Financial Statement Analysis with Large Language Models
“Even without narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes directionally.”
StrategyNews sentiment
TechniqueLarge language model
ClaimSharpe ratioalpha
InputsFundamentalstext and news
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
Institutional-grade audit of this strategy.
£5 — Request the audit- Audit of the paper claims accounting for costs, luck, crises, originality, geography, etc.
- Reproduction code integratable into Bloomberg, IBKR, etc.
- Bias-free datasets for backtesting included
- Delivered in 2 working days
The paper's neighbourhoodNews sentiment and text signals by year
FamilyNews sentiment and text signals148Momentum and trend following289Factor investing240Machine learning return prediction15318 more · 854
AssetEquities97
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
| Similar papers | citation | same method | same asset | same authors | |
|---|---|---|---|---|---|
| Execution-Aware Alpha Mining: Teaching LLM Factor Agents to Account for Trading Costs by Nagireddy | 2026 | ||||
| Memorisation or Alpha? Detecting Look-Ahead Contamination in Cross-Sectional Equity Signals by Nguyen | 2026 | ||||
| Beta Recall, Alpha Recall, and a Contamination Detector that Needs No Labels * Measuring Training-data Leakage in LLM Equity Signals by Nguyen | 2026 | ||||
| Crude oil arbitrage and momentum trading strategies with targeting volatility by using Large Language Models by Garmash | 2026 | ||||
| LLM-Based High-Growth Potential Ranking and Portfolio Selection: Evidence from China's Optical Industry by Lin and Xue | 2026 |