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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.
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Feature Selection for Factored Phrase-Based Machine Translation
Tamchyna, Aleš ; Bojar, Ondřej (advisor) ; Popel, Martin (referee)
In the presented work we investigate factored models for machine translation. We provide a thorough theoretical description of this machine translation paradigm. We describe a method for evaluating the complexity of factored models and verify its usefulness in practice. We present a software tool for automatic creation of machine translation experiments and search in the space of possible configurations. In the experimental part of the work we verify our analyses and give some insight into the potential of factored systems. We indicate some of the possible directions that lead to improvement in translation quality, however we conclude that it is not possible to explore these options in a fully automatic way.
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Algorithmic fundamental trading
Pižl, Vojtěch ; Krištoufek, Ladislav (advisor) ; Bubák, Vít (referee)
This thesis aims to apply methods of value investing into developing field of algorithmic trading. Firstly, we investigate the effect of several fundamental variables on stock returns using the fixed effects model and portfolio approach. The results confirm that size and book- to-market ratio explain some variation in stock returns that market alone do not capture. Moreover, we observe a significant positive effect of book-to-market ratio and negative effect of size on future stock returns. Secondly, we try to utilize those variables in a trading algorithm. Using the common performance evaluation tools we test several fundamentally based strategies and discover that investing into small stocks with high book-to-market ratio beats the market in the tested period between 2009 and 2015. Although we have to be careful with conclusions as our dataset has some limitations, we believe that there is a market anomaly in the testing period which may be caused by preference of technical strategies over value investing by market participants.
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Does LSTM neural network improve factor models' predictions of the European stock market?
Zelenka, Jiří ; Baruník, Jozef (advisor) ; Čech, František (referee)
This thesis wants to explore the forecasting potential of the multi-factor models to predict excess returns of the aggregated portfolio of the European stock mar- ket. These factors provided by Fama and French and Carhart are well-known in the field of asset pricing, we also add several financial and macroeconomic factors according to the literature. We establish a benchmark model of ARIMA and we compare the forecasting errors of OLS and the LSTM neural networks. Both models take the lagged excess returns and the inputs. We measure the performance with the root mean square error and mean absolute error. The results suggest that neural networks are in this particular task capable of bet- ter predictions given the same input as OLS but their forecasting error is not significantly lower according to the Diebold-Mariano test. JEL Classification C45, C53, C61, E37, G11, G15 Keywords Stocks, European market, Neural networks, LSTM, Factor Models, Fama-French, Predic- tions, RMSE Title Does LSTM neural network improve factor mod- els' predictions of the European stock market?
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Algorithmic fundamental trading
Pižl, Vojtěch ; Krištoufek, Ladislav (advisor) ; Bubák, Vít (referee)
This thesis aims to apply methods of value investing into developing field of algorithmic trading. Firstly, we investigate the effect of several fundamental variables on stock returns using the fixed effects model and portfolio approach. The results confirm that size and book- to-market ratio explain some variation in stock returns that market alone do not capture. Moreover, we observe a significant positive effect of book-to-market ratio and negative effect of size on future stock returns. Secondly, we try to utilize those variables in a trading algorithm. Using the common performance evaluation tools we test several fundamentally based strategies and discover that investing into small stocks with high book-to-market ratio beats the market in the tested period between 2009 and 2015. Although we have to be careful with conclusions as our dataset has some limitations, we believe that there is a market anomaly in the testing period which may be caused by preference of technical strategies over value investing by market participants.
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Feature Selection for Factored Phrase-Based Machine Translation
Tamchyna, Aleš ; Bojar, Ondřej (advisor) ; Popel, Martin (referee)
In the presented work we investigate factored models for machine translation. We provide a thorough theoretical description of this machine translation paradigm. We describe a method for evaluating the complexity of factored models and verify its usefulness in practice. We present a software tool for automatic creation of machine translation experiments and search in the space of possible configurations. In the experimental part of the work we verify our analyses and give some insight into the potential of factored systems. We indicate some of the possible directions that lead to improvement in translation quality, however we conclude that it is not possible to explore these options in a fully automatic way.
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Kvantitativní analýza hospodářského cyklu v České republice
Bocák, Petr ; Pošta, Vít (advisor) ; Potužák, Pavel (referee)
The aim of this thesis is to estimate monthly probability that the Czech economy is in a recession. For this purpose, I construct indexes of coincident and leading variables from multiple time series by Maximum Likelihood. Changes in coincident index are preceded by changes in the leading index by almost one year for peaks and about one month for troughs on average. To assess the probability of recession, I estimate multiple mixture models for growth rates of coincident index focusing on Markov-Switching specification for the latent business cycle process. I found that the two-state Markov-Switching AR (1) is superior to other models based on information criteria. Lagged values of leading index further improve the model fit but the model provides less clear signals of recessions compared to models based solely on coincident index.
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Analysis of the Price Convergence of CR towards EU
Havrlant, David ; Pánková, Václava (advisor) ; Mandel, Martin (referee) ; Singer, Miroslav (referee)
The price level convergence of the transition economies towards the reference economies is linked to the relative price of nontradables, which is explained by the total factor productivity differentials in tradable and nontradable sector. Basic concept is offered by the Balassa Samuelson model and its modifications. Testable equations are derived from these models, and the panel data approach is applied for their estimation. The results indicate faster growth of the relative price of nontradables in transition economies as succession of higher growth rate of the total factor productivity in tradable sector. Hence estimated models confirm the price level convergence of transition economies towards the reference economies. The analyses of price dynamics of the complementary field, i. e. of the tradables, follows, and the basic concept is represented by the rational bubble hypothesis. The stress is putted on the impact of the word prices on the price levels of the Czech Republic. After a cointegration analysis of the time series is carried out, the influence of the word prices of tradable commodities is estimated within a vector error correction model and regression analysis. This cost factors analysis is afterwards related to the export dynamics of the Czech Republic, and models suitable for quantitative analysis of export dynamics as well as its prediction based on vector error correction model and regression analysis are evaluated. Their forecasting ability is assessed within a simulation of ex-post forecasts and a root mean squared error. The aim is to consider the relationship between the price levels and the export dynamics, for the relation of both variables evaluated within the Granger causality seems to be less straightforward then the standard export equations suggest, and the estimated equations confirm significant influence of the export dynamics on the price level.
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