National Repository of Grey Literature 45 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Herd behavior of investors in the stock market: An analysis of cross-country effects in the CEE
Lerche, Vojtěch ; Kukačka, Jiří (advisor) ; Vácha, Lukáš (referee)
The thesis examines herding behavior of investors towards the market average in 10 CEE stock markets during the period 2000-2018. Least squares and quantile regression methods provide evidence of herding inside the majority of the countries. During the global financial crisis and the Eurozone crisis, the herding mentality was more intense only in Slovenia and Croatia. The thesis finds mixed results in asymmetric herding during days of positive and negative market returns. The main finding, and a contribution to the literature, is that the domestic cross-sectional dispersion of returns in the CEE is affected by the dispersion of returns of the foreign stock markets in the USA, the UK, and Germany. In addition, empirical results suggest that extreme market conditions in the U.K. market have an impact on the formation of herding forces within the CEE stock markets. Short-run arbitrageurs can benefit from collective decisions of investors that in turn drive stock prices away from their fair value, but the presence of herding undermines benefits of portfolio diversification. In the long-run, the contagious international effects may result in a severe instability of the whole region and in market inefficiency.
Corporate Acquisitions and Expected Stock Returns: A Meta-Analysis
Parreau, Thibault ; Havránek, Tomáš (advisor) ; Kukačka, Jiří (referee)
This thesis aims at investigating the puzzling relationship between cor- porate acquisitions and expected stock returns by reviewing numerous studies on this topic through the use of state of the art meta-analysis tools. Such an analysis is required because many papers examined this relationship but their results varied. We therefore collected 421 estimates from 20 papers and led multiple regressions to test for the presence of publication bias. Throughout this analysis we indeed found evidence supporting the existence of publication bias. Furthermore, we decided to apply Bayesian Model Averaging to reduce the model uncertainty and find out why our abnormal returns estimates greatly vary across stud- ies. Our results suggest that one of the most important drivers are the standard-error terms. This subsequently proves that publication bias is the most responsible for the heterogeneity amongst our estimates. Our analysis fails to demonstrate any positive effects from M&A activity on a firm post-acquisition performance. We suggest that other motives are under-represented in the underlying theory that aims to assess M&A outcomes. Keywords Mergers and Acquisitions, Stock Returns, Abnormal Re- turns, Meta-Analysis, Publication bias Author's e-mail thibault.parreau@gmail.com Supervisor's e-mail...
Cusp catastrophe theory: Application to the housing market
Kořínek, Vojtěch ; Kukačka, Jiří (advisor) ; Nevrla, Matěj (referee)
The bachelor's thesis applies the stochastic cusp catastrophe model to the housing market of the United States. Weekly data over the period from 2007 to 2017 are used. The current catastrophe theory literature related to the housing market is reviewed, the models found are assessed and expanded. Specifically, we have identified three deficiencies of the catastrophe models applied to housing market in the current literature and our contribution lies in the elimination of these deficiencies. In order to satisfy the constant volatility assumption of the model, the state variable is normalized by the estimated volatility derived from GARCH. Furthermore, multiple control variables are added to the model to represent the activity of fundamentalists and chartists. The results suggest that the cusp catastrophe model fits the data better than the linear and logistic models. The normalization of the state variable improves the model performance while the introduction of the additional control variables does not produce better results. Keywords Housing market, catastrophe theory, stochastic cusp catastrophe model, hous- ing bubble, real estate, fundamental investors, speculation. 1
The weather and stock returns
Černý, Patrik ; Kukačka, Jiří (advisor) ; Čornanič, Aleš (referee)
This thesis examines a behavioral finance topic, the effect of weather on stock returns. The research was performed with the aim to verify formerly published results of various weather variables like sunshine, precipitation or temperature influencing stock markets. For the analysis Ordinary Least Squares regressions were implemented to investigate the relationships of stock returns and weather variables proposed in the previous literature as well as other market efficiency effects, a Monday and a January effect. In addition, GARCH model was carried out to check the influence of weather conditions on stock return volatility. Data used for the analysis consists of 24 emerging and 23 developed markets worldwide in the period 2006-2017. The results are not in support of the theory of weather affecting market trading which corresponds to the market efficiency theory. There seems to be no difference between the developed and emerging countries, not even countries' land area plays a role. However, in the thesis repeatedly appears significant evidence of the presence of the Monday effect. Keywords Behavioral finance, Weather effect, Market efficiency, Anomaly, GARCH 1
Frequency connectedness and cross section of stock returns
Haas, Emma ; Baruník, Jozef (advisor) ; Kukačka, Jiří (referee)
The thesis presents a network model, where financial institutions form linkages at various investment horizons through their interdependence measured by volatility connectedness. Applying the novel framework of frequency connectedness mea- sures Baruník & Křehlík (2018), based on spectral representation of variance de- composition, we show fundamental properties of connectedness that originate in heterogeneous frequency responses to shocks. The newly proposed network mod- els characterize financial connections and systemic risk at the short-, medium- and long-term frequency. The empirical focus of this thesis is on the interde- pendence structure of US financial system, specifically, major U.S. banks in the period 2000 - 2016. In the light of frequency volatility connectedness measures, we argue that stocks with high levels of long-term connectedness represent greater systemic risk, because they are subject to persistent shocks transmitted for longer periods. When we assess institutions' risk premiums in asset pricing model, the model confirms the significance of volatility connectedness factor for asset prices. JEL Classification C18, C58, C58, G10, G15, Keywords connectedness, frequency, spectral analysis, sys- temic risk, financial network Author's e-mail 93539385@fsv.cuni.cz Supervisor's e-mail...
Artificial Prediction Markets, Forecast Combinations and Classical Time Series
Lipán, Marek ; Baruník, Jozef (advisor) ; Kukačka, Jiří (referee)
Economic agents often face situations, where there are multiple competing fore- casts available. Despite five decades of research on forecast combinations, most of the methods introduced so far fail to outperform the equal weights forecast combination in empirical applications. In this study, we gather a wide spectrum of forecast combination methods and reexamine these findings in two different classical economic times series forecasting applications. These include out-of- sample combining forecasts from the ECB Survey of Professional Forecasters and forecasts of the realized volatility of the U.S. Treasury futures log-returns. We asses the performance of artificial predictions markets, a class of machine learning methods, which has not yet been applied to the problem of combin- ing economic times series forecasts. Furthermore, we propose a new simple method called Market for Kernels, which is designed specifically for combining time series forecasts. We found that equal weights can be significantly out- performed by several forecast combinations, including Bates-Granger methods and artificial prediction markets in the ECB Survey of Professional Forecasters application and by almost all examined forecast combinations in the financial application. We also found that the Market for Kernels forecast...
Analysis of a Behavioral New Keynesian Model
Křížková, Šárka ; Kukačka, Jiří (advisor) ; Hlaváček, Michal (referee)
The thesis focuses on the analysis of a Behavioral New Keynesian DSGE model. In particular, various specifications of the model are collected from the existing literature and their combinations are simulated. The specifications include heuristics for forecasting output gap, sets of estimated or calibrated parameters and model structures. The resulting simulated output and inflation gap series are compared with the macroeconomic stylized facts and real world data from the US and Euro area based on their distributional characteristics and autocorrelation structures. In addition, a comparison of various simulated model specifications is performed based on the level of correlation between fractions of agents following a specific heuristic and the resulting output and inflation gap values. The distributional characteristics of the US output gap seem to be matched the best by the specifications with unbiased and extrapolative output gap heuristics generating series with higher levels of variance and kurtosis. Contrarily, the Euro output gap is best matched by specifications with optimistic, pessimistic and unbi- ased heuristics producing series with lower levels of variance and kurtosis. Second, the autocorrelation structure of the simulated series tends to mirror the stylized facts as opposed to the...
Good volatility, bad volatility, and the cross-section of stock returns at different investment horizons
Sako, Tony Ryan Hlali ; Baruník, Jozef (advisor) ; Kukačka, Jiří (referee)
Starting with the assumption that different investors have different investment time preferences and different risk tolerances within their given investment time-frames, this paper investigates the value of employing multiresolution analysis to model volatility and risk-pricing. In terms of estimation and fore- casting performance we were able to reduce by at least half the volatility fore- casting errors, with even better results at longer horizons. In regards to risk pricing we learn that extreme aggregate volatility (i.e. tail risk) is priced but regular volatility is not. Additionally we find that whilst aggregate volatility is generally more important over the long-horizon, during periods of market turmoil it is much more significant over the short-horizon. Finally we show that stocks with high sensitivity to aggregate volatility have lower subsequent returns supporting the idea that they become attractive as a hedge against market volatility. JEL Classification C12, C13, C21, C22, C31, C32, C51, C52, C53 Keywords Realized Volatility, Wavelet, Long-Memory Models, Cross-Section, Volatility Forecast, High-Frequency Data Author's e-mail tony sako@yahoo.com Supervisor's e-mail barunik@fsv.cuni.cz
Accounting-based credit scoring models - The Altman Z-score
Dibon, Michael ; Čornanič, Aleš (advisor) ; Kukačka, Jiří (referee)
This Bachelor thesis is focused on accounting-based credit scoring models, predominantly on Altman (1968) Z-score. We examine the relevance of the Z-score model on European publicly traded companies over the period 2012 - 2017. Moreover, we analyze whether it is important to calibrate original models as well as we test the performance of models given different misclassification costs. Our results suggest that Altman original Z-score model is still, after 50 years of existence, relevant in the European after-crisis environment. Further, we found evidence that re-estimation of the model is unnecessary and could even cause harm to model performance. Finally, the performance of models seems to be stable given not equal misclassification costs, as the more accurate models from ROC analysis reported better results in an economic test. Keywords Z-score, accounting-based models, credit score, Altman, financial ratios, bankruptcy, ROC, Europe
Extending Hotelling's location model into Agent-based domain
Vainer, Jan ; Kukačka, Jiří (advisor) ; Smutná, Šarlota (referee)
This thesis examines behaviour of adaptive agents in Hotelling's location model. We conduct an agent-based simulation in Hotelling's setting with two agents, where the agents use Nash-Q learning mechanism for adaptation. Traditional game-theoretic models often stand on strong assumptions imposed on players such as rationality and perfect information. We explore what alternations or re- finements of results this technique brings in comparison to the original analytical solution of the theoretical Hotelling's location model. We discover that under Nash-Q learning and quadratic consumer cost func- tion, agents with high enough valuation of future profits learn behaviour similar to aggressive market strategy, where both agents make similar products and lead a price war in order to eliminate their opponent from the market. This be- haviour closely resembles the Minimum differentiation principle from the original Hotelling's paper with linear consumer costs. This result is surprising because in our simulation, quadratic consumer cost functions are used, which should result in maximum differentiation of the products. Our results suggest that the Prin- ciple of minimum differentiation could be justified based on repeated interaction of the agents and long-run optimization. Additionally, suitability of...

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