Summary of Deep Reinforcement Learning and Mean-variance Strategies For Responsible Portfolio Optimization, by Fernando Acero et al.
Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization
by Fernando Acero, Parisa Zehtabi, Nicolas Marchesotti, Michael Cashmore, Daniele Magazzeni, Manuela Veloso
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the application of deep reinforcement learning in portfolio optimization, specifically incorporating Environmental, Social, and Governance (ESG) objectives. Traditional mean-variance optimization is extended to incorporate ESG considerations, and the authors compare the performance of deep reinforcement learning policies with modified mean-variance approaches. The results demonstrate competitive performance for responsible portfolio allocation across different utility functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses artificial intelligence to help make investment decisions more responsible and fair. It looks at two ways to optimize a portfolio: one that only considers how much money you’ll make, and another that also takes into account the environmental, social, and governance impact of your investments. The authors test these different approaches using a special kind of AI called deep reinforcement learning and find that they can be just as good as more traditional methods. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
