# FAR-Trans: An Investment Dataset for Financial Asset Recommendation

arXiv: https://arxiv.org/abs/2407.08692 · HTML: https://mlsanity.com/paper/2407.08692/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2024.

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

- [Conv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendation](https://mlsanity.com/paper/2602.16990/) (2026)
- [Aligning Language Models with Investor and Market Behavior for Financial Recommendations](https://mlsanity.com/paper/2510.15993/) (2025)
- [Parallel and Multi-Stage Knowledge Graph Retrieval for Behaviorally Aligned Financial Asset Recommendations](https://mlsanity.com/paper/2511.11583/) (2025)
- [Dynamic Objective Selection with Safeguards and LLM Oversight for Financial Decision-Making](https://mlsanity.com/paper/2606.03704/) (2026)
- [Fund2Persona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data](https://mlsanity.com/paper/2606.29793/) (2026)

## Abstract (arXiv)

Financial asset recommendation (FAR) is a sub-domain of recommender systems which identifies useful financial securities for investors, with the expectation that they will invest capital on the recommended assets. FAR solutions analyse and learn from multiple data sources, including time series pricing data, customer profile information and expectations, as well as past investments. However, most models have been developed over proprietary datasets, making a comparison over a common benchmark impossible. In this paper, we aim to solve this problem by introducing FAR-Trans, the first public dataset for FAR, containing pricing information and retail investor transactions acquired from a large European financial institution. We also provide a bench-marking comparison between eleven FAR algorithms over the data for use as future baselines. The dataset can be downloaded from https://doi.org/10.5525/gla.researchdata.1658 .