Czech Stock Research Articles

VOLATILITY REGIMES OF SELECTED CENTRAL EUROPEAN STOCK RETURNS: A MARKOV SWITCHING GARCH APPROACH

This paper investigates the weekly stock market data of the Hungarian stock index BUX, the Czech stock index PX and the Polish stock index WIG20 spanning from January 7, 2001 to April 18, 2021. The period of more than 20 years enabled to analyse the behaviour of returns and their volatility during both the calm as well as various crises/turmoil periods. Besides the traditional GARCH-type models (GARCH and GJR-GARCH) the two-regime Markov Switching GARCHtype models (MS-GARCH and MS-GJR-GARCH) were estimated in order to examine the volatility switches of the Central European transition stock markets. The t-distribution of error terms was used to capture the dynamics of analysed returns more precisely. The results proved high volatility persistence of individual markets which substantially differed across the both regimes. Furthermore, the GJR-GARCH and MS-GJR-GARCH models clearly confirmed the presence of the leverage effect. Consideration of the MS-GARCH-type models enabled to capture various volatility switches during the analysed period attributable mainly to the global financial crisis 2008–2009, to European debt crisis in 2011 and to the Covid-19 pandemic in 2020. Interesting results were received for the Czech market with the strong leverage effect indicating completely different specification of volatility regimes by the MS-GJR-GARCH model.

STOCK PRICE PREDICTION USING MARKOV CHAINS ANALYSIS WITH VARYING STATE SPACE ON DATA FROM THE CZECH REPUBLIC

The article describes empirical research that deals with short-term stock price prediction. The aim of this study is to use this prediction to create successful business models. A business model that outperforms the stock market, represented by the Buy and Hold strategy, is considered to be successful. A stochastic model based on Markov chains analysis with varying state space is used for short-term stock price prediction. The varying state spate is defined based on multiples of the moving standard deviation. A total of 80 state space models were calculated for the moving standard deviation with 5-step lengths from 10 to 30 in combination with the standard deviation multiples from 0.5 to 2.0 with the step of 0.1. The efficiency of the business models was verified for 3 long-term, liquid stocks of the Czech stock market, namely the stocks of KB, CEZ, and O2 within a 14-year period – from the beginning of 2006 to the end of 2019. Business models perform best when they use a state space defined on the length of a moving standard deviation between 15 and 30 in combination with multiples of the standard deviation between 1.1 and 1.2. Business models based on these parameters outperform the passive Buy and Hold strategy. In fact, they outperform the Buy and Hold strategy for both the entire period under review and the yielded five-year periods (including transaction fees). The only exception is the five-year periods covering 2015 for O2 stocks. After the end of the uncertainty period caused by unclear intentions of the new majority stockholder, the stock price rose sharply. These results are in conflict with the efficient markets theory and suggest that in the period under review, the Czech stock market was not effective in any form.

January anomalies on CEE stock markets

Numerous studies show that stock markets are often impacted by various calendar anomalies that disrupt the “random walk” behavior of stock prices. These anomalies contradict the Efficient markets theory and can be exploited to generate abnormal returns. This paper investigates the presence of two of them, namely the January effect and the January barometer, on the stock markets of 12 Central and Eastern European (CEE) countries. The paper examines the statistical significance of differences in returns recorded over the month of January and returns recorded over the other months (the January effect), as well as the statistical significance of differences between returns recorded during the remainder of year after a positive January return and after a negative January return (the January barometer). The results show, among other things, that the statistically significant January effect affects the Estonian, Lithuanian, Czech, Romanian, and Latvian stock markets. On the Romanian and Lithuanian stock markets, statistically significantly higher January returns are accompanied by statistically significantly higher January price volatility. On the other hand, we can speak of a statistically significant January barometer only in the case of the Latvian, Lithuanian, and Ukrainian stock markets. The presence of these anomalies is contrary to the Efficient market theory. It can be assumed that proper investment strategies based on these calendar anomalies should be able to generate abnormal returns. AcknowledgmentThis paper is an outcome of the science projects VEGA (1/0613/18) and VEGA (1/0221/21).

Cyclic abundance fluctuations in a completely isolated population of Euphydryas maturna

A highly isolated and the last autochthonous Czech Republic population of the endangeredEuphydryas maturna(Lepidoptera: Nymphalidae) is monitored since 2001 by larval nests counts. The 20 years` time series displays remarkable abundance fluctuations with peak-to-peak period 11 years, peak numbers >150 and bust numbers <15 larval nests (arithmetic and harmonic means: 92.6 and 36.3). Establishment of more favourable management of the site probably heightened and prolonged the boom phase but did not alter the overall pattern. We attribute the cycling to pressures of natural enemies. Climatically unfavourable years appear deepening the bust phase. Species displaying such fluctuations cannot be conserved within a single site, which is being addressed by ex-situ breeding of the Czech stock and recent reestablishment of two additional populations, with the aim to achieve asynchronous dynamics of the local populations and eventually stabilise the regional metapopulation.

Impact of MiFID II on the Market Volatility—Analysis on Some Developed and Emerging European Stock Markets

The paper investigates whether the implementation of MiFID II, a packet of financial legislation applying broadly to European Union financial markets, has led to a change in the volatility of some European developed and emerging stock markets. We show that for the developed capital markets considered in the analysis, MiFID II did not lead to a decrease in the volatility of capital markets. On the contrary, for all analysis intervals considered (3 months, 6 months, 12 months, 18 months and 24 months), the impact on volatility is positive, with volatility increasing in the case of the FTSE 100, CAC40 and DAX stock indexes. There is a similar significant relationship for the Czech stock market, but only over the three-month interval. For the Polish and Romanian stock markets, which enforced MiFID II later, a negative impact of MiFID II on volatility could also be observed. In the Romanian market, MiFID II had a negative impact on volatility on the short-term horizon, while for the Polish market, the impact of MiFID II on volatility is noticeable on a longer term of 24 months.

Stock portfolio selection using aspiration level-oriented procedure: real case on the RM-SYSTEM Czech stock exchange

Many approaches have been designed to solve a stock portfolio selection problem. The aim of this paper is to provide a complex supporting tool for a portfolio selection. So, the problem is seen as a multi-objective one. People tend to settle for a reasonably satisfactory rather than an optimal solution which is provided by the existing methods. That means they substitute the goal of reaching specified aspiration levels for the goal of maximization. Developed Aspiration Level-Oriented procedure (ALOP) is based on searching a linear decision space and current solutions are sought by means of an interactive goal programming approach. Such a procedure has significant advantages for investment decision making. The decision space is searched by changes of aspiration levels using problem-solving approaches. Moreover, the approach can be combined with a weight model of preferences. The power of the developed method is demonstrated on a real investment portfolio making on the RM-SYSTEM Czech stock exchange. A stock portfolio process is performed for two most typical investment strategies-dividend-oriented and capital-oriented investor. The essential criteria (objectives) are determined, as well as their importance (according to the investor’s preferences). After a decision making procedure, their resulting portfolios are analysed and compared.

Calendar Effects in the Stock Markets of Central European Countries

The efficient market hypothesis suggests that there are no opportunities to gain above‑normal profits using available information, because it is all reflected in the prices. However, calendar anomalies are found to contradict the efficient market hypothesis and enable investors to predict prices during specific days. Based on a review of papers on market efficiency and market anomalies, this paper examines and compares calendar effects known as ‘the month‑of‑the year effect’ and ‘the day‑of‑the‑week effect’ between the stock markets of three Central European countries: Poland, Hungary and the Czech Republic. The study has revealed the presence of calendar anomalies in the indexes representing small‑cap stocks listed on the Polish stock market and, to some extent, in the indexes used in the Hungarian and Czech stock markets.

Time-Varying Beta—The Case Study of the Largest Companies from the Polish, Czech, and Hungarian Stock Exchange

ABSTRACT The main goal of this article is to investigate empirically the Kalman approach to estimate the time-varying beta parameter as a systematic investment risk market in Poland, Czech Republic, and Hungary. In our research, we investigate the assessments of beta on the basis of seven specifications of time-varying beta for the 12 largest companies listed on the Warsaw Stock Exchange (Poland), 7 on Prague Stock Exchange (Czech Republic), and 11 on Budapest Stock Exchange (Hungary). The obtained results are compared with the estimates received on the basis of Sharpe’s linear model. Estimations are made using the maximum likelihood method for monthly data in the period 2005–2017. We are presenting the ranking of the used specifications according to three criteria of goodness of fit and the matrix of correlation coefficients between the results of these specifications. The results show that the Kalman filter estimators outperform the others.

Empirical Testing of the Response of Czech Stock Market to Downgrades of Greek Credit Rating in the Light of the Efficient Market Hypothesis

Empirical testing of the linkages between macroeconomic news and asset price movements in terms of response to released macroeconomic information have been a subject of many investigations using different testing methods. The objective of this paper is to study the impact of announcements of Greek credit rating downgrades on the prices of the most liquid assets quoted in the Czech stock market. This issue is tightly related to semi-strong form of the efficient market hypothesis, which is one of possible analytical approaches when analyzing behaviour of assets in financial markets. The reaction of the Czech stock market is assessed in relation to seven announcements of Moody´s rating agency regarding changes of credit rating of Greek government bonds in the period 2009–2012. For the purpose of this paper, the event study methodology is applied. The basic idea of this statistical method is to determine values of abnormal returns, which can be defined as a difference between actual and equilibrium returns. In order to calculate equilibrium returns, the Capital Asset Pricing Model (CAPM) is used. The differences between actual and equilibrium returns are then verified with a help of selected nonparametric statistical tests. Namely, the exact sign test and the Wilcoxon signed-rank test are utilized. Based on results of nonparametric statistical tests, the null hypothesis of information efficiency of the Czech stock market is conclusively rejected.

Source Attribution of Cyanides Using Anionic Impurity Profiling, Stable Isotope Ratios, Trace Elemental Analysis and Chemometrics.

Chemical attribution signatures (CAS) for chemical threat agents (CTAs), such as cyanides, are being investigated to provide an evidentiary link between CTAs and specific sources to support criminal investigations and prosecutions. Herein, stocks of KCN and NaCN were analyzed for trace anions by high performance ion chromatography (HPIC), carbon stable isotope ratio (δ(13)C) by isotope ratio mass spectrometry (IRMS), and trace elements by inductively coupled plasma optical emission spectroscopy (ICP-OES). The collected analytical data were evaluated using hierarchical cluster analysis (HCA), Fisher-ratio (F-ratio), interval partial least-squares (iPLS), genetic algorithm-based partial least-squares (GAPLS), partial least-squares discriminant analysis (PLSDA), K nearest neighbors (KNN), and support vector machines discriminant analysis (SVMDA). HCA of anion impurity profiles from multiple cyanide stocks from six reported countries of origin resulted in cyanide samples clustering into three groups, independent of the associated alkali metal (K or Na). The three groups were independently corroborated by HCA of cyanide elemental profiles and corresponded to countries each having one known solid cyanide factory: Czech Republic, Germany, and United States. Carbon stable isotope measurements resulted in two clusters: Germany and United States (the single Czech stock grouped with United States stocks). Classification errors for two validation studies using anion impurity profiles collected over five years on different instruments were as low as zero for KNN and SVMDA, demonstrating the excellent reliability associated with using anion impurities for matching a cyanide sample to its factory using our current cyanide stocks. Variable selection methods reduced errors for those classification methods having errors greater than zero; iPLS-forward selection and F-ratio typically provided the lowest errors. Finally, using anion profiles to classify cyanides to a specific stock or stock group for a subset of United States stocks resulted in cross-validation errors ranging from 0 to 5.3%.

STOCHASTIC MODEL OF SHORT-TERM PREDICTION OF STOCK PRICES AND ITS PROFITABILITY IN THE CZECH STOCK MARKET

This paper deals with stochastic modelling and short time prediction of share price development in Czech stock market. The aim of this research is to create such models which can be used for creating automatic trading strategies that will beat the market. Reliability of these models is being checked in three highly liquid shares from Prague Stock O2, CEZ and KB in seven years long period in years 2006–2012. We used Markov chain analysis for modelling. In our models a state space is defi ned on the basis of cumulative daily changes of share price and a state space with eight states is used. The state space is defi ned parametrically as a multiple of standard deviation of daily yields for each share. There were 14 parameters calculated in total and for each parameter nine trading strategies for all shares were applied. It means that 378 trading strategies were calculated. We succeeded in fi nding a set of compact state space models and in applying a compact group of trading strategies on these models which always beat the market when invested in portfolio. The average annual market yield was 3.6%. The average yield of our portfolios oscillates between 4.7% and 14.8%. Strategies overcame the market also even after including transaction costs. After including transaction costs in amount of 0.1% from the trade volume a decrease of average annual yields would occur in the range from 0.45 to 2.1 percentage points. We reached the best results in the sideway trend and in shares with less changing volatility. Conclusions of this research are in contradiction to the Effi cient Market Hypothesis. Results indicate that Czech stock market is not effective in any of its form.

Intermarket Technical Research of the U.S. Capital Markets and the Czech Stock Market Performance

Globalization of the capital markets increasingly leads the investors to understand the fundamentals and technicals of asset cross-correlations and the global asset allocation seems to be an important task. The paper measures product momentum correlations between the four leading global benchmarks Standard & Poor’s stock index, Thomson Reuters/Jefferies CRB index, 30-Year U.S. Treasury Bond Price index and Dollar Index and between these indices and the Czech stock PX index. Empirical results illustrate that statistically significant correlations between U.S. indices existed over some past period at the 95.0% confidence level. In addition, the significant relation between indices Standard & Poor’s stock index, Thomson Reuters/Jefferies CRB index and the Czech stock market PX during the past fifteen years has been detected. These conclusions were reached from an analysis of monthly data in the United States and the Czech Republic, from January 1999 to April 2014. The empirical results offer beneficial applications not only for investors to diversify their risk but also for policy-makers to allocate resources more efficiently.

Analysis of Moving Average Rules Applicability in Czech Stock Market

Abstract Contemporary state of the art of financial time series modelling is connected to the Efficient Market Hypothesis according to which “prices fully reflect all available information” and hence future evolutions are unforecastable. In simple terms, EMH states that by predicting the future development we are not able to achieve the profits superior to the profits of the market index when these are adjusted for the risk and transactions costs are deducted. On the other hand, there exist works providing evidences that markets are not efficient. In these works, however, the strategies (or technical trading rules) are demonstrated to provide the extra performance in short term only and then the extra performance vanishes. In the paper we apply moving averages in order to define automated trading system and then analyze its profitability in Czech stock market. The results are statistically tested and statistical inference about the applicability of such an automated trading system in Czech stock market is made.

Comparison of Forecasting Volatility in the Czech Republic Stock Market

The aim of this paper is to examine different GARCH models with three different distributions in order to compare their forecasting power in terms of volatility existing in the returns of the Czech Stock Market and more specific in the PX index, for the period 08.01.2001-20.07.2012. We have employed GARCH, GJR-GARCH and EGARCH models against normal, student-t and generalized error distributions. Then, we have forecasted stock market volatility for the Czech Republic by its returns using the same models, GARCH, GJR-GARCH and EGARCH comparing their forecasting performance. The results show that return volatility can be characterized by significant persistence and asymmetric effects. We have estimated the corresponding variances for all models for the full sample period using static forecasts. After comparing the forecasting performance of all nine models it was found that the EGARCH model has the best forecasting performance compared to others.

VYUŽITÍ DISTRIBUČNÍCH A KORELAČNÍCH TESTŮ PRO TESTOVÁNÍ HYPOTÉZY EFEKTIVNOSTI ČESKÉHO KAPITÁLOVÉHO TRHU

This paper deals with efficiency testing of the Czech stock market. In this work there are defined different forms of efficiency, whereas key attention is focused on the weak-form of market efficiency. The goal of this paper is to find the weak-form of efficiency with the help of various tests. We have used some basic methods for our analysis: the autocorrelation coefficient, the Ljung-Box test and selected tests of normality – some classical normality tests (the Shapiro-Wilk test, the Jarque-Bera test, the Lilliefors test) and some robust normality tests (the robust Jarque-Bera test, the directed SJ test and medcouple MC-LR test). Source data for purpose of testing of weak-form of efficiency include years from 2000 to 2008, whereas daily and monthly logarithmic returns of the stock exchange market index PX are analyzed. In this paper we also analyze the daily and monthly logarithmic returns of the U.S. stock exchange market index DJI for purposes of comparison.

Intraday and intraweek trade anomalies on the Czech stock market

The paper examines intraday and intraweek market returns on the Czech stock market for the search of time and seasonal anomalies in its activities during the last ten years. Existence or absence of anomalies indicates the efficiency of the market. A group of regression models and GARCH (1,1) model is used for the analysis of daily and high frequency data of the PX index. Time varying nature of market seasonalities is revealed with the Czech equity market having implications for changing efficiency over the studied period, when the Czech Republic’s accession to the EU implied the increase in efficiency and the global financial crisis led to opposite results and regularities, which are not yet fully overcomed. Additionally, significant hour-of-the-day effect (open jump effect) in the index returns is established.

Comparing multifractality among Czech, Hungarian and Russian stock exchanges

In this paper, we analyse multifractality among Czech, Hungarian and Russian stock exchanges. For this end we perform a method titled multifractal detrended fluctuation analysis (MF-DFA) to investigate the multifractal properties of PX, BUX and RTS indices. By applying the MF-DFA method we first calculate the generalised Hurst exponents, we then deduce the Renyi exponents as well as the singularity spectrum of these indices. Furthermore, we perform shuffling and surrogate techniques to detect the sources of multifractality. We also compute the contribution of two major sources of multifractality that are long-range temporal correlations and fat-tail distribution. This study shows that the Czech, Hungarian and Russian stock exchanges are neither efficient nor fractals, but they are multifractal markets. By comparing spectrum width of these indices, we also find which index has the richer multifractal feature.

Comovement Dynamics between Central and Eastern European and Developed European Stock Markets during European Integration and Amid Financial Crises – A Wavelet Analysis

Stock market comovements between developed (represented in the article by markets of Austria, France, Germany, and the UK) and developing stock markets (represented here by three Central and Eastern European (CEE) markets of Slovenia, the Czech Republic, and Hungary) are of great importance for the financial decisions of international investors. From the point of view of portfolio diversification, short-term investors are more interested in the comovements of stock returns at higher frequencies (short-term movements), while long-term investors focus on lower frequencies comovements. As such, one has to resort to a time-frequency domain analysis to obtain insight about comovements at the particular time-frequency (scale) level. The empirical literature on the CEE and developed stock markets interdependence predominantly apply simple (Pearsons) correlation analysis, Granger causality tests, cointegration analysis, and GARCH modeling. None of the existent empirical studies examine time-scale comovements between CEE and developed stock market returns. By applying a maximal overlap discrete wavelet transform correlation estimator and a running correlation technique, we investigated the dynamics of stock market return comovements between individual Central and Eastern European countries and developed European stock markets in the period from 1997-2010. By analyzing the time-varying dynamics of stock market comovements on a scale-by-scale basis, we also examined how major events (financial crises in the investigated time period and entrance to the European Union) affected the comovement of CEE stock markets with developed European stock markets. The results of the unconditional correlation analysis show that the developed European stock markets of France, the UK, Germany and Austria were more interdependent in the observed period than the CEEs stock markets. The later group of countries exhibited a lower degree of comovement between themselves as well as with the developed European stock markets during all the observed time period. The Slovenian stock market was the least correlated with other stock markets. By using the rolling wavelet correlation technique, we wanted to answer the question as to how the correlation between CEE and developed stock markets changed over the observed period. In particular, we wanted to examine whether major economic (financial) and political events in the world and European economies (the Russian financial crisis, the dot-com financial crisis, the attack on the WTC, the CEE countries joining the European union, and the recent global financial crisis) have influenced the dynamics of CEE stock market comovements with developed European stock markets. The results show that stock market return comovements between CEE and developed European stock markets varied over time scales and time. At all scales and during the entire observed time period the Hungarian and Czech stock markets were more interconnected to developed European stock markets than the Slovenian stock market was. The highest comovement between the investigated CEE and developed European stock market returns was normally observed at the highest scales (scale 5, corresponding to stock market return dynamics over 32-64 days, and scale 6, corresponding to stock market return dynamics over 32-64 and 64-128 days). At all scales the Hungarian and Czech stock markets were more connected to developed European stock markets than the Slovenian stock market. We found that European integration lead to increased comovement between CEE and developed stock markets, while the financial crises in the observed period led only to short-term increases in stock market return comovements.DOI: http://dx.doi.org/10.5755/j01.ee.23.1.1221

Are European equity markets efficient? New evidence from fractal analysis

We report an empirical analysis of long-range dependence in the returns of eight stock market indices, using the Rescaled Range Analysis (RRA) to estimate the Hurst exponent. Monte Carlo and bootstrap simulations are used to construct critical values for the null hypothesis of no long-range dependence. The issue of disentangling short-range and long-range dependence is examined. Pre-filtering by fitting a (short-range) autoregressive model eliminates part of the long-range dependence when the latter is present, while failure to pre-filter leaves open the possibility of conflating short-range and long-range dependence. There is a strong evidence of long-range dependence for the small central European Czech stock market index PX-glob, and a weaker evidence for two smaller western European stock market indices, MSE (Spain) and SWX (Switzerland). There is little or no evidence of long-range dependence for the other five indices, including those with the largest capitalizations among those considered, DJIA (US) and FTSE350 (UK). These results are generally consistent with prior expectations concerning the relative efficiency of the stock markets examined.

The impact of foreign macroeconomic news on financial markets in the Czech Republic, Hungary, and Poland

In this paper, we investigate the effects of euro area and US macroeconomic news on financial markets in the Czech Republic, Hungary, and Poland (CEEC-3) from 1999 to 2006. Using a GARCH model, we examine the impact of news on daily returns of 3-month interest rates, stock market indices, exchange rates versus the euro, and the US dollar. First, both US and European macroeconomic news has a significant impact on CEEC-3 financial markets. Second, the process of European integration is accompanied by an increasing importance of euro area news relative to US news. Third, there are country-specific differences: for example, the Czech stock market is relatively more affected by foreign news since the Copenhagen Summit in December 2002. In general, our results support the hypothesis of a deepening euro area influence on the CEEC-3 over time and a corresponding reduction in the relative importance of US shocks.