Portfolio selection in factor investing

Táto práca empiricky skúma úlohu pokročilých metód konštrukcií portfólia pri faktorovom investovaní. Tieto metódy umožňujú efektívnejšie zachytenie zdrojov rizika vo faktorových portfóliach. Ich výkonnosť je hodnotená naprieč mnohými faktormi a porovnáva sa s naivnejšími metódami, ktoré sa zvyčajne používajú v literatúre o oceňovacích anomáliach a faktorových indexoch. Na- jviac diverzifikované portfólio konzistentne dosahuje najvyššie výnosy, pričom má iba miernu volatilitu a jedno z najnižších rizík s nízkou pravdepodobnosťou výskytu. Na druhej strane, portfólio diferzifikovanej parity rizika trpí vysokou volatilitou a najväčšou expozíciou na riziká s nízkou pravdepdoobnosťou výskytu, pričom dosahuje len porovnateľne priemerné výnosy s inými stratégiami. 1This 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. 1Institut ekonomických studiíInstitute of Economic StudiesFaculty of Social SciencesFakulta sociálních vě

Spotrebitelia v prostredí zeleného marketingu a greenwashingu

A significant problem that is emerging in the discursive practices of green marketing is the rise of greenwashing; companies providing irrelevant, overstated or false information about a product's sustainable attributes. This raises a number of issues that have not yet been rigorously investigated. This study uses focus group discussions to provide one of the first assessments of the impact of greenwashing on consumers. Three semi-structured focus group interviews 14 participants aged 20-45 using reflective thematic analysis. The aim of the study is to gain first insights into how consumers perceive different green marketing strategies. Findings from the focus group discussions suggest that message source, green brands, brand perception and brand familiarity have a strong influence on perceived message credibility. The impact of disclosure of greenwashing strategies on purchase behaviour is influenced by several factors, but even in cases where it does not directly lead to a change in purchase behaviour, consumers believed that greenwashing information was important to their overall decisionmaking process. The observed deterioration in consumer trust in product brands in response to greenwashing undermines the potential of green marketing and contributes to reputational damage for manufacturers. More importantly from a practical perspective, this study shows evidence of the impact of greenwashing on credibility and opens ways for further research

The perspectives on the efficiency of individual sports programs within a selected public sector entity: a case study

Sport as a public benefit activity is of social and economic importance and contributes to the objectives of national economies and the European Union. An economic view of sports involves assessing the costs and benefits associated with sporting activities, and sports can be categorised according to their public benefit character. Despite the specificities of sport, the analysis of the efficiency of institutions providing sporting activities is as relevant as in other public sector sectors. This paper aims to evaluate the success of individual sports in a selected public sector organisation using DEA analysis with a focus on technical efficiency. The paper covers 2016-2019, where sports are considered separate units with their own management. Analysing the efficiency of institutions providing sports activities using DEA analysis is not common, as the availability of relevant data limits quantitative analyses. Although sport is a public good, assessing the efficiency of these institutions is critical to optimising their activities. This specific analysis is essential, as it is for other public sector organisations, as it helps to identify areas for improvement and more efficient use of available resources. In this context, the contribution of the scientific article is also that it highlights the importance of evaluating the efficiency of sport at a higher level, which is becoming an important area within the general economics and economics of sport. The search for optimal ways to use resources in sports poses a challenge, especially when it comes to individual sports under the umbrella of relevant organisations. The performance of these sports will be evaluated in detail using technical efficiency, which will allow a more accurate assessment of individual sports' contribution to the organisation's overall efficiency. Given the specificities of the sporting environment and the decentralised management of individual sports, this analysis will contribute to gaining a better insight into how to optimise the conditions for achieving outstanding sporting results in the environment of the public sector organisation analy

Forecasting stock market returns and volatility in different time horizons using Neural Networks

This thesis is focused on multiple-step-ahead forecasting of Nasdaq Composite index returns and daily range-based volatility. In order to capture the complex patterns potentially hidden to traditional linear models we use artificial neural networks as nonlinear, nonparametric and robust forecasting tool. We contribute to the ongoing discussion about stock market predictability with following empiri- cal results. In case of Nasdaq Composite returns, all four applied neural networks fail to outperform benchmark model in all time horizons, suggesting high unpre- dictability in accordance with Efficient market hypothesis. Also in case of Nasdaq Composite daily range-based volatility, 1 day and 1 month ahead predictions are not significantly more accurate than benchmark model. However, we find 1-week and 2-weeks-ahead forecasts to be significantly more accurate than benchmark model and able to capture the predictive patterns. Keywords predictability of stock returns, predictability of daily range-based volatility, multiple- step-ahead forecasting, neural networks, RPROP, BFGS learning algorith

Research of the role of ghrelin/GHS-R1A in the cannabinoid addiction

Background: Cannabinoids are the most widely used illicit substances. Cannabis strains with high THC content, which are currently the most common, are linked to higher risk of addiction development. Studies proved the role of ghrelin and it's receptor GHS-R1A in the brain reward system, which is crucial for reinforcing effects of palatable food and drugs of abuse. Because the role of ghrelin in rewarding effects of cannabinoids wasn't widely studied, we tested this relationship using the conditioned place preference (CPP) method. Aim: Using the rat model, to find out if: (A) acute premedication with GHS-R1A antagonist influences the manifestation of THC-induced place preference; (B) co- administration of the GHS-R1A antagonist together with THC during conditioning suppresses the development of CPP. Methods: Male rats (Wistar) were separated into three groups for both experimental arrangements (JMV2959 dosage of 0, 1 or 3 mg/kg). First day we determined natural preference of the rats for one of the compartments (20 min). 2-9th day the conditioning took place, where THC (0,3 mg/kg i.p.) in the less preferred compartment and saline in the preferred compartment were administered. 10th day we again observed preference of the rats for one of the compartments. In arrangement (A) acute application of..

Predikování výnosů a volatility akciových trhů v různých časových horizontech za použití neuronových sítí

Tato práce se zaměřuje na predikováni výnosů a denní volatility akciového in- dexu Nasdaq Composite ve více časových horizontech. Aby bylo možné zachytit komplexní vztahy, které mohou být potenciálně skryty pro tradiční lineární mod- ely, budeme používat neuronové sítě jako nelineární, neparametrické a robustní nástroje pro predikci. Námi dosažené empirické výsledky přispívají k probíhající diskuzi o předvídatelnosti akciovách trhů. V případě výnosů Nasdaq Composite, žádne ze čtyř neuronových sítí nedokázely překonat benchmark model na žádnem časovém horizontu, což naznačuje nepředvídatelnost v souladu s hypotézou efek- tivních trhů. Stejně tak v případě denní volatility Nasdaq Composite nejsou denní ani měsíční předpovědi výrazně přesnější než benchmark model. Avšak, jednotý- denní a dvoutýdenní předpovědi jsou výrazně přesnější než benchmark model a jsou schopny zachytit přítomné předpovědní vzorce. Klíčová slova předvídatelnost akciových výnosů, předvídatelnost denní volatility, predikováni v různych časových horizontech, neuronové sítě, RPROP, BFGS učící algoritmus Range of thesis: 94 pages, 82 936 charactersThis thesis is focused on multiple-step-ahead forecasting of Nasdaq Composite index returns and daily range-based volatility. In order to capture the complex patterns potentially hidden to traditional linear models we use artificial neural networks as nonlinear, nonparametric and robust forecasting tool. We contribute to the ongoing discussion about stock market predictability with following empiri- cal results. In case of Nasdaq Composite returns, all four applied neural networks fail to outperform benchmark model in all time horizons, suggesting high unpre- dictability in accordance with Efficient market hypothesis. Also in case of Nasdaq Composite daily range-based volatility, 1 day and 1 month ahead predictions are not significantly more accurate than benchmark model. However, we find 1-week and 2-weeks-ahead forecasts to be significantly more accurate than benchmark model and able to capture the predictive patterns. Keywords predictability of stock returns, predictability of daily range-based volatility, multiple- step-ahead forecasting, neural networks, RPROP, BFGS learning algorithmInstitute of Economic StudiesInstitut ekonomických studiíFakulta sociálních vědFaculty of Social Science

Portfolio selection in factor investing

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.

Does the source of fundamental data matter?

We study the role of the choice of a fundamental database on the portfolio returns of a set of 74 fundamental anomalies. We benchmark Compustat by comparing it to Datastream in the US and find systematic differences in the raw financial statements across the databases. These differences only have a small effect on the returns of anomalies when they are constructed on stock-months existing in both databases. Different stock coverage across the databases, however, leads to large statistically and economically significant disparities in the returns. Profitability anomalies yield negative returns on the Datastream universe

Effects of structures of molybdenum catalysts on selectivity in gas-phase propylene oxidation

Molybdenum-based catalysts for the gas-phase oxidation of propylene with air were investigated. Various types of silica-supported molybdenum oxide and molybdenum-bismuth mixed oxide catalysts were prepared from inorganic and organometallic molybdenum precursors using wet impregnation and physical vapor deposition methods. The epoxidation activities of the prepared catalysts showed direct correlations with their nanostructures, which were identified using transmission electron microscopy. The appearance of a partly or fully crystalline molybdenum oxide phase, which interacted poorly with the silica support, decreased the selectivity for propylene oxide formation to below 10%; non-crystalline octahedrally coordinated molybdenum species anchored on the support gave propylene oxide formations greater than 55%, with 11% propylene conversion. Electrochemical characterization of molybdenum oxides with various morphologies showed the importance of structural defects. Direct promotion by bismuth of the epoxidation reactivities over molybdenum oxides is disputed.Web of Science36111909190

Gas-phase epoxidation of propylene over iron-containing catalysts: the effect of iron incorporation in the support matrix

The gas-phase epoxidation of propylene using iron as a catalytically active metal has been studied. The XRD-amorphous silica nanopowder was found to host active as well as redox-silent iron species, using nitrous oxide as an oxidizing agent. The presence of iron oxide nanoparticles was proven in the most active catalysts, indicating that the epoxidation proceeds over nanoparticles rather than over isolated iron atoms. A combination of XPS, TEM and voltammetric techniques elucidated the mechanism of the formation of catalytically active forms of iron oxide, distinguishing selective forms from unselective and inactive ones in the epoxidation reaction. Transition response experiments showed a good correlation between epoxidation activity, N2O decomposition and electrochemical specification of iron oxides.Web of Science482673266