# Risk of Transfer Learning and its Applications in Finance

arXiv: https://arxiv.org/abs/2311.03283 · HTML: https://mlsanity.com/paper/2311.03283/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 47 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [LEEP: A New Measure to Evaluate Transferability of Learned Representations](https://mlsanity.com/paper/2002.12462/) (2020)
- [On the Theory of Transfer Learning: The Importance of Task Diversity](https://mlsanity.com/paper/2006.11650/) (2020)
- [LogME: Practical Assessment of Pre-trained Models for Transfer Learning](https://mlsanity.com/paper/2102.11005/) (2021)
- [Deep Signature Transforms](https://mlsanity.com/paper/1905.08494/) (2019)
- [Universal features of price formation in financial markets: perspectives from Deep Learning](https://mlsanity.com/paper/1803.06917/) (2018)
- [Frustratingly Easy Transferability Estimation](https://mlsanity.com/paper/2106.09362/) (2021)
- [Stratified Transfer Learning for Cross-domain Activity Recognition](https://mlsanity.com/paper/1801.00820/) (2017)
- [DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns](https://mlsanity.com/paper/1905.10760/) (2019)
- [Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks](https://mlsanity.com/paper/2006.10581/) (2020)
- [Learning a functional control for high-frequency finance](https://mlsanity.com/paper/2006.09611/) (2020)
- [Transfer Learning for Retinal Vascular Disease Detection: A Pilot Study with Diabetic Retinopathy and Retinopathy of Prematurity](https://mlsanity.com/paper/2201.01250/) (2022)
- [Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy](https://mlsanity.com/paper/2209.05709/) (2022)
- [Feasibility and Transferability of Transfer Learning: A Mathematical Framework](https://mlsanity.com/paper/2301.11542/) (2023)

## Built on by (most cited first, 5 of 5)

- [Fast Policy Learning for Linear Quadratic Control with Entropy Regularization](https://mlsanity.com/paper/2311.14168/) (2023)
- [Schr"odinger bridge for generative AI: Soft-constrained formulation and convergence analysis](https://mlsanity.com/paper/2510.11829/) (2025)
- [When to Transfer: Adaptive Source Selection for Positive Transfer in Linear Models](https://mlsanity.com/paper/2510.16986/) (2025)
- [Signature Approach for Contextual Bandits with Nonlinear and Path-dependent Rewards](https://mlsanity.com/paper/2605.10313/) (2026)
- [Sample Complexity of Transfer Learning: An Optimal Transport Approach](https://mlsanity.com/paper/2605.20545/) (2026)

## Abstract (arXiv)

Transfer learning is an emerging and popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones. In this paper, we propose a novel concept of transfer risk and and analyze its properties to evaluate transferability of transfer learning. We apply transfer learning techniques and this concept of transfer risk to stock return prediction and portfolio optimization problems. Numerical results demonstrate a strong correlation between transfer risk and overall transfer learning performance, where transfer risk provides a computationally efficient way to identify appropriate source tasks in transfer learning, including cross-continent, cross-sector, and cross-frequency transfer for portfolio optimization.