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Advanced Portfolio Management in Big Data Environments With Machine Learning and Advanced Analytical Techniques

Advanced Portfolio Management in Big Data Environments With Machine Learning and Advanced Analytical Techniques

Goran Klepac, Leo Mršić, Robert Kopal
Copyright: © 2022 |Pages: 25
ISBN13: 9781799886860|ISBN10: 1799886867|EISBN13: 9781799886877
DOI: 10.4018/978-1-7998-8686-0.ch016
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MLA

Klepac, Goran, et al. "Advanced Portfolio Management in Big Data Environments With Machine Learning and Advanced Analytical Techniques." Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning, edited by Maki K. Habib, IGI Global, 2022, pp. 413-437. https://doi.org/10.4018/978-1-7998-8686-0.ch016

APA

Klepac, G., Mršić, L., & Kopal, R. (2022). Advanced Portfolio Management in Big Data Environments With Machine Learning and Advanced Analytical Techniques. In M. Habib (Ed.), Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning (pp. 413-437). IGI Global. https://doi.org/10.4018/978-1-7998-8686-0.ch016

Chicago

Klepac, Goran, Leo Mršić, and Robert Kopal. "Advanced Portfolio Management in Big Data Environments With Machine Learning and Advanced Analytical Techniques." In Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning, edited by Maki K. Habib, 413-437. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-8686-0.ch016

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Abstract

The chapter will propose a novel approach that combines the traditional machine learning approach in churn management and customer satisfaction evaluation, which unite traditional machine learning approach and expert-based approach, which leans on event-based management. The core of the proposed framework is hybrid fuzzy expert system, which can contain a variety of data mining predictive models responsible for some specific areas as additions to traditional rule blocks. It can also include social network analysis metrics based on linguistic variables and incorporated within rule blocks. The chapter will introduce how revealed patterns can be applied for continual portfolio management improvement. The proposed solution unites advanced analytical techniques with the decision-making process within a holistic self-learning framework.

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