# Deep Portfolio Optimization via Distributional Prediction of Residual Factors

arXiv: https://arxiv.org/abs/2012.07245 · HTML: https://mlsanity.com/paper/2012.07245/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [From Deep Learning to LLMs: A survey of AI in Quantitative Investment](https://mlsanity.com/paper/2503.21422/) (2025)
- [Langevin dynamics based algorithm e-THvarepsilonO POULA for stochastic optimization problems with discontinuous stochastic gradient](https://mlsanity.com/paper/2210.13193/) (2022)
- [Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction](https://mlsanity.com/paper/2106.04114/) (2021)
- [Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty](https://mlsanity.com/paper/2210.17030/) (2022)
- [Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence Forecasting](https://mlsanity.com/paper/2312.16790/) (2023)
- [Financial Assets Dependency Prediction Utilizing Spatiotemporal Patterns](https://mlsanity.com/paper/2406.11886/) (2024)
- [Dynamic Objective Selection with Safeguards and LLM Oversight for Financial Decision-Making](https://mlsanity.com/paper/2606.03704/) (2026)

## Abstract (arXiv)

Recent developments in deep learning techniques have motivated intensive research in machine learning-aided stock trading strategies. However, since the financial market has a highly non-stationary nature hindering the application of typical data-hungry machine learning methods, leveraging financial inductive biases is important to ensure better sample efficiency and robustness. In this study, we propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors, which is known to be generally useful for hedging the risk exposure to common market factors. The key technical ingredients are twofold. First, we introduce a computationally efficient extraction method for the residual information, which can be easily combined with various prediction algorithms. Second, we propose a novel neural network architecture that allows us to incorporate widely acknowledged financial inductive biases such as amplitude invariance and time-scale invariance. We demonstrate the efficacy of our method on U.S. and Japanese stock market data. Through ablation experiments, we also verify that each individual technique contributes to improving the performance of trading strategies. We anticipate our techniques may have wide applications in various financial problems.