arXiv:2605.28853v1 Announce Type: cross Abstract: Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction costs. Standard predict-then-optimize methods first forecast returns and then solve for weights, compounding prediction errors and often failing under regime shifts. We propose an end-to-end framework that directly optimizes differentiable surrogates of key financial metrics - Sharpe ratio, Omega ratio, Conditional Value-at-Risk (CVaR), and Risk Parity - allowing neural networks to learn portfolio weights via
The rapid advancement in deep learning techniques combined with increasing computational power makes sophisticated AI-driven portfolio optimization more feasible than ever before, addressing long-standing challenges in financial markets.
This development could significantly alter asset management strategies by enabling more dynamic and resilient portfolio construction, potentially leading to superior risk-adjusted returns and reduced prediction errors.
Traditional two-step 'predict-then-optimize' portfolio management approaches will be challenged by integrated, end-to-end deep learning models directly optimizing financial metrics, reducing reliance on explicit return forecasts.
- · Quantitative hedge funds
- · AI/ML financial technology providers
- · Sophisticated institutional investors
- · Traditional asset managers
- · Investment firms reliant on static models
- · Retail investors without advanced tools
More efficient capital allocation and potentially higher market volatility due to faster algorithmic responses.
Increased concentration of wealth and power among entities with access to and expertise in these advanced AI systems for finance.
Regulatory scrutiny will intensify over the opacity and systemic risks posed by deeply integrated AI in financial markets, potentially leading to new compliance frameworks.
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Read at arXiv cs.LG