Bachelor Thesis from the year 2024 in the subject Computer Science - Commercial Information Technology, grade: 1.0, Frankfurt School of Finance & Management, language: English, abstract: In a world characterized by increasingly complex financial markets, the prediction of financial crises is a constant challenge. This bachelor thesis investigates the use of machine learning, in particular regression algorithms, to analyze and predict financial crises based on macroeconomic data. By building six different regression models and optimizing them using cross-validation and GridSearch, the feasibility of using these technologies for accurate predictions is discussed. Although traditional models show limited effectiveness, the integration of machine learning, especially kNN algorithms, reveals significant potential for improving prediction accuracy. The paper highlights the importance of classification algorithms and provides crucial insights for application in real-world scenarios to provide valuable tools for policy and business decision makers.
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Zusammenfassung: This is an open access book. Computation should be a good blend of theory and practice. Researchers in the field should create algorithms to address real world problems putting equal weight to analysis and implementation. Experimentation and simulation can be viewed as yielding to refined theories or improved applications. WCTP 2023 is the twelfth workshop organized by the Tokyo Institute of Technology, The Institute of Scientific and Industrial Research-Osaka University, Chitose Institute of Science and Technology, University of the Philippines-Diliman and De La Salle University-Manila that is devoted to theoretical and practical approaches to computation. It aims to present the latest developments by theoreticians and practitioners in academe and industry working to address computational problems that can directly impact the way we live in society. WCTP 2023 will feature work-in-progress presentations of prominent researchers selected by members of its Program Committee who come from highly distinguished institutions in Japan and the Philippines. The presentation at the workshop will certainly provide high quality comments and discussion that future research can benefit from. WCTP 2023 is supported by Chitose Institute of Science and Technology, and Photonics World Consortium
Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. • Gain a solid reason to use machine learning • Frame your question using financial markets laws • Know your data • Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.
ICTAI 2019 The IEEE International Conference on Tools with Artificial Intelligence (ICTAI) is a leading Conference of AI in the Computer Society providing a major international forum where the creation and exchange of ideas related to artificial intelligence are fostered among academia, industry, and government agencies The conference facilitates the cross fertilization of AI ideas and promotes their transfer into practical tools, for developing intelligent systems and pursuing artificial intelligence applications The ICTAI encompasses all technical aspects of specifying, developing and evaluating the theoretical underpinnings and applied mechanisms of the AI based components of computer tools (i e algorithms, architectures or languages)
“The crisis has deeply impacted virtually every economy in the world, and although growth has returned, much progress in the fight against poverty has been lost. More difficult international conditions in the years to come will mean that developing countries will have to place even more emphasis on improving domestic economic conditions to achieve the kind of growth that can durably eradicate poverty.� —Justin Yifu Lin, Chief Economist and Senior Vice President The World Bank 'Global Economic Prospects 2010: Crisis, Finance, and Growth' explores both the short- and medium-term impacts of the financial crisis on developing countries. Although global growth has resumed, the recovery is fragile, and unless business and consumer demand strengthen, the world economy could slow down again. Even if, as appears likely, a double-dip recession is avoided, the recovery is expected to be slow. High unemployment and widespread restructuring will continue to characterize the global economy for the next several years. Already, the crisis has provoked large-scale human suffering. Some 64 million more people around the world are expected to be living on less than a $1.25 per day by the end of 2010, and between 30,000 and 50,000 more infants may have died of malnutrition in 2009 in Sub-Saharan Africa, than would have been the case if the crisis had not occurred. Over the medium term, economic growth is expected to recover. But increased risk aversion, a necessary and desirable tightening of financial regulations in high-income countries, and measures to reduce the exposure of developing economies to external shocks are likely to make finance scarcer and more costly than it was during the boom period. As a result, just as the ample liquidity of the early 2000s prompted an investment boom and an acceleration in developing-country potential output, higher costs will likely yield a slowing in developing-country potential growth rates of between 0.2 and 0.7 percentage points, and as much as an 8 percent decline in potential output over the medium term. In the longer term, however, developing countries can more than offset the implications of more expensive international finance by reducing the cost of capital channeled through their domestic financial markets. For more information, please visit www.worldbank.org/gep2010. To access Prospects for the Global Economy, an online companion publication, please visit www.worldbank.org/globaloutlook.
This paper explains specifics of stress testing at the IMF. After a brief section on the evolution of stress tests at the IMF, the paper presents the key steps of an IMF staff stress test. They are followed by a discussion on how IMF staff uses stress tests results for policy advice. The paper concludes by identifying remaining challenges to make stress tests more useful for the monitoring of financial stability and an overview of IMF staff work program in that direction. Stress tests help assess the resilience of financial systems in IMF member countries and underpin policy advice to preserve or restore financial stability. This assessment and advice are mainly provided through the Financial Sector Assessment Program (FSAP). IMF staff also provide technical assistance in stress testing to many its member countries. An IMF macroprudential stress test is a methodology to assess financial vulnerabilities that can trigger systemic risk and the need of systemwide mitigating measures. The definition of systemic risk as used by the IMF is relevant to understanding the role of its stress tests as tools for financial surveillance and the IMF’s current work program. IMF stress tests primarily apply to depository intermediaries, and, systemically important banks.
This book constitutes the refereed proceedings of the 25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022, which took place in Taoyuan, Taiwan, in December 2022. ICS is one of the largest joint international IT symposia held in Taiwan. Founded in 1973, it is intended to provide a forum for researchers, educators, and professionals to exchange their discoveries and practices, and to explore future trends and applications in computer technologies. The biannual symposium offers a great opportunity to share research experiences and to discuss potential new trends in the IT industry. The 58 full papers and one invited paper presented in this volume were carefully reviewed and selected from 137 submissions. The papers have been organized in the following topical sections: Invited Paper; Algorithms, Bioinformatics, and Computation Theory; Cloud Computing and Big Data; Computer Vision and Image Processing; Cryptography and Information Security; Electronics and Information Technology; Mobile Computation and Wireless Communication; Ubiquitous Cybersecurity and Forensics.
This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
This textbook offers a statistical view on the geometry of multiple view analysis, required for camera calibration and orientation and for geometric scene reconstruction based on geometric image features. The authors have backgrounds in geodesy and also long experience with development and research in computer vision, and this is the first book to present a joint approach from the converging fields of photogrammetry and computer vision. Part I of the book provides an introduction to estimation theory, covering aspects such as Bayesian estimation, variance components, and sequential estimation, with a focus on the statistically sound diagnostics of estimation results essential in vision metrology. Part II provides tools for 2D and 3D geometric reasoning using projective geometry. This includes oriented projective geometry and tools for statistically optimal estimation and test of geometric entities and transformations and their relations, tools that are useful also in the context of uncertain reasoning in point clouds. Part III is devoted to modelling the geometry of single and multiple cameras, addressing calibration and orientation, including statistical evaluation and reconstruction of corresponding scene features and surfaces based on geometric image features. The authors provide algorithms for various geometric computation problems in vision metrology, together with mathematical justifications and statistical analysis, thus enabling thorough evaluations. The chapters are self-contained with numerous figures and exercises, and they are supported by an appendix that explains the basic mathematical notation and a detailed index. The book can serve as the basis for undergraduate and graduate courses in photogrammetry, computer vision, and computer graphics. It is also appropriate for researchers, engineers, and software developers in the photogrammetry and GIS industries, particularly those engaged with statistically based geometric computer vision methods.
