Paper: arXiv 2510.26228

Authors: Nikolas Anic, Andrea Barbon, Ralf Seiz, Carlo Zarattini

Abstract

This paper investigates whether large language models (LLMs) can improve cross-sectional momentum strategies by extracting predictive signals from firm-specific news. We combine daily U.S. equity returns for S&P 500 constituents with high-frequency news data and use prompt-engineered queries to ChatGPT that inform the model when a stock is about to enter a momentum portfolio. The LLM evaluates whether recent news supports a continuation of past returns, producing scores that condition both stock selection and portfolio weights. An LLM-enhanced momentum strategy outperforms a standard long-only momentum benchmark, delivering higher Sharpe and Sortino ratios both in-sample and in a truly out-of-sample period after the model’s pre-training cut-off. These gains are robust to transaction costs, prompt design, and portfolio constraints, and are strongest for concentrated, high-conviction portfolios. The results suggest that LLMs can serve as effective real-time interpreters of financial news, adding incremental value to established factor-based investment strategies.

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 9.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper’s core mathematical complexity is moderate, focusing on standard momentum signal construction and portfolio weighting, with no advanced statistical theory or heavy derivations. However, it demonstrates high empirical rigor by utilizing high-frequency news and equity datasets, conducting in-sample and out-of-sample backtests with robustness checks against transaction costs and prompt variations, and reporting performance metrics like Sharpe and Sortino ratios.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Can LLMs enhance<br>cross-sectional momentum<br>strategies using news?"]

  B["Data & Inputs<br>Daily S&P 500 Returns +<br>High-Frequency News Data"]

  C["Core Methodology<br>ChatGPT Prompt Engineering<br>Stock enters momentum portfolio?"]

  D["Computational Process<br>LLM Evaluation Score<br>Conditions Selection & Weights"]

  E["Key Outcome 1<br>LLM-Enhanced Strategy<br>Outperforms Benchmark"]

  F["Key Outcome 2<br>Higher Sharpe & Sortino Ratios<br>Robust Out-of-Sample Results"]

  A --> B
  B --> C
  C --> D
  D --> E
  D --> F

Cited References (15)

  1. Chronologically Consistent Large Language Models — → 2502.21206
  2. Returns to buying winners and selling losers: Implications for stock market efficiency (1993)
  3. Momentum strategies (1996)
  4. On persistence in mutual fund performance (1997)
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  6. A unified theory of underreaction, momentum trading, and overreaction in asset markets (1999)
  7. Giving content to investor sentiment: The role of media in the stock market (2007)
  8. When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks (2011)
and 7 more
  1. Text mining for market prediction: A systematic review (2014)
  2. Predicting returns with text data (2019)
  3. Empirical asset pricing via machine learning (2020)
  4. Can chatgpt forecast stock price movements? return predictability and large language models — → 2304.07619
  5. Financial statement analysis with large language models — → 2407.17866
  6. Automated machine learning (2019)
  7. Explanatory model analysis: explore, explain, and examine predictive models (2021)

3 of 15 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.