, Oleksii Leonidovych; Олійник, Віктор Михайлович; Олейник, Виктор Михайлович; Oliinyk, Viktor MykhailovychThis paper investigates the relationship between Bitcoin returns and the frequency of daily abnormal returns over the period from June 2013 to February 2020 using a number of regression techniques and model specifications including standard OLS, weighted least squares (WLS), ARMA and ARMAX models, quantile regressions, Logit and Probit regressions, piecewise linear regressions, and non-linear regressions. Both the in-sample and out-of-sample performance of the various models are compared by means of appropriate selection criteria and statistical tests. These suggest that, on the whole, the piecewise linear models are the best, but in terms of forecasting accuracy they are outperformed by a model that combines the top five to produce “consensus” forecasts. The finding that there exist price patterns that can be exploited to predict future price movements and design profitable trading strategies is of interest both to academics (since it represents evidence against the EMH) and to practitioners (who can use this information for their investment decisions).Item The frequency of one-day abnormal returns and price fluctuations in the forex(Routledge on behalf of the Universidad del CEMA, 2021) Caporale, G.M.; Пластун, Олексій Леонідович; Пластун, Алексей Леонидович; Plastun, Oleksii Leonidovych; Олійник, Віктор Михайлович; Олейник, Виктор Михайлович; Oliinyk, Viktor MykhailovychThis paper analyses the explanatory power of the frequency of abnormal returns in the FOREX over the period 1994–2019. The following hypotheses are tested: frequency of abnormal returns is asignificant driver of price movements (H1); it does not exhibit seasonal patterns (H2); it is stable over time (H3). For our purposes avariety of statistical methods are applied including ADF, PP and KPSS tests, Granger causality tests, correlation analysis, regression analysis, Probit and Logit regression models. No evidence is found of either seasonal patterns or instability. However, there appears to be astrong positive (negative) relationship between returns in the FOREX and the frequency of positive (negative) abnormal returns. On the whole, the results suggest that the latter is an important driver of price dynamics in the FOREX, is informative about crises and can be the basis of profitable trading strategies, which is inconsistent with market efficiency.Item Momentum effects in the cryptocurrency market after one-day abnormal returns(Springer, 2020) Caporale, G.M.; Пластун, Олексій Леонідович; Пластун, Алексей Леонидович; Plastun, Oleksii LeonidovychThis paper examines whether there exists a momentum effect after one-day abnormal returns in the cryptocurrency market. For this purpose, a number of hypotheses of interest are tested for the Bitcoin, Ethereum and Litecoin exchange rates vis-à-vis the US dollar over the period 01.01.2015–01.09.2019, specifically whether or not: (H1) the intraday behavior of hourly returns is different on abnormal days compared to normal days; (H2) there is a momentum effect on days with abnormal returns, and (H3) after one-day abnormal returns. The methods used for the analysis include various statistical methods as well as a trading simulation approach. The results suggest that hourly returns during the day of positive/negative abnormal returns are significantly higher/lower than those during the average positive/negative day. The presence of abnormal returns can usually be detected before the day ends by estimating specific timing parameters. Prices tend to move in the direction of the abnormal returns till the end of the day when it occurs, which implies the existence of a momentum effect on that day giving rise to exploitable profit opportunities. This effect (together with profit opportunities) is also observed on the following day. In two cases (BTCUSD positive abnormal returns and ETHUSD negative abnormal returns), a contrarian effect is detected instead.Item Bitcoin fluctuations and the frequency of price overreactions(Springer, 2019) Caporale, G.M.; Пластун, Олексій Леонідович; Пластун, Алексей Леонидович; Plastun, Oleksii Leonidovych; Олійник, Віктор Михайлович; Олейник, Виктор Михайлович; Oliinyk, Viktor MykhailovychThis paper investigates the role of the frequency of price overreactions in the cryptocurrency market in the case of BitCoin over the period 2013–2018. Specifically, it uses a static approach to detect overreactions and then carries out hypothesis testing by means of a variety of statistical methods (both parametric and non-parametric) including ADF tests, Granger causality tests, correlation analysis, regression analysis with dummy variables, ARIMA and ARMAX models, neural net models, and VAR models. Specifically, the hypotheses tested are whether or not the frequency of overreactions (i) is informative about Bitcoin price movements (H1) and (ii) exhibits no seasonality (H2). On the whole, the results suggest that it can provide useful information to predict price dynamics in the cryptocurrency market and for designing trading strategies (H1 cannot be rejected), whilst there is no evidence of seasonality (H2 cannot be rejected).Item Long memory and data frequency in financial markets(Taylor and Francis, 2019) Caporale, G.M.; Gil-Alana, L.; Пластун, Олексій Леонідович; Пластун, Алексей Леонидович; Plastun, Oleksii LeonidovychThis paper investigates persistence in financial time series at three different frequencies (daily, weekly and monthly). The analysis is carried out for various financial markets (stock markets, FOREX, commodity markets) over the period from 2000 to 2016 using two different long memory approaches (R/S analysis and fractional integration) for robustness purposes. The results indicate that persistence is higher at lower frequencies, for both returns and their volatility. This is true of the stock markets (both developed and emerging) and partially of the FOREX and commodity markets examined. Such evidence against the random walk behaviour implies predictability and is inconsistent with the Efficient Market Hypothesis (EMH), since abnormal profits can be made using trading strategies based on trend analysis.Item Calendar anomalies in the Ukrainian stock market(LLC “Consulting Publishing Company “Business Perspectives”, 2017) Пластун, Олексій Леонідович; Пластун, Алексей Леонидович; Plastun, Oleksii Leonidovych; Caporale, G.M.This paper is a comprehensive investigation of calendar anomalies in the Ukrainian stock market. It employs various statistical techniques (average analysis, Student's t-test, ANOVA, the Kruskal-Wallis test, and regression analysis with dummy variables) and a trading simulation approach to test for the presence of the following anomalies: Day of the Week Effect; Turn of the Month Effect; Turn of the Year Effect; Month of the Year Effect; January Effect; Holiday Effect; Halloween Effect. The results suggest that in general calendar anomalies are not present in the Ukrainian stock market, but there are a few exceptions, i.e. the Turn of the Year and Halloween Effect for the PFTS index, and the Month of the Year Effect for UX futures. However, the trading simulation analysis shows that only trading strategies based on the Turn of the Year Effect for the PFTS index and the Month of the Year Effect for the UX futures can generate exploitable profit opportunities that can be interpreted as evidence against market efficiency.Item The weekend effect: an exploitable anomaly in the Ukrainian stock market?(DIW Berlin ; Німецький інститут економічних досліджень, 2015) Пластун, Олексій Леонідович; Пластун, Алексей Леонидович; Plastun, Oleksii Leonidovych; Caporale, G.M.; Gil-Alana, L.This paper provides some new empirical evidence on the weekend effect (one of the best known anomalies in financial markets) in Ukrainian futures prices. The analysis uses various statistical techniques (average analysis, Student's t-test, dummy variables, and fractional integration) to test for the presence of this anomaly, and then a trading simulation approach to establish whether it can be exploited to make extra profits. The statistical evidence points to abnormal positive returns on Fridays, and a trading strategy based on this anomaly is shown to generate annual profits of up to 25%. The implication is that the Ukrainian stock market is inefficient.Item Long-term price overreactions: are markets inefficient?(2015) Caporale, G.M.; Gil-Alana, L.; Пластун, Олексій Леонідович; Пластун, Алексей Леонидович; Plastun, Oleksii LeonidovychThis paper examines long-term price overreactions in various financial markets (commodities, US stock market and FOREX). First, t-tests are carried out for overreactions as a statistical phenomenon. Second, a trading robot approach is applied to test the profitability of two alternative strategies, one based on the classical overreaction anomaly, the other on a so-called “inertia anomaly”. Both weekly and monthly data are used. Evidence of anomalies is found predominantly in the case of weekly data. In the majority of cases strategies based on overreaction anomalies are not profitable, and therefore the latter cannot be seen as inconsistent with the EMH.Item Short-Term Price Overreactions: Identification, Testing, Exploitation(Brunel University, London, 2014) Пластун, Олексій Леонідович; Пластун, Алексей Леонидович; Plastun, Oleksii Leonidovych; Caporale, G.M.; Gil-Alana, L.This paper examines short-term price reactions after one-day abnormal price changes and whether they create exploitable profit opportunities in various financial markets. A t-test confirms the presence of overreactions and also suggests that there is an “inertia anomaly”, i.e. after an overreaction day prices tend to move in the same direction for some time. A trading robot approach is then used to test two trading strategies aimed at exploiting the detected anomalies to make abnormal profits. The results suggest that a strategy based on counter-movements after overreactions does not generate profits in the FOREX and the commodity markets, but it is profitable in the case of the US stock market. By contrast, a strategy exploiting the “inertia anomaly” produces profits in the case of the FOREX and the commodity markets, but not in the case of the US stock market.