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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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Climate Change Risk Premium, Stock Returns and Volatility Analysis in Relation to ESG Score
Barotov, Timur ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
The purpose of this study is to provide the evidence in regards to how the ESG score integration in the investment strategies affects the stock portfolio performances. The 10 year long panel data on European stocks were used to test how does the corporate ESG score correlate with returns and volatility on corporate stocks and does it (if at all) hold any explanatory power if added to popularly used asset pricing models. Data sample was divided in two based on long and short ESG reporting periods, where on each the analysis was performed separately. Furthermore, both the single sort and double sort analyses were performed to isolate size and ESG effects. Using Fama-MacBeth regression the results seem to suggest that investors are already pricing in the climate related risks as shown by the negative risk premium associated with high ESG firms. Returns and volatility of corporate stocks tend to be lower with higher ESG score, although not uniformly nor very significantly. Comparing Leaders portfolio showed that high (European) ESG scorers underperfomed S&P 500 index both in terms of return and volatility.
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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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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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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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Debt Contracts and Stochastic Default Barrier
Dózsa, Martin ; Janda, Karel (advisor) ; Krištoufek, Ladislav (referee)
This thesis focuses on the theory of asset pricing models and their usage in the design of credit contracts. We describe the evolution of structural models start- ing from the basic Mertonian framework through the introduction of a default barrier, and ending with stochastic interest rate environment. Further, with the use of game theory analysis, the parameters of an optimal capital struc- ture and safety covenants are examined. To the author's best knowledge, the first EBIT-based structural model is built up that considers stochastic default barrier. This set-up is able to catch the different optimal capital structures in various business cycle periods, as well as bankruptcy decisions dependent on the state of the economy. The effects of an exogenous change in the risk-free interest rate on the asset value, probability of default, and optimal debt ratio are also explained. JEL Classification C73, G12, G32, G33 Keywords credit contracts, stochastic default barrier, asset pricing, EBIT-based models, struc- tural models Author's e-mail martin@dozsa.cz Supervisor's e-mail Karel-Janda@seznam.cz Abstrakt Tato práce se zabývá teoretickými modely pro oceňování finančních aktiv a je- jich použitím při návrhu optimálních úvěrových smluv mezi dlužníky a věřiteli. V první části je popsán...
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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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Asset prices and macroeconomics: towards a unified macro-finance framework
Maršál, Aleš ; Horváth, Roman (advisor) ; Holub, Tomáš (referee) ; Kónya, István (referee) ; Pástor, Luboš (referee)
Asset prices and macroeconomics: towards a unified macro-finance framework Aleš Maršál March 30, 2020 Abstract The dissertation consists of three papers focused on fiscal policy and explaining what determines the dynamics of cross-sectional distribution of bond prices. The connecting factor of the thesis is however not just its main theme but also the used methodology. The valuation of bonds and effects of studied policies are endogenous outcome of the full-fledged macro-finance dynamic stochastic general equilibrium model. The first chapter provides broader context and non-technical summary of the three papers in following chapters. The first paper studies the role of trend inflation in bond pricing. Motivated by recent empirical findings that emphasize low-frequency movements in inflation as a key determinant of term structure, we introduce trend inflation into the workhorse macro-finance model. We show that this compromises the earlier model success and delivers implausible busi- ness cycle and bond price dynamics. We document that this result applies more generally to non-linearly solved models with Calvo pricing and trend inflation and is driven by the behavior of price dispersion, which is i) counterfactually high and ii) highly inaccurately approximated. We highlight the channels be- hind the undesired performance...
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