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Short-term Electric Load Forecasting Using Czech Data
Řanda, Martin ; Krištoufek, Ladislav (advisor) ; Čech, František (referee)
Forecasting electric load accurately is a critical prerequisite to dependable power grid operation. It is thus in the best interests of the responsible institutions to develop and maintain performant models for predicting load. In this thesis, we analyze Czech electric load data and execute three pseudo-out-of-sample forecasting exercises. We employ standard econometric as well as machine learning methods and compare the results to benchmarks, including the predictions published by the Czech transmission system operator. The results of the first task examining the predictability of minute loads using 11 years of data indicate that the high-frequency load series is predictable. In the second and third exercises, we utilize hourly loads with additional explanatory variables. We generate one-step-ahead and 48-hours-ahead forecasts on the 2021 out- of-sample set and evaluate the performance of several methods. In both exercises, the most accurate results are produced by averaging forecasts of our specified recurrent neural network and the seasonal autoregressive integrated moving average model, achieving a mean absolute percentage error of less than 0.5% on the out-of-sample set in the one-step-ahead analysis and 2.3% in the 48-hours-ahead exercise, outperforming the operator's predictions.
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The impact of oil-related events on volatility spillovers across oil-based commodities
Bartušek, Daniel ; Kočenda, Evžen (advisor) ; Krištoufek, Ladislav (referee)
Although oil-based commodities play a crucial role in the world from an indus- trial perspective, their prices are often heavily influenced by the occurrence of various events covered in the news. These events often trigger a sudden increase in volatility, that spills across all oil-based commodities. As a result, it becomes riskier to invest in this group of commodities. Furthermore, the increase in oil price volatility introduces friction in oil trade due to pricing uncertainty. In this thesis, we processed over 900 events related to oil from 1978 to 2022 and grouped them based on a set of repeating characteristics. Utilizing a novel bootstrap- after-bootstrap econometric framework developed by Greenwood-Nimmo et al. (2021), we identified over 20 historical events that triggered a sudden and per- sistent rise in volatility connectedness. We discover that geopolitical events are twice as likely to cause an increase in volatility spillovers than economic events. We did not find evidence for natural events influencing oil volatility spillover levels. Furthermore, a majority of the events after which the spillover levels increased share three common characteristics: they are negative, unexpected, and introduce fear of oil supply shortage. Investors and policymakers can use our findings to assess the...
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Do Left-handers and Left-footers Have a Competitive Advantage in Sports?
Hadžić, Aner ; Krištoufek, Ladislav (advisor) ; Pavlovová, Anna (referee)
Left-sided athletes are often perceived as better performing as they can leverage their minority status within the sports world. While various specific left-sided athletes, such as Lionel Messi and Rafael Nadal, perform at the very top of their disciplines, these might be simply non-representative outliers. The current thesis puts the hy- pothesis of left-sided over-performance to test via a battery of tests and regressions. My thesis thoroughly analyses the prevalence and the performance of left-handed/left-footed athletes across 5 differ- ent sports. As majority of the current studies are focusing only on a few performance metrics in the given sport, my work broad- ens the knowledge on the topic since it compares the performance of left-sided and right-sided athletes in many categories in order to cover a great portion of the in-game action. Furthermore, this thesis also expands the current understanding of the (potential) left-sided advantage in direct encounters between both teams and individu- als, achieving so by implementing predictive Bradley-Terry models that are based on past matches. The overall results are rather sur- prising: in the majority of the performance comparisons between left-handers/left-footers and right-handers/right-footers, no signifi- cant difference between the two...
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Cluster-based asset allocation strategies during market stress periods
Zacharová, Beáta ; Krištoufek, Ladislav (advisor) ; Čech, František (referee)
This thesis empirically examines the alternatives to traditional asset allocation strategies based on clustering mechanisms. Portfolio selection strategies utilizing hierarchical clustering are compared to the market benchmark and traditional methods: minimum-variance and equally weighted allocation, focusing on market stress periods. The allocation strategies are tested on daily stock prices of the S&P 100 index constituents from 2005 to 2021. The performance of Hierarchical Risk Parity (HRP) and Hierarchical Equal Risk Contribution (HERC) portfolios is evaluated across several market stress periods, including the financial crisis of 2007-2008 and the global coronavirus (COVID-19) pandemic in 2020. Empirical results do not prove the superiority of hierarchical clustering allocation strategies over traditional strategies in risk-adjusted performance. JEL Classification G01, G10, G11 Keywords portfolio selection, hierarchical clustering, HRP, HERC, market stress Title Cluster-based asset allocation strategies during market stress periods
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Non-Fungible Tokens (NFTs): A hype or hope? Analysis of random NFT portfolios
Iordosopol, Ana ; Krištoufek, Ladislav (advisor) ; Nechvátalová, Lenka (referee)
This thesis reflects on the newly emerged alternative asset class of non-fungible tokens (NFTs). We perform both qualitative and quantitative analyses on the matter. In the empirical part, we construct different types of random portfolios to investigate the performance of cryptocurrency- based portfolios after the possible inclusion of NFTs in such. Our results suggest that as of the end of 2022, portfolios of Bitcoin and Ether perform better without NFTs, thus rejecting the previous assumptions of limited diversification potential of NFTs, which was detected during the last crisis period during the COVID-19 pandemic. The qualitative analysis on the topic, however, suggests that NFTs are not just the hype and the innovative blockchain solutions that NFTs represent may be of greater use in the near future. Therefore, despite of non-efficiency of NFTs as a financial asset in 2022, they still display significant potential as a disruptive technology. Keywords NFT, Cryptocurrency, Random portfolio, Blockchain, Non-fungible token. Title Non-Fungible Tokens (NFTs): A hype or hope? Analysis of random NFT portfolios.
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Can Bitcoin serve as an inflation hedge in the USA, Euro area, and Czech markets?
Volkov, Aleksandr ; Krištoufek, Ladislav (advisor) ; Šestořád, Tomáš (referee)
Since the 1970s, economists have started studying the concept of inflation hedging as a way to protect investments. With the recent high inflation rates, investors might be interested if newly created assets such as cryptocurrencies can be effective against inflation. This thesis paper aims to find out whether thelargest crypto asset Bitcoin can be used as an inflation hedge. To answer this question, Fisher coefficient estimation and hedging demand for the US, Euro Area, and the Czech Republic for the period between November 2014 and October 2022 will be analyzed. In addition, the vector autoregressive model (VAR)will be used for the US market in the same time frame. The results showed overall positive Bitcoin returns but all three methods indicated no or negative correlation between inflation rates in three regions and Bitcoin returns. The thesis paper concludes that Bitcoin cannot be used as an inflation hedge as notall requirements are met. Keywords Cryptocurrency, Bitcoin, gold, inflation, inflation hedge, Fisher coefficient,VAR model Title Can Bitcoin serve as an inflation hedge asset in the US, Euro Area, andCzech markets?
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Gamified Stock Markets, Sentiment and Volatility: Evidence from the GameStop frenzy
Tran Nguyen, Thai Nhat Phi ; Krištoufek, Ladislav (advisor) ; Kočenda, Evžen (referee)
In this thesis, we study the impact of individual retail investors on the financial markets. We follow the GameStop retail trading frenzy from the beginning of 2021 and the retail investors aggregated on Reddit's r/wallstreetbets. The tools employed include natural language processing, wavelet analysis and vector error correction models. The results propose that the retail investor sentiment is highly susceptible to high volatility, extreme returns and frequent news coverage. Social media is shown to exacerbate these behavioural tendencies. We find evidence that retail investor sentiment is able to predict short-term returns for stocks specifically targeted by retail investors. The findings are, however, dependent on the investment horizon. Over long horizons, we find evidence for the reversal of the relationship. Lastly, while the effect of news and social media is similar in the long run, we show that Reddit sentiment, as opposed to news sentiment, is a significant predictor of retail targeted stocks in the near term. JEL Classification C55 C58, G12, G14, G41 Keywords Sentiment, Social media, GameStop, Reddit, Natural language processing, Wavelet analysis Title Gamified Stock Markets, Sentiment and Volatility: Evid- ence from the GameStop frenzy 1
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Price gaps in the stock market
Vosmanský, Jakub ; Krištoufek, Ladislav (advisor) ; Vácha, Lukáš (referee)
This thesis aims to scrutinise price gaps in the stock market. The key objective is to analyse candlestick charts surrounding price gaps and determine whether any patterns accompany their presence. Firstly, the thesis briefly describes candlestick patterns, literature relevant to price gaps and Convolutional Neural Network (CNN) as the method of choice. Price gaps are studied in a 5-minute time frame in the data of all S&P 500 constituents in the years from 2015 to 2021. By feeding images of the candlestick chart into the CNN, the proposed model reaches an Accuracy of 74.2% in predicting whether a future price will be higher or lower than the price at the gap. This result can be translated into a statement that the CNN detects hidden patterns around the price gaps. Furthermore, the thesis finds that these patterns di er across individual stocks. The thesis also shows that including news sentiment in the analysis does not improve the ability to discover patterns. JEL Classification C45, C55, C88, G14, G15, G41 Keywords price gap, convolutional neural network, pattern detection, news sentiment Title Price gaps in the stock market
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Consequences of Implementation of Video Assistant Referee in Fortuna Liga
Habáň, Ondřej ; Krištoufek, Ladislav (advisor) ; Nevrla, Matěj (referee)
The thesis deals with the issue of the Video Assistant Referee in football. It evaluates the consequences of its implementation in Czech Fortuna Liga on the sample of 678 matches held during two and half seasons. The results from the models designed to treat count data were compared with relevant literature. In the form of both simple and multiple regression with additional control variables was investigated the relationship between VAR and the set of match- changing incidents, including yellow cards, red cards and penalty kicks, and the relationship between VAR and errors of on-pitch referees. The terms presence of VAR, VAR interventions and VAR as the whole were di erentiated. Whereas a significant statistical association of VAR as the whole was not revealed for yellow and red cards, a 56% increase in the number of penalties associated with VAR as the whole significantly performed. Furthermore, the negative and highly significant 118% association of the presence of VAR was reckoned in the case of errors of on-pitch referees. Subsequently, the percentage decreased due to VAR interventions, however, not su ciently to reveal a negative and significant association in errors of on-pitch referees for VAR as the whole. The exception created errors based on factual decisions. JEL Classification Z21, F21, Z29,...
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