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Reinforcement Machine Learning Optimization Algorithms for the Computation of Downside Risk and Investable Portfolios in Post 2007–2009 Financial Meltdown

In: Artificial Intelligence and Beyond for Finance

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  • Mazin A.M. Al Janabi

Abstract

The objective of this chapter is to examine reinforcement machine learning quadratic optimization techniques for the computation of downside-risk limits and investable portfolios in post 2007–2009 global financial meltdown. The modeling techniques are based on the notion of Liquidityadjusted Value-at-Risk (LVaR) as well as the application of reinforcement machine learning optimization algorithms with meaningful financial and operational constraints. In this chapter, some simulation case studies are presented for the computation of downside-risk limits and investable portfolios. The applied risk valuation techniques and quadratic optimization algorithms can help in advancing reinforcement machine learning methods, risk computations, and portfolio management practices in the wake of the 2007–2009 global financial turmoil.

Suggested Citation

  • Mazin A.M. Al Janabi, 2024. "Reinforcement Machine Learning Optimization Algorithms for the Computation of Downside Risk and Investable Portfolios in Post 2007–2009 Financial Meltdown," World Scientific Book Chapters, in: Marco Corazza & René Garcia & Faisal Shah Khan & Davide La Torre & Hatem Masri (ed.), Artificial Intelligence and Beyond for Finance, chapter 10, pages 337-357, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9781800615212_0010
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    More about this item

    Keywords

    Artificial Intelligence; Machine Learning; Deep Learning; Reinforcement Learning; Sentiment Analysis; Portfolio Management; Financial Forecasting;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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