Machine Learning Meets Markowitz - SSRN
Researchers have developed an end-to-end machine learning system that combines return prediction and portfolio optimization, directly reflecting investor preferences and real-world trading limits. Tested on data from over 5400 Chinese stocks between 2010 and 2023, the model consistently outperformed traditional methods, delivering higher returns and Sharpe ratios. It adapts to transaction costs and risk appetites, learns to prioritize useful signals, and introduces new methods like optimization layers in neural networks. The study is limited to the Chinese market.
What it examines
This paper introduces an end-to-end machine learning framework for portfolio optimization, directly linking asset return prediction with investor preferences and real-world constraints. The approach aims to improve investment outcomes by integrating prediction and decision-making, addressing the limitations of traditional two-stage methods.
What it concludes
The study finds that the end-to-end framework outperforms traditional methods, especially under real-world constraints like transaction costs and risk preferences. Potential applications include personalized investment strategies, tax-aware portfolio management, and asset-liability optimization for pensions. Future research may extend the framework to tax and liability considerations.
Evidence objects
A new end-to-end machine learning framework revolutionizes portfolio management by unifying return prediction and optimization, directly addressing investor preferences and real-world trading constraints like transaction costs and short-selling bans.
key_findings bullet 1 · key_findings · validation V0
Tested on over 5,400 Chinese stocks from 2010 to 2023, the model consistently outperforms traditional methods, delivering higher returns and Sharpe ratios while dynamically adapting to transaction costs and individual risk appetites.
key_findings bullet 2 · key_findings · validation V0
Notably, the approach integrates optimization layers into neural networks, prioritizes practical signals, and reduces costly trading, though its focus on a single market and simplified risk calculations are key limitations.
key_findings bullet 3 · key_findings · validation V0
This paper introduces an original end-to-end machine learning framework for portfolio optimization, uniquely integrating return prediction and portfolio construction while explicitly modeling investor heterogeneity, real-world constraints, and transaction costs. Its novelty lies in surpassing the traditional 'predict then optimize' paradigm, demonstrating empirical superiority and practical relevance, making it compelling and impactful.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
For fundamental characteristics derived from financial statements, we strictly ensure that the data is incorporated only after its public announcement. The
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