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Stock Ownership Structure and Related Risk Premium
Rosický, Ondřej ; Baruník, Jozef (advisor) ; Kočenda, Evžen (referee)
Goal of this thesis is to discover the possible risk premium for stocks with respect to their ownership structure. We work with two types of investors, retail and institutional. Those types of investors have different expectations, preferences and behave differently in certain market events. We built the long-short IMR (institutional minus retail) factor as difference in returns of top and bottom portfolios based on proportion of institutional ownership and added this factor to Fama and French Three Factor Model. There is approximately 0.23 % risk premium for stocks with high share of institutional owners. Further we also try to find the possible impact of nominal stock price on ownership structure. With higher nominal price there is higher institutional ownership. On the other hand, this impact is negligible for low and high percentage share of institutional ownership, therefore IMR factor could not be substituted by the nominal stock price. Lastly, we tried to discover what causes the abnormal returns after the execution date. We found out that with increase in retail ownership by 1 p.p., the abnormal returns are higher in one week after stock split execution date by 0.8 p.p. That is in line with earlier discovered risk premium because with the decrease in the portion of institutional ownership...
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Can Machines Explain Stock Returns?
Chalupová, Karolína ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
Can Machines Explain Stock Returns? Thesis Abstract Karolína Chalupová January 5, 2021 Recent research shows that neural networks predict stock returns better than any other model. The networks' mathematically complicated nature is both their advantage, enabling to uncover complex patterns, and their curse, making them less readily interpretable, which obscures their strengths and weaknesses and complicates their usage. This thesis is one of the first attempts at overcoming this curse in the domain of stock returns prediction. Using some of the recently developed machine learning interpretability methods, it explains the networks' superior return forecasts. This gives new answers to the long- standing question of which variables explain differences in stock returns and clarifies the unparalleled ability of networks to identify future winners and losers among the stocks in the market. Building on 50 years of asset pricing research, this thesis is likely the first to uncover whether neural networks support the economic mechanisms proposed by the literature. To a finance practitioner, the thesis offers the transparency of decomposing any prediction into its drivers, while maintaining a state-of-the-art profitability in terms of Sharpe ratio. Additionally, a novel metric is proposed that is particularly suited...
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Bias and Accuracy in Equity Research: The Case of CFA Challenge
Hloušek, Pavel ; Novák, Jiří (advisor) ; Máková, Barbora (referee)
This thesis analyses drivers of optimistic bias in equity research and substance of ability in explaining differences in accuracy among equity analysts. I have shown the existence of a relevant reason for optimistic bias in equity research, which is not related to conflict of interest - the usually referred driver of the bias. Then I have supported the stream of literature showing that analyst's ability is not a strong determinant of analyst's accuracy. A new perspective on the topics is offered by using a sample of equity reports from valuation competition CFA Research Challenge. Contribution of the thesis lies (i) in working with a sample of analysts who do not face the conflicts of interest proposed by the literature to be causing optimistic bias, which offers a unique opportunity to test whether such conflict-of- interest-free analysts issue biased recommendations and in (ii) using success in CFA Challenge as a new proxy for ability of equity analysts. The methods used are an analysis of bias and accuracy of target prices, hit-ratio of investment recommendations, and analysis of returns - estimated by CAPM, Fama French three-factor model and Carhart four-factor model.
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Investment horizon in the CAPM: A comparison of a wavelet-based decomposition and the fractal regression
Spousta, Radek ; Krištoufek, Ladislav (advisor) ; Vácha, Lukáš (referee)
This thesis study two promising methods used to define the multiscale CAPM - the wavelet-based decomposition and the fractal regression. Their estimates, obtained on monthly excess return on ten portfolios formed on beta in the US market, are compared in the period from November 2000 to October 2020 and, subsequently, in the period from November 1965 to October 2020. In the first period, the multiscale beta is not significantly different from the original single-scale beta for most of the portfolios. Contrary, both methods uncover significant multiscale behavior of the beta in the second period. Specifically, the high-beta portfolios have higher multiscale beta at longer investment horizons, mainly at wavelet scale 3 and scales 12-24 of the fractal regression. Overall, both methods deliver consistent results, and seem suitable for extending the CAPM with an investment horizon. JEL Classification Keywords G12, C20 CAPM, asset pricing, multiscale analysis, wavelets, fractal regression Title Investment horizon in the CAPM: A comparison of a wavelet-based decomposition and the fractal regression
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Can Machines Explain Stock Returns?
Chalupová, Karolína ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
Can Machines Explain Stock Returns? Thesis Abstract Karolína Chalupová January 5, 2021 Recent research shows that neural networks predict stock returns better than any other model. The networks' mathematically complicated nature is both their advantage, enabling to uncover complex patterns, and their curse, making them less readily interpretable, which obscures their strengths and weaknesses and complicates their usage. This thesis is one of the first attempts at overcoming this curse in the domain of stock returns prediction. Using some of the recently developed machine learning interpretability methods, it explains the networks' superior return forecasts. This gives new answers to the long- standing question of which variables explain differences in stock returns and clarifies the unparalleled ability of networks to identify future winners and losers among the stocks in the market. Building on 50 years of asset pricing research, this thesis is likely the first to uncover whether neural networks support the economic mechanisms proposed by the literature. To a finance practitioner, the thesis offers the transparency of decomposing any prediction into its drivers, while maintaining a state-of-the-art profitability in terms of Sharpe ratio. Additionally, a novel metric is proposed that is particularly suited...
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Multi-horizon equity returns predictability via machine learning
Nechvátalová, Lenka ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictabil- ity of returns using neural networks models decreases with longer forecasting horizon. We also document the profitability of long-short portfolios, which were created using predictions of cumulative returns at various horizons, be- fore and after accounting for transaction costs. There is a trade-off between higher transaction costs connected to frequent rebalancing and greater returns on shorter horizons. However, we show that increasing the forecasting hori- zon while matching the rebalancing period increases risk-adjusted returns after transaction cost for the U.S. We combine predictions of expected returns at multiple horizons using double-sorting and buy/hold spread, a turnover reduc- ing strategy. Using double sorts significantly increases profitability on the U.S. sample. Buy/hold spread portfolios have better risk-adjusted profitability in the U.S. JEL Classification G11, G12, G15, C55 Keywords Machine learning, asset pricing, horizon pre- dictability, anomalies Title Multi-horizon equity returns predictability via machine learning
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Are realized moments useful for stock market returns analysis?
Saktor, Ira ; Baruník, Jozef (advisor) ; Kočenda, Evžen (referee)
This thesis analyzes the use of realized moments in asset pricing. The analysis is done using dataset containing log-returns for 29 of the most traded stocks and covering 10 years of data. The dataset is split into training set covering 7 years and test set covering 3 years of data. For each of the stocks a separate time series model is estimated. In evaluation of the quality of the models, metrics such as RMSE, MAD, accuracy in forecasting the sign of future returns, and returns achievable by executing trades based on the recommendations from the model are used. Even though the inclusion of realized moments does not provide significant improvements in terms of RMSE, it is found that realized skewness and kurtosis significantly contribute to explaining the returns of individual stocks as they lead to consistent improvements in identifying future positive, as well as negative, returns. Moreover, the recommendations from the models using realized moments can help us achieve significantly higher returns from trading stocks. Inclusion of the interaction terms for variance and returns, skewness and returns, and kurtosis and variance, provides additional improvement of forecasting accuracy, as well as improvements in returns achievable by executing transactions based on recommendations from the model....
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Fama-French Model: Multiscale Portfolio Analysis
Spousta, Radek ; Kraicová, Lucie (advisor) ; Teplý, Petr (referee)
This thesis studies the empirical relationship between excess asset returns and the Fama−French risk factors at various scales using a combination of the Fama−French model and wavelet-based methods. We re-examine previously published results obtained for six portfolios formed on size and book-to-market ratio in the U.S. market, and focus on the influence of different scales on the original results. We conclude that the most the total variance of the risk factors and excess portfolio returns is concentrated at scale 1 and 2, which corresponds to periodicities of 2-4 months and 4-8 months, respectively. Next, we observe significant variation in estimated parameters across different scales. Furthermore, some of the Fama−French risk factors are strongly correlated at scale 2, 3 and 4, which is unobservable in standard correlation matrix. Overall, the multiscale approach seems beneficial for analysis of the Fama−French three-factor model as it reveals information that remains hidden to traditional methods.
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The Impact of Mergers and Acquisition Activity on the Time Series Variation in the Stock Size Premium
Kaplan, Robert ; Novák, Jiří (advisor) ; Geršl, Adam (referee)
This work studies whether intertemporal variation in future takeover activity explains intertemporal changes in stock size premium. Taking into account that takeover activity involves 2-9% of firms every year and building upon existing research stating that small firms are more likely takeover targets, receive 40% higher takeover premium than large firms, we argue that small firms benefit from high takeover activity more than large firms and size premium should be more pronounced in the time of high takeover activity. We study takeover activity as well as stock size premium on aggregate level and test whether size premium can be explained by the expected takeover activity, i.e. its change compared to past. We find that change in takeover activity in the next six months versus last six months is positively correlated with size premium. Additionally, we construct a simple predictive model for estimating future takeover activity. The relation between size premium and change in takeover activity remains significant when we use forecasted values given by the predictive model instead of true future values in the model.
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Bias and Accuracy in Equity Research: The Case of CFA Challenge
Hloušek, Pavel ; Novák, Jiří (advisor) ; Máková, Barbora (referee)
This thesis analyses drivers of optimistic bias in equity research and substance of ability in explaining differences in accuracy among equity analysts. I have shown the existence of a relevant reason for optimistic bias in equity research, which is not related to conflict of interest - the usually referred driver of the bias. Then I have supported the stream of literature showing that analyst's ability is not a strong determinant of analyst's accuracy. A new perspective on the topics is offered by using a sample of equity reports from valuation competition CFA Research Challenge. Contribution of the thesis lies (i) in working with a sample of analysts who do not face the conflicts of interest proposed by the literature to be causing optimistic bias, which offers a unique opportunity to test whether such conflict-of- interest-free analysts issue biased recommendations and in (ii) using success in CFA Challenge as a new proxy for ability of equity analysts. The methods used are an analysis of bias and accuracy of target prices, hit-ratio of investment recommendations, and analysis of returns - estimated by CAPM, Fama French three-factor model and Carhart four-factor model.
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