---
title: Portfolio Choice with Genetic Programming
url: https://www.ml-quant.com/papers/ssrn/4793204/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4793204
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4793204
featured: 2024-04-17
citations: unknown
topic: Portfolio & Allocation
---


# Portfolio Choice with Genetic Programming

A new method for creating efficient portfolios using genetic programming and economic constraints has been developed, which doubles the out-of-sample Sharpe ratio of existing methods.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4793204
- Identifier: SSRN 4793204
- Released: 2024-01-16
- First featured: Quant Letter No. 45 (2024-04-17): https://www.ml-quant.com/issues/2024-04-17/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [ESG and Behavioral Finance: Why ESG Investing Is Primarily a Psychological Phenomenon](https://www.ml-quant.com/papers/ssrn/4786610/): A Psychological Phenomenon: The study expands traditional portfolio selection and asset pricing theory to include ESG investing, introducing two behavioral innovations related to investor preferences and biased judgments about ESG impact and return distributions.
- [Optimal Rebalancing in Dynamic AMMs](https://www.ml-quant.com/papers/arxiv/2403.18737/): A new method for optimally rebalancing asset ratios in Dynamic Automated Market Maker pools could potentially increase pool profit and loss by about 25% for a BTC-ETH-DAI pool from July 2022 to June 2023.
- [Deep Reinforcement Learning: Extending Traditional Financial Portfolio Methods](https://www.ml-quant.com/papers/ssrn/4780026/): The paper suggests that deep reinforcement learning can potentially improve traditional portfolio allocation strategies by incorporating contextual data and future rewards.
- [GRIP: Graphical Models Revealing Insights for Portfolio Replication - A Learning Approach](https://www.ml-quant.com/papers/ssrn/4780148/): The paper introduces a new method for decoding investment portfolio strategies using Dynamic Bayesian Graphical Models, resulting in better portfolio allocation decisions and adaptability to various market conditions.
- [Who Clears the Market When Passive Investors Trade?](https://www.ml-quant.com/papers/ssrn/4777585/): The article investigates the role of firms in providing shares to passive investors, particularly in response to index funds' buying.
- [Machine Learning Mutual Fund Flows](https://www.ml-quant.com/papers/ssrn/4812038/): Nonlinear machine learning models are more effective than linear models in predicting future fund flows, with past flows and the Morningstar rating as key predictors.