Fourier analysis and its applications in finance

This thesis presents the potential of Fourier analysis in predicting the price movements of financial instruments. The Fourier trading bot, which was backtested across multiple asset classes, achieved above-average returns, particularly in environments with stable periodic patterns. However, further testing is required to confirm the robustness of the strategy. The thesis also highlights the limitations of Fourier analysis, especially when dealing with non-stationary signals and its limited ability to respond to sudden market shocks.Táto diplomová práca skúma potenciál Fourierovej analýzy pri predikcii cenových pohybov finančných inštrumentov. Fourier trading bot, ktorý bol spätne testovaný na viacerých triedach aktív dosahoval nadpriemerný výnos najmä v prostredí so stabilnými periodickými vzormi. Pre potvrdenie robustnosti stratégie sú však potrebné ďalšie testy. Práca zároveň poukazuje na obmedzenia Fourierovej analýzy, najmä pri nestacionárnych signáloch a jej obmedzenú schopnosť reagovať na náhle trhové šoky.Tato diplomová práce zkoumá potenciál Fourierovy analýzy při predikci cenových pohybů finančních instrumentů. Fourierův obchodní bot, který byl zpětně testován na různých třídách aktiv, dosahoval nadprůměrného výnosu zejména v prostředí se stabilními periodickými vzory. Pro potvrzení robustnosti strategie jsou však zapotřebí další testy. Práce zároveň poukazuje na omezení Fourierovy analýzy, zejména při nestacionárních signálech a na její omezenou schopnost reagovat na náhlé tržní šoky

Communication of Machine Learning Results Through Tabular Outputs

The field of data mining constantly pursues the development of predictive models, crucial for various applications. While much emphasis is placed on improving the accuracy of these models, their comprehensibility to analysts and end-users is an often overlooked aspect. This thesis addresses this gap by presenting an empirical study that investigates the interpretability of alternative representation format, with a specific focus on decision trees and decision tables. In our research, decision trees are identified as challenging to read and understand, prompting the exploration of a solution. Motivated by the objective of enhancing model interpretability, we propose the development of a Python program designed to convert complex decision trees into more accessible decision tables. Decision tables, known for their clarity and ease of interpretation, emerge as a favorable alternative.The field of data mining constantly pursues the development of predictive models, crucial for various applications. While much emphasis is placed on improving the accuracy of these models, their comprehensibility to analysts and end-users is an often overlooked aspect. This thesis addresses this gap by presenting an empirical study that investigates the interpretability of alternative representation format, with a specific focus on decision trees and decision tables. In our research, decision trees are identified as challenging to read and understand, prompting the exploration of a solution. Motivated by the objective of enhancing model interpretability, we propose the development of a Python program designed to convert complex decision trees into more accessible decision tables. Decision tables, known for their clarity and ease of interpretation, emerge as a favorable alternative

Development of the CzechUni Application: Administrative Section

This thesis focuses on the design and implementation of the administrative section of the CzechUni web application, which supports international students in applying to Czech universities. The work is part of a team project aimed at developing the CzechUni application, with the objective of creating an efficient administrative interface for managing users, applications, study programs, and other relevant data. As part of the project, an analysis of administrative interfaces from selected applications was conducted, identifying best practices in UI/UX and functionality. Based on these findings, the design of the administrative interface was created and subsequently implemented using modern technologies. The thesis also reflects the limitations of the production environment and suggests further possibilities for expanding the application's functionality. The result of the thesis is a fully functional prototype of the administrative section of the CzechUni application, providing added value to both the Czech Education organization and international students using the application's services.Tato diplomová práce se zaměřuje na návrh a implementaci administrační části webové aplikace CzechUni, která slouží k podpoře zahraničních studentů při přihlašování na české vysoké školy. Práce je součástí týmového projektu zaměřeného na vývoj aplikace CzechUni a jejím cílem bylo vytvořit efektivní administrační rozhraní umožňující správu uživatelů, přihlášek, studijních programů a dalších relevantních dat. V rámci práce byla provedena analýza administračních rozhraní vybraných aplikací, která pomohla identifikovat nejlepší praktiky v oblasti UI/UX a funkcionality. Na základě těchto poznatků byl navržen design administračního rozhraní a následně realizována jeho implementace s využitím moderních technologií. Diplomová práce rovněž reflektuje omezení produkčního prostředí a navrhuje další možnosti rozšíření funkcionality aplikace. Výsledkem práce je plně funkční prototyp administrační části aplikace CzechUni, který přináší přidanou hodnotu jak společnosti Czech Education, tak zahraničním studentům využívajícím služeb této aplikace

The impact of IT audit on improving the IT environment: An analysis of the effectiveness of IT General Controls

Diplomová práce se zaměřuje na hodnocení přínosu IT auditů orientovaných na základní IT kontrolní mechanismy, známé jako General IT Controls (GITC), s důrazem na rozsah prací Design and Implementation (D&I). Cílem je zjistit, jaký dopad mají tyto audity na zlepšení IT prostředí, zejména v kontextu menších organizací s omezenými IT zdroji. Práce se zaměřuje na hodnocení efektivity doporučení z auditů, spolupráci mezi auditory a IT manažery a překážky při implementaci doporučení. Výzkum je založen na kvalitativní analýze rozhovorů s IT manažery a nabízí hluboký pohled na vnímání přínosu IT auditů, jejich efektivitu a možné oblasti pro zlepšení. Výsledky mají za cíl poskytnout praktické doporučení pro efektivnější realizaci auditů a zlepšení IT procesů.This thesis focuses on evaluating the benefits of IT audits aimed at the fundamental IT control mechanisms known as General IT Controls (GITC), with an emphasis on the scope of Design and Implementation (D&I) activities. The objective is to determine the impact of these audits on improving IT environments, especially in the context of smaller organizations with limited IT resources. The thesis evaluates the effectiveness of audit recommendations, the collaboration between auditors and IT managers, and the barriers to implementing these recommendations. The research is based on a qualitative analysis of interviews with IT managers, providing an in-depth view of the perceived benefits of IT audits, their efficiency, and possible areas for improvement. The results aim to provide practical recommendations for more effective audit implementation and improvements in IT processes

Social Media Communication and Its Impact on Stock Price Volatility During Product Launches

This thesis investigates the influence of social media communication on stock price volatility, focusing on events related to well-known companies like Tesla, Apple, TSMC, Meta, Amazon, Microsoft, Nio Inc., and P&G. By analyzing tweets and YouTube comments surrounding key corporate events, sentiment analysis will be conducted to assess correlations with stock price fluctuations during these periods. The research adopts a quantitative data analysis with qualitative sentiment evaluation. Sentiment scores will be calculated based on a set of positive and negative keywords, facilitating a deeper understanding of the relationship between public sentiment and market reactions. The findings reveal that patterns in social media sentiment are not only associated with short-term stock price volatility but also provide predictive insights into market trends. A machine learning model is employed to analyze the relationship between sentiment dynamics and stock price volatility, helping to predict how stock prices may fluctuate in response to changes in sentiment. This study aims to provide actionable insights for corporations and investors, enabling informed decisions based on the impact of social media on financial markets. The findings contribute to existing literature on behavioural finance and social media's role in investor sentiment, with potential implications for future corporate communication strategies.This thesis investigates the influence of social media communication on stock price volatility, focusing on events related to well-known companies like Tesla, Apple, TSMC, Meta, Amazon, Microsoft, Nio Inc., and P&G. By analyzing tweets and YouTube comments surrounding key corporate events, sentiment analysis will be conducted to assess correlations with stock price fluctuations during these periods. The research adopts a quantitative data analysis with qualitative sentiment evaluation. Sentiment scores will be calculated based on a set of positive and negative keywords, facilitating a deeper understanding of the relationship between public sentiment and market reactions. The findings reveal that patterns in social media sentiment are not only associated with short-term stock price volatility but also provide predictive insights into market trends. A machine learning model is employed to analyze the relationship between sentiment dynamics and stock price volatility, helping to predict how stock prices may fluctuate in response to changes in sentiment. This study aims to provide actionable insights for corporations and investors, enabling informed decisions based on the impact of social media on financial markets. The findings contribute to existing literature on behavioural finance and social media's role in investor sentiment, with potential implications for future corporate communication strategies

Dolování pravidel ze znalostních grafů

Tato práce se zabývá tématy znalostních grafů a dolování asociačních pravidel z nich. Znalostní grafy jsou velké datové struktury, které obsahují propojená data, která sémantickým způsobem popisují vztahy mezi entitami. Obrovské množství dat uložených v těchto grafech je skvělým zdrojem materiálu pro data miningovou analýzu. Jednou z nejlépe vysvětlitelných metod data miningu, která se zaměřuje na hledání vztahů a vzorců, je dolování asociačních pravidel. Biochemie je jedním z nejprogresivnějších oborů z hlediska publikování propojených dat. Jedním z příkladů tohoto úsilí je KG-Microbe, což je znalostní graf, který spojuje data týkající se mikroorganismů a jejich vlastnosti a funkce. Experiment popsaný v této práci využívá speciální nástroj RDFRules pro dolování asociačních pravidel z tohoto znalostního grafu. Jiné experimenty byly provedeny na datech z tohoto znalostního grafu v tabulkovém formátu a za použití metod strojového učení, které jsou méně vysvětlitelné. Tato práce popisuje proces vytváření klasifikátoru založeného na asociačních pravidlech pro predikci kultivačního média pro mikroby. Přestože přesnost tohoto klasifikátoru není na stejné úrovni jako referenční klasifikátor CatBoost, výsledky jsou vysoce interpretovatelné a lze je použít pro další analýzu nově nalezených vzorů v grafu.This thesis explores the topics of knowledge graphs and mining association rules from them. Knowledge graphs are large data structures which store linked data that describe the relations between entities in a semantic way. The massive quantities of data stored in those graphs are a great source of material for data mining analysis. One of the most explainable data mining methods that focuses on finding relationships and patterns is association rule mining. Biochemistry is one of the most progressive fields in terms of publishing linked data. One example of this effort is KG-Microbe which is a knowledge graph that connects data concerning microorganisms and their features and functions. The experiment which is described in this thesis uses a special tool called RDFRules for mining association rules from this knowledge graph. Other experiments have been conducted on the data from this knowledge graph in a flattened format and using less explainable machine learning methods. This work describes the process of creating an association-rule-based classifier to make predictions about the cultivation medium of a microbe. While the accuracy of this classifier is not on par with a referential CatBoost classifier, the results are highly interpretable and can be used for further analysis of the new-found patterns in the graph

The impact of the covid and energy crisis on the labour market in the Czech Republic

The COVID-19 pandemic has had a significant impact on the lives of people and the economy. It has also caused a current energy crisis, which has affected various aspects of our lives, including the labor market. Employment is an essential part of people's lives, and unemployment is a major socio-economic problem that affects the stability and growth of the economy. Therefore, monitoring unemployment is an important task for state institutions. This bachelor's thesis focuses on analyzing the impact of the COVID-19 pandemic and energy crisis on the Czech labor market. It aims to identify the most vulnerable population groups and labor sectors during this time. The theoretical part of the thesis defines the key concepts associated with the labor market, the types of unemployment and its consequences. The practical part analyzes changes in the distribution by gender, age and gender structure, educational structure and sectoral focus. It also covers current trends and their impact on the labor market.Pandemie COVID-19 významně ovlivnila život každého člověka a zasáhla všechny oblasti ekonomiky, stejně jako současná energetická krize. Jednou z klíčových oblastí, na kterou se tato práce zaměřuje, je trh práce, neboť zaměstnanost představuje zásadní aspekt života lidí. Nezaměstnanost patří mezi hlavní socioekonomické problémy, které mají vliv na stabilitu a růst ekonomiky, a její sledování je proto důležitým úkolem státních institucí. Tato bakalářská práce se věnuje analýze dopadů pandemie COVID-19 a energetické krize na trh práce v České republice a usiluje o identifikaci nejzranitelnějších skupin obyvatel a ohrožených pracovních odvětví. Teoretická část práce vymezuje klíčové pojmy spojené s trhem práce, typy nezaměstnanosti a její důsledky. Praktická část analyzuje změny v ukazatelích trhu práce podle pohlaví, věkové a genderové struktury, vzdělanostní struktury a sektorového zaměření. Součástí jsou rovněž aktuální trendy a jejich dopady na trh práce

Comparison of travel insurance on the Czech financial market

Bakalářská práce „Srovnání cestovního pojištění na českém finančním trhu“ analyzuje nabídku cestovního pojištění a identifikuje klíčové rozdíly v pojistných podmínkách, cenotvorbě a výlukách. Cílem práce je, na základě analýzy trhu a pozitivní i negativní praxe v konkrétních pojistných událostech, vytvořit návrh postupu pro klienty i poradce. Práce využívá komparativní analýzu a modelování, ze kterých vyplývá, že zájemci o pojištění a klienti mají stále malé povědomí o výlukách a podmínkách cestovního pojištění. Výstupem práce je soubor otázek, které by měli být zodpovězeny před sjednáním cestovního pojištění a "desatero" cestovního pojištění s nejdůležitějšími informacemi, které by měl zájemce o pojištění vzít v úvahu.The bachelor thesis "Comparison of travel insurance on the Czech financial market" analyses the travel insurance offer and identifies key differences in insurance conditions, pricing and exclusions. The aim of the thesis is, on the basis of market analysis and positive and negative practices in specific insurance claims, to create a proposal for a course of action for clients and advisors. The thesis uses comparative analysis and modelling, which shows that insurance seekers and clients still have little awareness of exclusions and conditions of travel insurance. The output of the thesis is a set of questions that should be answered before purchasing travel insurance and a travel insurance 'top ten' list of the most important information that prospective buyers should consider

Motivace zaměstnanců pracujících na dálku ke zlepšení produktivity a pracovní spokojenosti: Případová studie SAP

This study investigates employee motivation, productivity, and job satisfaction in a remote work environment in the post-pandemic era. Semi-structured interviews were conducted with twelve employees from SAP SE’s IT departments using qualitative research methods. The research applies Maslow's Hierarchy of Needs and Herzberg's Two-Factor Theory to analyze employees' perceptions of key motivational factors, including flexibility, autonomy, communication, social interaction, technology and infrastructure. Findings reveal that while flexibility and autonomy enhance motivation and productivity, challenges such as boundary management, social isolation, and disorganized communication tools hinder performance and satisfaction. Participants expressed a preference for hybrid work models, highlighting the need for occasional in-person interactions and improved home office setups. The study concludes with practical recommendations for managers to enhance remote working conditions by reducing mandatory office days, adopting trust-based management, promoting social engagement, standardizing communication tools, and supporting ergonomic home office setups. These findings provide critical insights for organizations implementing long-term hybrid work models, offering strategies to maintain employee motivation and organizational success.Tato studie se zabývá motivací zaměstnanců, produktivitou a pracovní spokojeností v prostředí práce na dálku po pandemii. Využitím kvalitativních výzkumných metod byly provedeny polostrukturované rozhovory s dvanácti zaměstnanci IT oddělení společnosti SAP SE. Výzkum aplikuje Maslowovu hierarchii potřeb a Herzbergovu dvoufaktorovou teorii k analýze vnímání klíčových motivačních faktorů zaměstnanci, které zahrnují flexibilitu, autonomii, komunikaci, sociální interakce, technologii a infrastrukturu. Výsledky ukazují, že flexibilita a autonomie zvyšují motivaci a produktivitu, ale překážky, jako jsou hranice mezi pracovním a osobním životem, sociální izolace a neorganizované komunikační nástroje, brání výkonu a pracovní spokojenosti. Účastníci preferovali hybridní pracovní modely a zdůraznili také potřebu občasných osobních interakcí a zlepšení pracovního vybavení v domácím prostředí. Na závěr studie předkládá praktická doporučení pro manažery, jak zlepšit zkušenosti s prací na dálku omezením povinných dnů docházení do kanceláře, přijetím vedení založeného na důvěře, podporou sociálního zapojení, standardizací komunikačních nástrojů a podporou ergonomického pracovního vybavení v domácím prostředí. Tato zjištění poskytují cenné poznatky pro organizace zavádějící hybridní pracovní modely a nabízejí strategie pro udržení motivace zaměstnanců a take úspěchu organizace

Studying the role of specific fields of education on country’s social and economic performance

This study tries to investigate the effect of stem education and stem workforce and several social and economic indicators with the objective to understand how they affect these indicators for a country and also overall. It is a general consensus that fields of science, engineering and mathematics are beneficial for competitiveness in the global economy. It is a long-standing topic that there are benefits and as such is a very important area for education and labour force policies and in determining the government expenditure on these. The goal of this paper is not to make predictions for the social and economic indicators but to understand the hidden and spillover effects of STEM in the economic region (which in my case are individual European countries). As with any inferential analysis, drawing inferences are often with a risk as the assumptions for the same are hard to establish in real life. Numerous research has been carried out in this topic but all of them fail to address the social effects of education in STEM fields. The aim of my paper is to build on existing studies and expand the array of returns to education in terms of social development as well as economic growth. Innovation is considered to be the most important tool in solving the problems faced by societies today. It can be confidently stated that innovation and STEM fields cannot be separated from each other. Even the individual components of STEM (Science, Technical, Engineering, Mathematics) cannot be separated from each other for analysis due to the simple reason that they are not located in nature separately (Moomaw 2013). Further, the main idea of this research is to look into the lagged effects of STEM graduates on social and economic development - that is to say, a person graduating in STEM fields may not immediately start contributing to such development but probably after a few years as they enter the workforce and bring around innovation. Therefore, it is essential to use lagged variables of STEM education from before the period of study to understand their effect on the dependent indicators. For this study I employ panel data regression models with fixed effects for each longitudinal section cross country effects in addition to the lagged independent variables. The framework of this study is based on traditional growth models enhanced to include the effect of STEM education. All the data used in this study is obtained from Eurostat sources using a publicly available library. The data ranges over 13 European countries over a period of 10 years. Estimates are obtained using regression analysis over panels as well as cross sectional data. The results indicate some positive, unambiguous effects on these indicators and calls for further research in this topic and policy changes for promoting STEM programs and tertiary graduates and higher levels. The analysis revealed that a 1% increase in STEM workforce is associated with a 0.203% increase in GDP per worker, demonstrating the significant economic impact of STEM education. In addition to economic productivity, STEM concentration contributes to reductions in inequality, severe material deprivation, and crime rates. The findings are limited to European countries, and further global studies are needed to generalize the results.This study tries to investigate the effect of stem education and stem workforce and several social and economic indicators with the objective to understand how they affect these indicators for a country and also overall. It is a general consensus that fields of science, engineering and mathematics are beneficial for competitiveness in the global economy. It is a long-standing topic that there are benefits and as such is a very important area for education and labour force policies and in determining the government expenditure on these. The goal of this paper is not to make predictions for the social and economic indicators but to understand the hidden and spillover effects of STEM in the economic region (which in my case are individual European countries). As with any inferential analysis, drawing inferences are often with a risk as the assumptions for the same are hard to establish in real life. Numerous research has been carried out in this topic but all of them fail to address the social effects of education in STEM fields. The aim of my paper is to build on existing studies and expand the array of returns to education in terms of social development as well as economic growth. Innovation is considered to be the most important tool in solving the problems faced by societies today. It can be confidently stated that innovation and STEM fields cannot be separated from each other. Even the individual components of STEM (Science, Technical, Engineering, Mathematics) cannot be separated from each other for analysis due to the simple reason that they are not located in nature separately (Moomaw 2013). Further, the main idea of this research is to look into the lagged effects of STEM graduates on social and economic development - that is to say, a person graduating in STEM fields may not immediately start contributing to such development but probably after a few years as they enter the workforce and bring around innovation. Therefore, it is essential to use lagged variables of STEM education from before the period of study to understand their effect on the dependent indicators. For this study I employ panel data regression models with fixed effects for each longitudinal section cross country effects in addition to the lagged independent variables. The framework of this study is based on traditional growth models enhanced to include the effect of STEM education. All the data used in this study is obtained from Eurostat sources using a publicly available library. The data ranges over 13 European countries over a period of 10 years. Estimates are obtained using regression analysis over panels as well as cross sectional data. The results indicate some positive, unambiguous effects on these indicators and calls for further research in this topic and policy changes for promoting STEM programs and tertiary graduates and higher levels. The analysis revealed that a 1% increase in STEM workforce is associated with a 0.203% increase in GDP per worker, demonstrating the significant economic impact of STEM education. In addition to economic productivity, STEM concentration contributes to reductions in inequality, severe material deprivation, and crime rates. The findings are limited to European countries, and further global studies are needed to generalize the results