FinDPO: Financial Sentiment Analysis for Algorithmic Trading through Preference Optimization of LLMs

Paper: arXiv 2507.18417

Authors: Giorgos Iacovides, Wuyang Zhou, Danilo Mandic

Abstract

Opinions expressed in online finance-related textual data are having an increasingly profound impact on trading decisions and market movements. This trend highlights the vital role of sentiment analysis as a tool for quantifying the nature and strength of such opinions. With the rapid development of Generative AI (GenAI), supervised fine-tuned (SFT) large language models (LLMs) have become the de facto standard for financial sentiment analysis. However, the SFT paradigm can lead to memorization of the training data and often fails to generalize to unseen samples. This is a critical limitation in financial domains, where models must adapt to previously unobserved events and the nuanced, domain-specific language of finance. To this end, we introduce FinDPO, the first finance-specific LLM framework based on post-training human preference alignment via Direct Preference Optimization (DPO). The proposed FinDPO achieves state-of-the-art performance on standard sentiment classification benchmarks, outperforming existing supervised fine-tuned models by 11% on the average. Uniquely, the FinDPO framework enables the integration of a fine-tuned causal LLM into realistic portfolio strategies through a novel ’logit-to-score’ conversion, which transforms discrete sentiment predictions into continuous, rankable sentiment scores (probabilities). In this way, simulations demonstrate that FinDPO is the first sentiment-based approach to maintain substantial positive returns of 67% annually and strong risk-adjusted performance, as indicated by a Sharpe ratio of 2.0, even under realistic transaction costs of 5 basis points (bps).

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper’s primary innovation is a novel framework for training and evaluating a Large Language Model, focusing on practical implementation details and backtest results with specific financial metrics, rather than complex mathematical derivations.

Research Flowchart

  flowchart TD
  A["Research Goal: Develop robust FinSA model for trading"] --> B["FinDPO Framework<br>LLM Pref. Opt. via DPO"]
  B --> C["Data: Financial Sentiment Benchmarks"]
  C --> D["Compute: Logit-to-Score Conversion<br>→ Continuous Probabilities"]
  D --> E["Simulation: Portfolio Strategy<br>with Trans. Costs"]
  E --> F{"Outcomes"}
  F --> G["SOTA Sentiment Accuracy<br>+11% over SFT"]
  F --> H["Realized Portfolio Returns<br>67% Annual / Sharpe 2.0"]

Cited References (37)

1. Good debt or bad debt: Detecting semantic orientations in economic texts (2014)
2. Twitter Financial News Sentiment (2022) — link
3. News with GPT instructions (2023) — link
4. Fixing weight decay regularization in adam (2017) — link
5. Lo RA : Low-Rank Adaptation of Large Language Models (2022)
6. Direct Preference Optimization: Your Language Model is Secretly a Reward Model (2023)
7. The llama 3 herd of models (2024) — link
8. The stock sonar—sentiment analysis of stocks based on a hybrid approach (2011)

0 of 37 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.