# A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy

arXiv: https://arxiv.org/abs/1910.01491 · HTML: https://mlsanity.com/paper/1910.01491/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction](https://mlsanity.com/paper/2310.05627/) (2023)
- [Fractional SDE-Net: Generation of Time Series Data with Long-term Memory](https://mlsanity.com/paper/2201.05974/) (2022)
- [Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity](https://mlsanity.com/paper/2208.09968/) (2022)
- [Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty](https://mlsanity.com/paper/2210.17030/) (2022)
- [The cross-sectional stock return predictions via quantum neural network and tensor network](https://mlsanity.com/paper/2304.12501/) (2023)

## Abstract (arXiv)

Stock return predictability is an important research theme as it reflects our economic and social organization, and significant efforts are made to explain the dynamism therein. Statistics of strong explanative power, called "factor" have been proposed to summarize the essence of predictive stock returns. Although machine learning methods are increasingly popular in stock return prediction, an inference of the stock returns is highly elusive, and still most investors, if partly, rely on their intuition to build a better decision making. The challenge here is to make an investment strategy that is consistent over a reasonably long period, with the minimum human decision on the entire process. To this end, we propose a new stock return prediction framework that we call Ranked Information Coefficient Neural Network (RIC-NN). RIC-NN is a deep learning approach and includes the following three novel ideas: (1) nonlinear multi-factor approach, (2) stopping criteria with ranked information coefficient (rank IC), and (3) deep transfer learning among multiple regions. Experimental comparison with the stocks in the Morgan Stanley Capital International (MSCI) indices shows that RIC-NN outperforms not only off-the-shelf machine learning methods but also the average return of major equity investment funds in the last fourteen years.