EVALUATION THE PERFORMANCE OF EXCHANGE TRADED FUNDS (ETFs) LISTED ON THE INTERNATIONAL STOCK MARKETS

Exchange traded funds (ETFs) are one of the most popular and fastest growing classes of financial assets available in today's markets. Due to the significant increase in popularity, ETFs allow investors to diversify their portfolios through a basket of assets from different countries that are traded on international stock markets. The aim of this study is to analyze the performance of ETFs on different continents. The sample in this research is 40 ETFs listed on the stock exchanges in the USA, Europe, Asia-Pacific and Emerging markets. The monitoring period is 2015 to 2019. Standard indicators such as Tracking error, Jensen alpha, Treynor ratio, Appraisal ratio, Information ratio are used to evaluate ETFs performance. The results of this research showed that ETFs from the USA show the best performance, according to the selected measurement methods. The Treynor ratio has also been shown to be skewed, as ETFs with identical systemic risk but different overall risk are not quantified correctly

Critical review of text mining and sentiment analysis for stock market prediction

The paper is aimed at a critical review of the literature dealing with text mining and sentiment analysis for stock market prediction. The aim of this work is to create a critical review of the literature, especially with regard to the latest findings of research articles in the selected topic strictly focused on stock markets represented by stock indices or stock titles. This requires examining and critically analyzing the methods used in the analysis of sentiment from textual data, with special regard to the possibility of generalization and transferability of research results. For this reason, an analytical approach is also used in working with the literature and a critical approach in its organization, especially for completeness, coherence, and consistency. Based on the selected criteria, 260 articles corresponding to the subject area are selected from the world databases of Web of Science and Scopus. These studies are graphically captured through bibliometric analysis. Subsequently, the selection of articles was narrowed to 49. The outputs are synthesized and the main findings and limits of the current state of research are highlighted with possible future directions of subsequent research

Comparison Uncertainty of Different Types of Membership Functions in T2FLS: Case of International Financial Market

This article deals with the determination and comparison of different types of functions of the type-2 interval of fuzzy logic, using a case study on the international financial market. The model is demonstrated on the time series of the leading stock index DJIA of the US market. Type-2 Fuzzy Logic membership features are able to include additional uncertainty resulting from unclear, uncertain or inaccurate financial data that are selected as inputs to the model. Data on the financial situation of companies are prone to inaccuracies or incomplete information, which is why the type-2 fuzzy logic application is most suitable for this type of financial analysis. This paper is primarily focused on comparing and evaluating the performance of different types of type-2 fuzzy membership functions with integrated additional uncertainty. For this purpose, several model situations differing in shape and level or degree of uncertainty of membership functions are constructed. The results of this research show that type-2 fuzzy sets with dual membership functions is a suitable expert system for highly chaotic and unstable international stock markets and achieves higher accuracy with the integration of a certain level of uncertainty compared to type-1 fuzzy logic

Aplikace modelu CAPM na český akciový trh

Janková, Z. Application of the CAPM model on the Czech Stock Market. Bachelor thesis. Brno: PEF MENDELU, 2015 The bachelor thesis is concerned with the Capital Asset Pricing Model (CAPM) and it's explicatory ability application on the Czech Stock Market. The CAPM is empirically tested on historical stock data of selected shares from the Prague Stock Exchange.Ability to dermine individual stock returns is tested on the wide range of investment horizont, namely 1, 3, 5, 7 and 10 years. The praktical application shows that the CAPM model is not capable to explain individual stock returns only using beta coefficient which represent systematick risk

Expert System for Decision-Making on Stock Markets Using Investor Sentiment

Předložená disertační práce zkoumá potenciál využití skóre sentimentu extrahovaného z textových dat společně s historickými daty o akciovém indexu ke zlepšení výkonnosti predikce na akciovém trhu prostřednictvím vytvořeného modelu expertního systému. Vzhledem k tomu, že velké množství textových dokumentů souvisejících s financemi, které zveřejňují jak profesionální, tak amatérští investoři, nejen na online sociálních sítích, by mohly mít dopad na vývoj akciových trhů, je zásadním úkolem analyzovat finanční texty zveřejněné různými uživateli a zejména z nich extrahovat sentiment. V této práci je sentiment investorů získán z online finančních zpráv a příspěvků zveřejněných na finanční sociální platformě StockTwits. Skóre sentimentu je stanoveno pomocí hybridního přístupu kombinující modely strojového učení s učitelem a neuronových sítí, přičemž ke klasifikaci polarity sentimentu je využito vícero lexikonů pozitivních a negativních slov. Je analyzován vliv skóre sentimentu na akciový trh prostřednictvím kauzality, kointegrace a koherence. V disertační práci je navržen model expertního systému založený na metodách fuzzy logiky. Fuzzy logika poskytuje pozoruhodné vlastnosti při práci s vágními, nepřesnými či nejasnými údaji a je schopna se lépe vypořádat s chaotickým prostředím na akciových trzích. V nedávných vědeckých studiích na popularitě získává vyšší úroveň fuzzy logiky, která je označována jako type-2 fuzzy logika. Oproti klasické type-1 fuzzy logice, je tento vyšší typ schopen mezi zdvojené funkce členství integrovat určitou úroveň nejistoty. Tento typ expertního systému je ovšem v předmětné problematice predikce akciového trhu s využitím extrahovaného sentimentu investorů značně opomíjen. Z toho důvodu je v disertační práci zkoumán potenciál využít a výkonnost type-2 fuzzy logiky. Konkrétně je vytvořeno několik type-2 fuzzy modelů, které jsou trénovány na historických datech akciového indexu a skóre sentimentu investorů za období 2018-2020. Vytvořené modely jsou posouzeny k měření výkonu predikce bez sentimentu i s integrací sentimentu investorů. Následně je na základě vytvořeného expertního modelu stanovena investiční strategie a sledována jeho ziskovost. Výkonnost predikce fuzzy modelů je komparována s výkonností několika srovnávacích modelů, včetně SVM, k-NN, naivního Bayes a dalších. Z experimentů vyplynulo, že modely fuzzy logiky jsou schopny vhodným nastavením funkcí členství a nejistoty v nich obsažených vylepšit predikci a jsou schopny konkurovat klasickým modelům predikce, které jsou standardně využívané ve výzkumných studiích. Vytvořený model by měl také sloužit jako nástroj pro podporu investičního rozhodování individuálním investorům.The presented dissertation examines the potential of using the sentiment score extracted from textual data with historical stock index data to improve the performance of stock market prediction through the created model of the expert system. Given the large number of financial-related text documents published by both professional and amateur investors, not only on online social networks that could have an impact on real stock markets, but it is also crucial to analyze and in particular extract financial texts published by different users. investor sentiment. In this work, investor sentiment is obtained from online financial reports and contributions published on the financial social platform StockTwits. Sentiment scores are determined using a hybrid approach combining machine learning models with the teacher and neural networks, with multiple lexicons of positive and negative words used to classify sentiment polarity. The influence of sentiment score on the stock market through causality, cointegration and coherence is analyzed. The dissertation proposes a model of an expert system based on fuzzy logic methods. Fuzzy logic provides remarkable features when working with vague, inaccurate or unclear data and is able to deal with the chaotic environment of stock markets. In recent scientific studies, it has gained in popularity a higher level of fuzzy logic, which is referred to as type-2 fuzzy logic. Unlike the classic type-1 fuzzy logic, this higher type is able to integrate a certain level of uncertainty between the dual membership functions. However, this type of expert system is considerably neglected in the subject issue of stock market prediction using the extracted investor sentiment. For this reason, the dissertation examines the potential to use and the performance of type-2 fuzzy logic. Specifically, several type-2 fuzzy models are created. which are trained on historical stock index data and sentiment scores extracted from text data for the period 2018-2020. The created models are assessed to measure the prediction performance without sentiment and with the integration of investor sentiment. Subsequently, based on the created expert model, the investment strategy is determined, and its profitability is monitored. The prediction performance of fuzzy models is compared with the performance of several comparison models, including SVM, KNN, naive Bayes and others. It has been observed from experiments that fuzzy logic models are able to improve prediction by appropriate setting of membership and uncertainty functions contained in them and are able to compete with classical expert prediction models, which are standardly used in research studies. The created model should serve as a tool to support investment decisions for individual investors.

Performance Evaluation of Real Estate Investment and Mutual Funds

Diplomová práce se zaobírá hodnocením a následnou komparací výkonnosti podílových a investičních fondů se zaměřením na sektor nemovitostí. Je představena podstata a principy fungování podílových fondů, ETF a REIT a z nich vyplývající nedostatky a přednosti. Dle zvolených ukazatelů je zkoumána výnosnost, rizikovost i nákladovost investičních možností a stanoveno investiční doporučení pro potenciální drobné investory a management investičních společností.Diploma thesis deals with the evaluation and the comparison of the performance of mutual funds and investment funds with a focus on the real estate sector. The essence and principles of mutual funds, ETF and REIT are presented, and the resulting weaknesses and advantages. According to the selected indicators, the profitability, riskiness and expense of the investment opportunities are examined and investment recommendations for management of an investment company and potential retail investors are established.

Komparace výkonnosti podílových fondů a ETF

JANKOVÁ, Z. A Performance Comparison of mutual funds and ETF. Mendel University in Brno, 2017. Diploma thesis. The diploma thesis is focused on performance comparison of open-end mutual funds and ETF. Selected funds are separated by region USA, World, European and Emerging markets equities. Funds are analyzed in the practical part for the period between 2007 and 2016. The first part introduced defines notion of collective investment, advantages and disadvantages. Funds are analyzed in the terms of return, risk, cost and including foreing exchange risk

A Bibliometric Analysis of Artificial Intelligence Technique in Financial Market

This article aims to explore the main areas of research, development trends and provide a systematic overview of publications in the field of artificial intelligence in financial markets. The bibliometric tool VOSViewer is used in this paper. We analyzed 353 articles and contributions obtained from the database of Web of Science, and summarized our findings as follows: artificial intelligence is becoming increasingly widespread in the field of finance and interdisciplinary interconnection; artificial intelligence tools such as neural networks and fuzzy logic are most often used to predict the development of financial time series, or to create decision models; the most important cited authors in this field are Markowitz and Lebaron. Expert System with Application is the cradle of a significant part of fundamental research in the field of artificial intelligence. By using effective bibliometric methods, we provide comprehensive analysis and in-depth insight into the subject area of research, which allows individuals and especially new beginners interested in this area to obtain valuable information and possible direction of future research. The study is recommended to focus on hybrid models prediction of individual sectors of the financial markets, which are present in the current research on the rise

Interval Type-2 fuzzy logic expert system for investment analysis

In this paper, a higher degree of fuzzy logic type-2 fuzzy logic is presented as decision making process of investment. There is a key difference between type-2 and type-1 fuzzy logic. The application is made on the Czech stock market and is used to decide on investing in PX index stocks. The proposed type 2 fuzzy model uses the return and risk of investment instruments as input variables. The system created is able to generate aggregate models from a certain number of language rules, allowing the investor to understand the generated financial model. The use of T2FLS can lead to more realistic and accurate results than T1FLS