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Deep Reinforcement Learning Ashwin Rao ICME, Stanford University November 14, 2020 ... Alternative approach is for a trader to play Portfolio Optimization

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.edu Applications of Machine Learning (ML) to stock market analysis include Portfolio Optimization, Investment Strategy Determination, and Market Risk Analysis. This paper focuses on the problem of Investment Strategy Determination through the use of reinforcement learning techniques.

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Portfolio optimization is an important topic in Finance. Modern portfolio theory (MPT) states that investors are risk averse and given a level of risk, they will choose the portfolios that offer the most return.

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What is the difference between "recurrent reinforcement learning" and normal "reinforcement learning" (like Q-Learning algorithm)? The RRL approach differs clearly from dynamic programming and reinforcement algorithms such as TD-learning and Q-learning, which attempt to estimate a value function for the control problem.

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Dynamic portfolio optimization is the process of sequentially allocating wealth to a collection of assets in some consecutive trading periods, based on investors' return-risk profile. Automating this process with machine learning remains a challenging problem.

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Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will help you master RL algorithms and understand their implementation as you build self-learning agents.

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