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

Mazin A.M. Al Janabi

Chapter 10 in Artificial Intelligence and Beyond for Finance, 2024, pp 337-357 from World Scientific Publishing Co. Pte. Ltd.

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.

Keywords: Artificial Intelligence; Machine Learning; Deep Learning; Reinforcement Learning; Sentiment Analysis; Portfolio Management; Financial Forecasting (search for similar items in EconPapers)
JEL-codes: C63 C8 G11 G17 (search for similar items in EconPapers)
Date: 2024
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