National Repository of Grey Literature 17 records found  1 - 10next  jump to record: Search took 0.01 seconds. 
Does Euro Adoption Reduce Central Bank Staff?
Žídeková, Lucia ; Havránek, Tomáš (advisor) ; Fanta, Nicolas (referee)
The thesis examined the impact of euro adoption and joining the EU's Exchange Rate Mechanism (ERM II) on central bank staff using the synthetic control method. The first part of the work focused on assembling a data set on personnel development and proposing predictors for approximating central bank size. In the second part, the study presents the models and results, revealing a significant decrease in the number of employees at Latvijas Banka after joining ERM II. However, the research encountered limitations due to data unavailability, possible shocks experienced by donor pool countries, and poor fit of pre-intervention characteristics, leading to ambiguous findings regarding the size and significance of the negative effect of euro adoption on personnel development for Latvijas Bank, the National Bank of Slovakia, and Eesti Pank. The reliability of the results for the National Bank of Belgium and the Bank of Finland was affected by a short pre-intervention period and the risk of over-fitting.
Analysis of herd behavior across cryptocurrencies
Krouská, Kateřina ; Kukačka, Jiří (advisor) ; Fanta, Nicolas (referee)
This thesis studies herding behavior in the cryptocurrency market between 2017 and 2022. Results from the static model reveal significant imitative behavior in the up market and during the bull year 2017. In addition, this thesis ranks among the first papers that study the effect of the early stage of the war in Ukraine on the market-wide herding behavior. Furthermore, due to Bitcoin's dominant position among other coins, closer attention is devoted to studying its influence on the herding behavior in the market. However, herding seems to be present only during extreme Bitcoin movements. In response to these results, five dominant coins (Bitcoin, Ethereum, XRP, Litecoin and Dogecoin) are excluded from the sample and their influence on the rest of the market is studied. The evidence suggests strong herding behavior of the rest of the market around these five giants. Therefore, the return of smaller coins seems to be influenced by the performance of larger coins, rather by solely that of Bitcoin. JEL Classification G02, G15, G40, C22, C58 Keywords Cryptocurrencies, Herding behavior, Bitcoin, COVID-19 Title Analysis of herd behavior across cryptocurren- cies Author's e-mail 43789078@fsv.cuni.cz Supervisor's e-mail jiri.kukacka@fsv.cuni.cz
Examining the Interaction between the Cryptocurrency Market Development and Activity on Leading Social Networks
Doškář, Jakub ; Fanta, Nicolas (advisor) ; Baruník, Jozef (referee)
In this thesis, analyses are conducted to determine whether various measurements of social media activity, including the sentiment value of posts, can be drivers or even predictors of a change in a selected metrices of cryptocurrencies. The analyses are performed on data collected in one-hour and fifteen-minute time intervals from the February of 2021 until the November 2022. The results of the analysis show that variability of the closing price of Bitcoin can be to some extent explained by sentiment derivatives only. Furthermore, it was proven that sentiment derived from social media is significant when used as a predictor of a direction of a price change, under specific circumstances. These results oppose the previous studies, where sentiment was not recognized significant. Moreover, it was determined that considering one- hour intervals returns marginally better outcomes than in the case of shorter time intervals. The thesis outlines the challenges researchers can face when using this technique in their work. Keywords Cryptocurrencies, Bitcoin, Social sentiment, Sentiment analysis, Twitter, Social media, Cryptocurrency exchange Title Examining the Interaction between the Cryptocurrency Market Development and Activity on Leading Social Networks
Order book microstructure and fair price estimation on betting exchanges
Smutný, Josef ; Fanta, Nicolas (advisor) ; Kukačka, Jiří (referee)
The aim of this paper is to predict the fair price as accurately as possible from the microstructure of the orderbook on Betfair, the world's largest betting exchange. In particular, the work focuses on the analysis of the effect of disproportionately large quotes on the fair price of the market. It also addresses their theoretical monetization in practice in the case of market inefficiency. The results show that, from the data examined, traded markets are relatively efficient and the factors that can be inferred from the microstructure of the orderbook are, in the vast majority of cases, not statistically significant for predicting the fair price of a given event. However, the data do show exceptions where, in particular types of markets, these quotes are statistically significant and have the expected impact on the model's prediction in the form of increasing the probability of the selection they want to trade. Thus, the model prediction can in some cases be used as a fair price indicator by which one could theoretically trade profitably on Betfair or even on other platforms, assuming they offered a better price. Keywords Betting exchange, Fair price, Quotes, Betting odds, Back, Lay, Spread Title Order book microstructure and fair price estimation on betting exchanges
Momentum trading strategy performance before, during, and after the COVID-19 crisis
Řeřicha, Dávid ; Fanta, Nicolas (advisor) ; Vácha, Lukáš (referee)
This thesis investigates the well-known momentum trading strategy from January 2013 to May 2022 on the US stock market. The goal of this thesis is to examine whether the phenomenal momentum anomalies occurred during COVID-19 crisis. The main part is addressed to the creation of momentum portfolios from the whole US stock market using daily data from 500 firms in the S&P 500 index and additional 11 sectoral momentum portfolios. Results confirm the power of momentum portfolios as the past winners accumulated the highest returns over the whole observed period and clearly outperformed the market. Focusing closely on COVID- 19 period we observed past losers outperforming past winners, which confirms another momentum anomaly on the US stock market. Therefore, this thesis referred to the Carhart Four - Factor Model model that is based on the Fama-French Three - Factor model with additional momentum factor. Unfortunately, results indicate no statistically significant power to explain the momentum behaviour during COVID-19 crisis.
Impact of COVID-19 fiscal measures on Non-Performing Loans
Bajcár, Tomáš ; Jakubík, Petr (advisor) ; Fanta, Nicolas (referee)
We study to which extent fiscal measures related to COVID-19 have mitigated credit risk proxied by non-performing loans (NPLs) in selected European countries. In this respect, we control for the macroeconomic and bank-specific determinants of non-performing loans. We limit our empirical analysis to NPLs and fiscal measures that aimed at non-financial corporations. We utilize a quarterly panel dataset covering the period from 2019 to 2021. We further employ split according to sectors of economic activity and cover 423 sectors in 23 European countries. The difference GMM estimation for dynamic panel data is utilized. Our empirical analysis suggests that the following variables significantly affect NPL ratios: economic growth, employment, nominal effective exchange rate and return on equity. In the case of the fiscal measures, public guarantees and tax reliefs were found to have a statistically significant and negative effect on NPLs. This finding supports the notion that during the COVID-19 pandemic, loan guarantees and lower tax burdens helped businesses maintain liquidity and solvency, which resulted in reduction of NPL ratios. Contrary, loan moratoria were found to positively affect NPL ratios. There is mixed evidence regarding direct grants and no empirical evidence was found in the case of...
An empirical study of impact of inclusion and exclusion from an Index on stock prices
Vellechová, Karolína ; Vozárová, Pavla (advisor) ; Fanta, Nicolas (referee)
This thesis studies the effect of index components changes on the stock prices of added and deleted components. Specifically, the thesis follows two market indexes from Western Europe, CAC 40 and DAX 30, in the period from financial crisis in 2008 to 2020. The theory of Efficient Market Hypothesis suggests that index components change should not impact firm's price, because the index membership is supposed to have no new information, the findings of various researchers found out the opposite. There are several hypotheses, which try to explain these effects. In this thesis three methods were used to estimate the effect of addition and deletion, these were abnormal return, abnormal trading volume and close open difference calculation. The results obtained by all three methods were mostly consistent with the results of previous studies of the US indexes. The calculation of abnormal volume reported significant and expected results for both indexes, which suggested that the day before the change has the highest abnormal volume. Also, the results for close open differences discovered expected pattern, that the highest change happens usually on the day after announcement, which informs about how fast investors reply to index change. The abnormal returns for the CAC 40 index show expected significant...
Arbitrage on the Cryptocurrency Markets: An Analysis of Potential Opportunities
Suchánek, Vojtěch ; Fanta, Nicolas (advisor) ; Čech, František (referee)
1 Abstract Cryptocurrency markets have currently a lot of attention both from the public and researchers. This thesis connects the well-documented field of arbitrage with the relatively new bitcoin phenomenon. Thanks to the efforts of cryptocurrencies for decentralization and non-regulation, they are an ideal asset for arbitrage trading. This study tries to answer whether price differences between cryptocurrency exchanges existed during the second and third quartal of 2021 and if it was possible to perform an arbitrage trading with positive profit. It analyzes several trading strategies and ways how to execute these trades. An important part of the study is the involvement of trading fees, which play a crucial role in total profitability but are often omitted in similar research. The findings confirm that price differences existed during the analyzed period, and their values allow for profitable arbitrage trading. The best performing strategy uses stable-coin USDT as a mean of transport money between exchanges, which lowers the time of one trade and allows multiple trades during one price difference spike. This strategy was able to gain 362.60% profit over the analyzed period. On the other hand, the distribution of trades over the analyzed period shows some irregularities, which might have a negative impact...
Analysis of the US stock market during the COVID-19 pandemic
Tůma, Adam ; Krištoufek, Ladislav (advisor) ; Fanta, Nicolas (referee)
This work investigates the effect of the COVID-19 pandemic on the S&P 500 stock index and its eleven sectors. Employing the ARMA and the T-GARCH model on a time series of daily returns from 2018 until March 2021, we examine the impact on volatility, returns, and day-of-the-week effect during the stock market crash caused by the pandemic and the period after. Our main findings imply that in the case of returns, the Monday effect was more negative than the Friday effect during the market crash and vice versa in the rising market after the crash. Concluding that the calendar time hypothesis holds for the observed periods. In terms of volatility, it drastically increased across the US stock market during and even after the crash. The increase was especially noticeable for the IT and Energy sectors. We also found the U-shaped daily volume pattern changed significantly with proportionately less volume of trades happening in the first half-hour of trading and more throughout the whole day.

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