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Original title:
Recent Trends in Machine Learning with a Focus on Applications in Finance
Authors: Kalina, Jan ; Neoral, Aleš
Document type: Papers
Conference/Event: International Days of Statistics and Economics /16./, Praha (CZ), 20220908
Year: 2022
Language: eng
Abstract: Machine learning methods penetrate to applications in the analysis of financial data, particularly to supervised learning tasks including regression or classification. Other approaches, such as reinforcement learning or automated machine learning, are not so well known in the context of finance yet. In this paper, we discuss the advantages of an automated data analysis, which is beneficial especially if a larger number of datasets should be analyzed under a time pressure. Important types of learning include reinforcement learning, automated machine learning, or metalearning. This paper overviews their principles and recalls some of their inspiring applications. We include a discussion of the importance of the concept of information and of the search for the most relevant information in the field of mathematical finance. We come to the conclusion that a statistical interpretation of the results of theautomatic machine learning remains crucial for a proper understanding of the knowledge acquired by the analysis of the given (financial) data.
Keywords: automated machine learning; financial data analysis; metalearning; statistical learning; stock market investing
Project no.: GA21-19311S (CEP), GA22-02067S (CEP)
Funding provider: GA ČR, GA ČR
Host item entry: The 16th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-29-2
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://msed.vse.cz/msed_2022/article/577-Kalina-Jan-paper.pdf
Original record: https://hdl.handle.net/11104/0336174
Permalink: http://www.nusl.cz/ntk/nusl-511782
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Authors: Kalina, Jan ; Neoral, Aleš
Document type: Papers
Conference/Event: International Days of Statistics and Economics /16./, Praha (CZ), 20220908
Year: 2022
Language: eng
Abstract: Machine learning methods penetrate to applications in the analysis of financial data, particularly to supervised learning tasks including regression or classification. Other approaches, such as reinforcement learning or automated machine learning, are not so well known in the context of finance yet. In this paper, we discuss the advantages of an automated data analysis, which is beneficial especially if a larger number of datasets should be analyzed under a time pressure. Important types of learning include reinforcement learning, automated machine learning, or metalearning. This paper overviews their principles and recalls some of their inspiring applications. We include a discussion of the importance of the concept of information and of the search for the most relevant information in the field of mathematical finance. We come to the conclusion that a statistical interpretation of the results of theautomatic machine learning remains crucial for a proper understanding of the knowledge acquired by the analysis of the given (financial) data.
Keywords: automated machine learning; financial data analysis; metalearning; statistical learning; stock market investing
Project no.: GA21-19311S (CEP), GA22-02067S (CEP)
Funding provider: GA ČR, GA ČR
Host item entry: The 16th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-29-2
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://msed.vse.cz/msed_2022/article/577-Kalina-Jan-paper.pdf
Original record: https://hdl.handle.net/11104/0336174
Permalink: http://www.nusl.cz/ntk/nusl-511782
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Record created 2022-11-27, last modified 2023-12-06