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Portfolio selection in factor investing
Hronec, Martin ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
This thesis empirically examines the role of advanced portfolio selection methods in factor investing. These methods provide more efficient exposure to underlying risk sources in factor portfolios. Their performance is evaluated across number of prominent factors and compared with more naive equal- and value- weighting, typically used in asset pricing literature as well commercial investment vehicles. The most diversified portfolio consistently achieves the highest returns, while having only moderate volatility and one of the lowest tail risk exposure. On the other hand, the diversified risk parity portfolio suffers high volatility as well as the greatest tail risk exposure, while achieving only comparable average returns with other strategies. 1
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Comparison of different models for forecasting of Czech electricity market
Kunc, Vladimír ; Krištoufek, Ladislav (advisor) ; Kopečná, Vědunka (referee)
There is a demand for decision support tools that can model the electricity markets and allows to forecast the hourly electricity price. Many different ap- proach such as artificial neural network or support vector regression are used in the literature. This thesis provides comparison of several different estima- tors under one settings using available data from Czech electricity market. The resulting comparison of over 5000 different estimators led to a selection of several best performing models. The role of historical weather data (temper- ature, dew point and humidity) is also assesed within the comparison and it was found that while the inclusion of weather data might lead to overfitting, it is beneficial under the right circumstances. The best performing approach was the Lasso regression estimated using modified Lars. 1
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Prediction of Stock Return Volatility Using Internet Data
Juchelka, Tomáš ; Krištoufek, Ladislav (advisor) ; Novák, Jiří (referee)
The thesis investigates relationship between daily stock return volatility of Dow Jones Industrial Average stocks and data obtained on Twitter, the social media network. The Twitter data set contains a number of tweets, categorized according to their polarity, i.e. positive, negative and neutral sentiment of tweets. We construct two classes of models, GARCH and ARFIMA, where for either of them we research basic model setting and setting with additional Twitter variables. Our goal is to compare, which of them predicts the one day ahead volatility most precisely. Besides, we provide commentary regarding the effects of Twitter volume variables on future stock volatility. The analysis has revealed that the best performing model, given the length and structure of our data set, is the ARFIMA model augmented on Twitter volume residuals. In the context of the thesis, Twitter volume residuals represent unexpected activity on the social media network and are obtained as residuals from Twitter volume autoregression. Plain ARFIMA model was the second best and plain volume augmented ARFIMA was in third place. This means that all three ARFIMA models outperformed all three GARCH models in our research. Regarding the Twitter estimation parameters, we found that higher the activity the higher tomorrow's stock...
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Portfolio Construction Using Hierarchical Clustering
Fučík, Vojtěch ; Krištoufek, Ladislav (advisor) ; Baruník, Jozef (referee)
Hlavním cílem této práce je vyložit a zejména propojit existující metodologii filtrování korelačních matic, grafových algoritmů aplikovaných na minimální kostry grafu, hierarchického shlukování a analýzy hlavních komponent, pro vytvoření kvantitativních investičních strategií. Namísto tradičního použití časových řad akciových výnosů je užito reziduí z faktorových modelů. Tato rezidua jsou klíčovým vstupem pro všechny používané algoritmy k výpočtu pravděpodobnosti středovosti dané akcie. Pravděpodobnost středovosti je nekonvenční ukazatel pravděpodobnosti, kde hodnota blízko 1 značí vysokou pravděpodobnost středovosti dané akcie v dané ekonomické síti. Na základě této míry pravděpodobnosti je vybudováno několik investičních strategií, které jsou dále testován hlavních amerických akciových indexů. Nemůže být generalizováno, že periferní strategie dosahují konzistentně lepších výsledků než středové strategie. Zatímco při použití klasického Markowitzova optimalizačního procesu jsou zisky stabilní a potenciál průměrný, oba typy vybudovaných strategií (středové i periferní) sdílí vysoký potenciál zisku, který je ovšem vykoupen vysokou volatilitou.
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Spillovers between low and high risk assets during business cycle
Matyáš, Jan ; Krištoufek, Ladislav (advisor) ; Kukačka, Jiří (referee)
1 Abstract This master thesis examines linkages among bond and stock markets in Ger- many, Austria and Italy. For the purpose of analysis of return spillovers, we use Spillover index framework which enables us to describe development of inter- market linkages over time. The data used in the study includes the period from January 2nd, 1998 to May 23rd, 2017 which allows us to estimate long- term development of spillovers among markets. We find unequal link between stocks and bonds and increase in co-integration of markets during the financial crisis of 2007-2008 with significant persistence after the crisis. Mechanism of transmission of financial shocks among European countries is affected by eco- nomic and political integration of countries. We identify strong interlinkages of markets with substantial influence of Italian assets in transmitting shocks to German and Austrian assets, especially during periods of economic distress. On the other hand, Germany represents an open economy that is increasingly integrated to other markets. Scale of return spillovers is highly dependent on economic situation which is evident from clustering of high spillovers during recessions and a great deal of persistence of these interdependencies. JEL Classification G01, G12, G15, C63, C67 Keywords return spillovers, asset...
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Relationship between Stock Returns and Net Income: Evidence from U.S. Market
Kolář, Michal ; Kočenda, Evžen (advisor) ; Krištoufek, Ladislav (referee)
It is important to know if earnings variables influence stock returns. This is important not just for investors who want to know what drives stock returns, but also for the overall economy as stock returns and stock markets are also considered to be significant indicators of its performance. Many studies were conducted in the past but with inconclusive results. The aim of the thesis is to examine the relationship between net income and stock returns using two approaches, namely panel data model and multiple linear regression. We utilize a dataset of companies selected from the S&P500 Index. We also analyse possible heterogeneity in cross section and time. Moreover, we incorporate additional factors which have been proven to have significant explanation power for stock returns. Our findings from the panel data estimation suggest that there is no relationship between scaled net income and stock returns. We find there are random effects present between the companies and three structural breaks in time. Furthermore, we explore the significance of the consumer sentiment index and the percentage change in the book value per share variables in the panel estimation. We do not confirm the debt to equity ratio and the GDP growth news factors in the panel estimation as significant. Results concerning the...
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Predicting Field Experiment Results in a Lab
Chadimová, Kateřina ; Cingl, Lubomír (advisor) ; Krištoufek, Ladislav (referee)
This thesis is aimed at forecasting of experimental results in a lab environment, investigating often discussed external validity of laboratory experiments. We run a novel laboratory experiment in which the subject pool is asked to make predictions on results of a certain field experiment. The collected data is ana lyzed using different accuracy measures, arriving at several interesting results. First, the forecast among the 94 subjects is quite informative about the actual treatment effects although its accuracy substantially varies based on a type of accuracy measure and a particular treatment. Second, the average forecast is either more accurate or at least comparable to the mean individual forecast, proving the presence of "wisdom-of-crowds" effect.
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Can the stock markets predict changes in macroeconomic variables?
Vařeka, Marek ; Krištoufek, Ladislav (advisor) ; Hayat, Arshad (referee)
A bstract There is a consensus in the literature, that the stock market can predict the Gross domestic product on quarterly base or the industrial production, which is good proxy for GDP, on monthly basis and that the causal rela tionship between stock market and GDP should work both ways. However, using Vector autoregression model on US data since 1950, model shows that the stock market can not only predict the Industrial production on monthly basis, but also ISM non-manufacturing index, which is a good proxy for services in the economy. Furthermore I have managed to prove, that the unemployment can be predicted by past realizations of the stock market and managed to explain almost one third of all variations in change in un employment using S&P500 and oil prices during last 20 years. The Granger causality test concluded that stock market does cause the unemployment but not vice versa, at least during last 20 years.
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Visualization of changes in correlations of stock returns during and after financial crisis
Zbožínek, David ; Krištoufek, Ladislav (advisor) ; Hauzr, Marek (referee)
This thesis aims to describe structural changes in US stock markets during and after global financial crisis. We utilize correlation coefficients of logar- ithmic differences in daily closing prices to generate correlation networks. Minimal spanning tree and hierarchical tree are used to filter out less im- portant information from correlation network, and thus they enable us to obtain unique taxonomy of stocks. Daily closing prices from 8 June 2007 to 31 December 2010 for 73 constituents of market index S&P 100 are di- vided into nine 100 trading-days-long time intervals. The effect of market shock after the fall of Lehman Brothers on 15 September 2008 is investigated. Minimal spanning tree significantly shrinks in the period from 15 September 2008 to 7 January 2009 and afterwards, it gradually reverts back to its pre- crisis state. We also describe clustering patterns of stocks and their changes during the crisis. Clusters of companies from financial, energy, and utilit- ies sectors are recognized in most time windows with only slight variations. In the time window after 15 September 2008, several topological shifts are identified. Additionally, companies from industrials sector are found to form significantly larger clusters in time windows following 8 January 2009.
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Short-term electricity price forecasting - evaluation of selected hybrid models
Svoboda, Štěpán ; Krištoufek, Ladislav (advisor) ; Jonášová, Júlia (referee)
In this thesis a thorough study of the previous literature and the division and special aspects of EPF was carried out. Then the evaluation and comparison of several models was done - the ARIMA, SVR, SVRARIMA and PSF model. This comparison was done on the intra-day Nord Pool market, which is quite unique as almost all short-term EPF is carried out on the day-ahead market. Our results are robust as the modeling was done on 100 test periods and we have tested the difference in predictive accuracy using the modified DM test. Our conclusion is the PSF model is inadequate in our intra-day set- ting and the overall ARIMA model seems to outperform the SVR and SVRARIMA model somewhat. The dominance of ARIMA is not very strong and a further investigation of the causes of these results can better illuminate the strengths and weaknesses of these models.
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