# Aligning Language Models with Investor and Market Behavior for Financial Recommendations

arXiv: https://arxiv.org/abs/2510.15993 · HTML: https://mlsanity.com/paper/2510.15993/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Preference alignment for recommenders and RAG, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Preference alignment for recommenders and RAG](https://mlsanity.com/alignment/preference-alignment-for-recommenders-and-rag/)

## Builds on (most cited first, 11 of 11)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://mlsanity.com/paper/2005.09683/) (2020)
- [InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning](https://mlsanity.com/paper/2309.13064/) (2023)
- [Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems](https://mlsanity.com/paper/2109.04584/) (2021)
- [FAR-Trans: An Investment Dataset for Financial Asset Recommendation](https://mlsanity.com/paper/2407.08692/) (2024)
- [Federated Fine-Tuning of Large Language Models: Kahneman-Tversky vs. Direct Preference Optimization](https://mlsanity.com/paper/2502.14187/) (2025)
- [Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Mean-Variance Efficient Sampling](https://mlsanity.com/paper/2404.07223/) (2024)
- [Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations](https://mlsanity.com/paper/2509.07133/) (2025)

## Built on by (most cited first, 1 of 1)

- [Federated Personal Knowledge Graph Completion with Lightweight Large Language Models for Personalized Recommendations](https://mlsanity.com/paper/2603.13264/) (2026)

## Abstract (arXiv)

Most financial recommendation systems often fail to account for key behavioral and regulatory factors, leading to advice that is misaligned with user preferences, difficult to interpret, or unlikely to be followed. We present FLARKO (Financial Language-model for Asset Recommendation with Knowledge-graph Optimization), a novel framework that integrates Large Language Models (LLMs), Knowledge Graphs (KGs), and Kahneman-Tversky Optimization (KTO) to generate asset recommendations that are both profitable and behaviorally aligned. FLARKO encodes users' transaction histories and asset trends as structured KGs, providing interpretable and controllable context for the LLM. To demonstrate the adaptability of our approach, we develop and evaluate both a centralized architecture (CenFLARKO) and a federated variant (FedFLARKO). To our knowledge, this is the first demonstration of combining KTO for fine-tuning of LLMs for financial asset recommendation. We also present the first use of structured KGs to ground LLM reasoning over behavioral financial data in a federated learning (FL) setting. Evaluated on the FAR-Trans dataset, FLARKO consistently outperforms state-of-the-art recommendation baselines on behavioral alignment and joint profitability, while remaining interpretable and resource-efficient.