A risk-aware fuzzy linguistic knowledge-based recommender system for hedge funds

One of the most difficult tasks for hedge funds investors is selecting a proper fund with just the right level level of risk. Often times, the issue is not only quantifying the hedge fund risk, but also the level the investors consider just right. To support this decision, we propose a novel recommender system, which is aware of the risks associated to different hedge funds, considering multiple factors, such as current yields, historic performance, diversification by industry, etc. Our system captures the preferences of the investors (e.g. industries, desired level of risk) applying fuzzy linguistic modeling and provides personalized recommendations for matching hedge funds. To demonstrate how our approach works, we have first profiled more than 4000 top hedge funds based on their composition and performance and second, created different simulated investment profiles and tested our recommendations with them.This paper has been developed with the FEDER financing under Project TIN2016-75850-R

Unveiling the impact of managerial traits on investor decision prediction

Investment decisions are influenced by various factors, including personal characteristics and managerial issues. In this research, we aimed to investigate the impact of managerial traits on investment decisions by using adaptive neuro-fuzzy inference system (ANFIS) to develop a personalized investment recommendation system. We collected data from potential investors through a survey, which included questions on investment-types, investment habits, and managerial traits. The survey data were used to create an ANFIS model, which is a hybrid model that combines the strengths of both artificial neural networks and fuzzy logic systems. The ANFIS model was trained using 1542 survey data pairs, and the model's performance was evaluated using a validation set. The results of the ANFIS model showed that the model had a minimal training root mean square error of 0.837341. The ANFIS model was able to effectively capture the relationship between managerial traits and investment decisions and was able to make personalized investment recommendations based on the input data. The results of this research provide valuable insights into the impact of managerial traits on investment decisions and demonstrate the potential of ANFIS in developing personalized investment recommendation systems. In conclusion, this research aimed to investigate the impact of managerial traits on investment decisions using ANFIS. The results of this study demonstrate the potential of ANFIS to personalize investment recommendations based on the input data. This research can be used as a foundation for future research in the field of investment recommendations and can be helpful to investors to take their decision-making

Banking and Finance

The banking and finance industry plays a significant role in the economy of a nation. As such, continuous research and up-to-date feeds are necessary for it to stay competitive and resilient. Due to its revolving and dynamic nature as well as its significance and interlinkages with other industries, a well-functioning banking and finance system is vital in safeguarding the interest of all stakeholders. Banking and Finance covers a wide range of essential topics highlighting major issues related to banking and finance. The book is rich with empirical evidence, scientific researches, best practices, and recommendations, making it a compact yet handy reference for readers, especially those who are in the field of banking and finance

Technical and Fundamental Features Analysis for Stock Market Prediction with Data Mining Methods

Predicting stock prices is an essential objective in the financial world. Forecasting stock returns and their risk represents one of the most critical concerns of market decision makers. This thesis investigates the stock price forecasting with three approaches from the data mining concept and shows how different elements in the stock price can help to enhance the accuracy of our prediction. For this reason, the first and second approaches capture many fundamental indicators from the stocks and implement them as explanatory variables to do stock price classification and forecasting. In the third approach, technical features from the candlestick representation of the share prices are extracted and used to enhance the accuracy of the forecasting. In each approach, different tools and techniques from data mining and machine learning are employed to justify why the forecasting is working. Furthermore, since the idea is to evaluate the potential of features in the stock trend forecasting, therefore we diversify our experiments using both technical and fundamental features. Therefore, in the first approach, a three-stage methodology is developed while in the first step, a comprehensive investigation of all possible features which can be effective on stocks risk and return are identified. Then, in the next stage, risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, based on some filters and function-based clustering; and re-predicted the risk and return of stocks. In the second approach, instead of using single classifiers, a fusion model is proposed based on the use of multiple diverse base classifiers that operate on a common input and a meta-classifier that learns from base classifiers’ outputs to obtain a more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting, and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes are determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. Finally, in the third approach, a novel forecasting model for stock markets based on the wrapper ANFIS (Adaptive Neural Fuzzy Inference System) – ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick is presented. Two approaches of Raw-based and Signal-based are devised to extract the model’s input variables and buy and sell signals are considered as output variables. To illustrate the methodologies, for the first and second approaches, Tehran Stock Exchange (TSE) data for the period from 2002 to 2012 are applied, while for the third approach, we used General Motors and Dow Jones indexes.Predicting stock prices is an essential objective in the financial world. Forecasting stock returns and their risk represents one of the most critical concerns of market decision makers. This thesis investigates the stock price forecasting with three approaches from the data mining concept and shows how different elements in the stock price can help to enhance the accuracy of our prediction. For this reason, the first and second approaches capture many fundamental indicators from the stocks and implement them as explanatory variables to do stock price classification and forecasting. In the third approach, technical features from the candlestick representation of the share prices are extracted and used to enhance the accuracy of the forecasting. In each approach, different tools and techniques from data mining and machine learning are employed to justify why the forecasting is working. Furthermore, since the idea is to evaluate the potential of features in the stock trend forecasting, therefore we diversify our experiments using both technical and fundamental features. Therefore, in the first approach, a three-stage methodology is developed while in the first step, a comprehensive investigation of all possible features which can be effective on stocks risk and return are identified. Then, in the next stage, risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, based on some filters and function-based clustering; and re-predicted the risk and return of stocks. In the second approach, instead of using single classifiers, a fusion model is proposed based on the use of multiple diverse base classifiers that operate on a common input and a meta-classifier that learns from base classifiers’ outputs to obtain a more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting, and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes are determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. Finally, in the third approach, a novel forecasting model for stock markets based on the wrapper ANFIS (Adaptive Neural Fuzzy Inference System) – ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick is presented. Two approaches of Raw-based and Signal-based are devised to extract the model’s input variables and buy and sell signals are considered as output variables. To illustrate the methodologies, for the first and second approaches, Tehran Stock Exchange (TSE) data for the period from 2002 to 2012 are applied, while for the third approach, we used General Motors and Dow Jones indexes.154 - Katedra financívyhově

Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis

Identifying investment patterns as part of customer segmentation is one of the most important tasks in retail banking. Clustering customers efectively is an important element of improving marketing policy and strategic planning. There are several methods for identifying similar groups of customers and describing their characteristics to ofer them appropriate products. However, using machine learning methods is rare, and the application is limited for certain types of data. The aim of this study is to investigate the benefts of using a two-stage clustering method using neural-network-based Kohonen self-organizing maps followed by hierarchical clustering for identifying the investment patterns of potential retail banking customers. The unique beneft of this method is the ability to use both categorical and numerical variables at the same time. This research examined 1,542 responses received for an online investment survey, focusing on the questions that are related to the respondents’ investment preferences and their current fnancial assets. The research utilizes descriptive statistics and multiple correspondence analysis (MCA) to understand the variables and Kohonen self-organizing maps (SOMs), in combination with hierarchical clustering, to identify customer groups and describe the characteristics of these clusters. The analysis was able to identify clusters of potential customers with similar preferences and gained insights into their investment patterns related to their investment portfolio and investment behavior, including their savings profle, attitude to risk-taking, and preferences for investment advice. These fndings were supported by additional insights through the application of multiple correspondence analysis (MCA) describing patterns of fnancial instruments and portfolios. The main contribution of the research is the combined application of the machine learning methods Kohonen SOM, hierarchical clustering, and MCA for investment pattern analysis in the retail banking business

Machine Learning-Driven Decision Making based on Financial Time Series

L'abstract è presente nell'allegato / the abstract is in the attachmen

Artificial Intelligence in Asset Management

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways

News Analytics for Financial Decision Support

This PhD thesis contributes to the newly emerged, growing body of scientific work on the use of News Analytics in Finance. Regarded as the next significant development in Automated Trading, News Analytics extends trading algorithms to incorporate information extracted from textual messages, by translating it into actionable, valuable knowledge. The thesis addresses one main theme: the incorporation of news into trading algorithms. This relates to three main tasks: i) the extraction of the information contained in news, ii) the representation of the information contained in news, and iii) the aggregation of this information into actionable knowledge. We validate our approach by designing and implementing three semantic systems: a system for the computational content analysis of European Central Bank statements, a system for incorporating news in stock trading strategies, and a time-aware system for trading based on analyst recommendations. The approach we choose for addressing these tasks is an interdisciplinary one. For the extraction of information from news we rely on approaches borrowed from Computer Science and Linguistics. The representation of the information contained in news is realized by using, and extending, the state-of-the-art in Semantic Web technology. We do this by bringing together insights from Logics, Metaphysics, and Computational Semantics. The aggregation of information is done by using techniques and results from Computational Intelligence and Financ

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation