Advances in Machine Learning for Stochastic Control in Financial and Energy Markets
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Organizer(s): |
Name:
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Affiliation:
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Country:
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Nacira Agram
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KTH Royal Institute of Technology
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Sweden
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Introduction:
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The convergence of machine learning and stochastic optimal control has introduced significant advancements in addressing complex decision-making problems under uncertainty, particularly within finance and energy markets. Recent developments in machine learning algorithms are enhancing the optimization of control strategies, providing innovative solutions for dynamic market behaviors. This exploration bridges theoretical insights with real-world applications, highlighting the transformative impact of these technologies on market performance and efficiency.
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