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Stock market prediction using Twitter
Hynek, Jan ; Krištoufek, Ladislav (advisor) ; Křehlík, Tomáš (referee)
In this work I examine the short-time relationship of Twitter on the markets. I had been downloading English tweets in the period between 9th March and 4th April and also tweets containing words and hashtags "apple", "microsoft", "boe- ing", "cocacola". Afterwards, I investigate the predictive power of frequency of individal words on the marke using multinomial and binomial penalised logistic regression. I conclude that this method cannot be used for prediction, but can provide interesting insight ex-post. 1
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Do crypto-currencies form a new asset class?
Mayr, Samuel ; Krištoufek, Ladislav (advisor) ; Hanus, Luboš (referee)
This paper examines statistical properties of crypto-currencies' price variations in comparison with statistical properties of price variations in common financial markets. Price data of Bitcoin, ripple and Litecoin have been directly compared with price data of euro currency and stock index S&P500. Additionally, and compared with set of stylized facts of asset returns. The properties in scope of this work include an autocorrelation of day-to-day returns, a shape of return distributions, a volatility clustering, a leverage effect and a volume/volatility correlation. To answer the question of this thesis, we have tried to find unique differences in the way prices of crypto-currencies behave. After every point of the data analysis has been checked, we have concluded that the only major difference is in the shape and the significance of autocorrelation in day-to-day returns. While crypto-currencies seem to autocorrelate, there has been no such a cross-autocorrelation found in the benchmark values. Therefore, we argue that it is the most distinctive sign of crypto-currencies and the reason for crypto-currencies to be regarded as separate asset class. Powered by TCPDF (www.tcpdf.org)
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Electricity market: Analysis and prediction of volatility
Kunc, Vladimír ; Krištoufek, Ladislav (advisor) ; Hájek, Jan (referee)
Electricity market: Analysis and prediction of volatility Abstract Vladimír Kunc July 30, 2015 The last two decades can be characterized by restructuring of energy industry and the creation of new, competitive energy markets, where accurate forecasts of elec- tricity prices and price volatility are valuable both to consumers and producers. The aim of this work is to analyse several models for prediction of the price volatility of electricity on the Czech Electricity Day-ahead market on price data provided by OTE, a.s. for years 2009-2014. This work compares 144 different models' configura- tions for three distinct classes of models - autoregressive models, GARCH models, and artificial neural network models. This work provides comparison based on five different criteria, each describing the model in different way. Keywords: price prediction, volatility prediction, GARCH, neural networks, LSTM 1
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Forecasting Jump Occurrence in Czech Day-Ahead Power Market
Hortová, Jana ; Krištoufek, Ladislav (advisor) ; Kukačka, Jiří (referee)
The very specific features of the spot prices, especially occurrence of severe jumps, create a spot price risk for retailers who purchase electricity at unregulated highly volatile prices but resell it to consumers at fixed price. Therefore, it is of high im- portance to forecast whether jump is likely to occur during the next hour. However, to the best of our knowledge, such research has not been devoted to the Czech power market yet. Therefore, the aim of this thesis is to forecast the jump occurrence in the Czech day-ahead market. For this purpose we suggest four logit model spec- ifications, each containing various independent variables (for example, electricity demand, outside temperature, lagged price and various dummy variables) where the variable selection is supported by the previous literature and by the characteristic features of the spot prices. Within the in-sample period we compare the suggested models based on the values of pseudo-R squared and Bayesian information criterion. When evaluating the out-of sample performance of suggested models we apply jump prediction accuracy and confidence, but opposed to the previous literature we sug- gest a kind of sensitivity analysis which, to the best of our knowledge, has not be proposed by any other power research. JEL Classification C25, C32, C51,...
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Practical usage of optimal portfolio diversification using maximum entropy principle
Chopyk, Ostap ; Krištoufek, Ladislav (advisor) ; Kraicová, Lucie (referee)
"Practical usage of optimal portfolio diversification using maximum entropy principle" by Ostap Chopyk Abstract This thesis enhances the investigation of the principle of maximum entropy, implied in the portfolio diversification problem, when portfolio consists of stocks. Entropy, as a measure of diversity, is used as the objective function in the optimization problem with given side constraints. The principle of maximum entropy, by the nature itself, suggests the solution for two problems; it reduces the estimation error of inputs, as it has a shrinkage interpretation and it leads to more diversified portfolio. Furthermore, improvement to the portfolio optimization is made by using design-free estimation of variance-covariance matrices of stock returns. Design-free estimation is proven to provide superior estimate of large variance-covariance matrices and for data with heavy-tailed densities. To asses and compare the performance of the portfolios, their out-of-sample Sharpe ratios are used. In nominal terms, the out-of- sample Sharpe ratios are almost always lower for the portfolios, created using maximum entropy principle, than for 'classical' Markowitz's efficient portfolio. However, this out-of-sample Sharpe ratios are not statistically different, as it was tested by constructing studentized time-series...
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Time-scale analysis of sovereign bonds market co-movement in the EU
Šmolík, Filip ; Vácha, Lukáš (advisor) ; Krištoufek, Ladislav (referee)
The thesis analyses co-movement of 10Y sovereign bond yields of 11 EU mem- bers (Greece, Spain, Portugal, Italy, France, Germany, Netherlands, Great Britain, Belgium, Sweden and Denmark) divided into the three groups (the Core of the Eurozone, the Periphery of the Eurozone, the states outside the Eurozone). In the center of attention are changes of co-movement in the crisis period, especially near the two significant dates - the fall of Lehman Brothers (15.9.2008) and the day, when increase of Greek public deficit was announced (20.10.2009). Main contribution of the thesis is usage of alternative methodol- ogy - wavelet transformation. It allows to research how co-movement changes across scales (frequencies) and through time. Wavelet coherence is used as well as wavelet bivariate and multiple correlation. The thesis brings three main findings: (1) co-movement significantly decreased in the crisis period, but the results differ in the groups, (2) co-movement significantly differs across scales, but its heterogeneity decreased in the crisis period, (3) near to the examined dates sharp and significant decrease of wavelet correlation was observable across lower scales in some states. JEL Classification C32, C49, C58, H63 Keywords Co-movement, Wavelet Transformation, Sovereign Debt Crisis, Sovereign Bond Yields,...
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Statistical properties of the liquidity and its influence on the volatility prediction
Brandejs, David ; Krištoufek, Ladislav (advisor) ; Burda, Martin (referee)
This master thesis concentrates on the influence of liquidity measures on the prediction of volatility and given the magic triangle phenomena subsequently on the expected return. Liquidity measures Amihud Illiquidity, Amivest Liquidity and Roll adjusted for high frequency data have been utilized. Dataset used for the modeling was consisting of 98 shares that were traded on S&P 100. The time range was from 1st January 2013 to 31st December 2014. We have found out that the liquidity truly enters into the return-volatility relationship and influences these variables - the magic triangle interacts. However, contrary to our hypothesis, the model shows up that lower liquidity signifies lower realized risk. This inference has been suggested by all three models (3SLS, 2SLS and OLS). Furthermore, we have used the realized variance and bi-power variation to separate the jump. Our second hypothesis that lower liquidity signifies higher frequency of jumps was confirmed only for one of two liquidity proxies (Roll) included in the resulting logit FE model. Keywords liquidity, risk, volatility, expected return, magic triangle, price jumps, realized variance, bi-power variation, three-stage least squares model, logit, high-frequency data, S&P 100 Author's e-mail david.brandejs@seznam.cz Supervisor's e-mail...
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Efficient market hypothesis in the modern era
Vlček, Šimon ; Krištoufek, Ladislav (advisor) ; Korbel, Václav (referee)
Efficient Market Hypothesis (EMH) has been the central assumption of financial modelling in the previous decades. At its core, it is a statement about the efficient incorporation of available information in the prices of assets, rendering each price a 'true' representation of the asset's intrinsic value. The notion of informationally efficient financial markets has been, since its formulation, entrenched in the very core of our understanding of how asset pricing works, yet, with ever so increasing frequency, when subjected to empirical scrutiny, it fails to prove its explanatory and predictive prowess. New academic strands emerged have emerged as a result, attempting to explain those empirical short-comings, with rather mixed results. The new models and theories often either explain a singular anomaly, rather than pro- viding a generalized and consistent theoretical framework, or are exclusive with the general state of financial markets, which tends to be efficient and rational. This thesis shall explore the relationship of information and financial mar- kets, taking into account developments that have occurred since the inception of the EMH. Subsequently it will present a new theoretical model for asset pric- ing and ipso facto the efficiency of financial markets, based on meta-analysis of information, along...
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Are financial returns and volatility multifractal at all?
Sedlaříková, Jana ; Krištoufek, Ladislav (advisor) ; Kraicová, Lucie (referee)
Over the last decades, multifractality has become a downright stylized fact in financial markets. However, its presence has not been adequately statistically proved. The main aim of this thesis is to contribute to the discussion by an ex- tensive statistical analysis of the problem. We investigate returns and volatility of the collection of the four stock indices employing the three popular methods: the GHE, the MF-DFA, and the MF-DMA method. By comparing the results of the original series to those for simulated monofractal series, we conclude that stock market returns as well as volatility exhibit a multifractal nature. Additionally, in order to understand the origin of underlying multifractality, we study vari- ous surrogate series. We found that a fat-tailed distribution significantly affects multifractality. On the other, we were not able to confirm the impact of time correlations as the results strongly depend on the applied model. JEL Classification F12, G02, G10, C12, C22, C49, C58 Keywords econophysics, multifractality, financial markets, Hurst exponent Author's e-mail jana.sedlarikova@gmail.com Supervisor's e-mail kristoufek@ies-prague.org
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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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