The List / Categories
This collection covers stock prediction, trading agents and portfolio decisions. Reported research performance depends on data timing, market conditions and execution assumptions; it does not establish future returns.
The agents and benchmarks guides help separate model capability from environment and backtest design.
GPT-4 accurately predicts stock market reactions to news headlines with ~90% hit rates and significant drift predictability, particularly for small caps and negative news, though strategy returns decline as LLM adoption increases.
This study demonstrates that ChatGPT-generated sentiment scores from corporate annual reports regarding environmental policy effectively predict firms' risk management adjustments and subsequent stock return performance.
This paper demonstrates that LLMs, particularly GPT-4 with Chain-of-Thought prompting, can outperform traditional statistical and machine learning baselines in forecasting NASDAQ-100 stock returns while providing human-readable explanations derived from multi-modal financial data.
InvestLM is a 65B parameter financial LLM instruction-tuned on a curated dataset of ~1,300 high-quality examples, demonstrating expert-rated performance comparable to GPT-3.5/4 and strong generalization across financial NLP benchmarks.
The paper proposes a framework using ChatGPT to infer dynamic stock networks from financial news, which are then processed by a Graph Neural Network and LSTM to predict stock movements, outperforming baselines in both classification accuracy and portfolio performance.
The study demonstrates that the OPT large language model significantly outperforms BERT, FinBERT, and the Loughran-McDonald dictionary in analyzing U.S. financial news sentiment, achieving a Sharpe ratio of 3.05 for a long-short trading strategy based on its predictions.
This paper evaluates ChatGPT's zero-shot capability in multimodal stock movement prediction, finding it underperforms traditional baselines and specialized deep learning models despite the utility of tweet data, while highlighting its limitations in reasoning stability and explainability.
Discovered through citation/reference expansion; connected to 2 source papers.
This paper empirically demonstrates that off-the-shelf LLMs suffer from severe hallucinations in financial tasks, but accuracy can be significantly restored using Retrieval Augmented Generation (RAG) and prompt-based tool learning for external API calls.
This study demonstrates that incorporating FinBERT-derived sentiment scores from New York Times news summaries significantly improves the accuracy of LSTM-based S&P 500 index forecasts compared to models using only historical price data.
Alpha-GPT introduces a human-AI interactive framework for quantitative alpha mining, leveraging LLMs to translate trading ideas into formulaic factors and achieving top-10 global rankings in the WorldQuant International Quant Championship 2024.
MarketSenseAI leverages GPT-4 with Chain of Thought and In-Context Learning to analyze multi-source financial data for stock selection, achieving up to 72% cumulative returns and 10-30% excess alpha on S&P 100 stocks over 15 months.
The paper demonstrates that instruction-fine-tuning Llama-3-8B with 4-bit QLoRA on a dataset combining earnings transcripts, financial metrics, and external market factors significantly outperforms GPT-4 in predicting next-day stock direction following earnings announcements.
LLMFactor introduces Sequential Knowledge-Guided Prompting to extract explainable factors from news and historical prices, achieving superior stock movement prediction accuracy over keyphrase, sentiment, and time-series baselines across US and Chinese markets.
This study compares FinBERT, GPT-4, and Logistic Regression for predicting the NGX All-Share Index using financial news, finding that optimized Logistic Regression outperforms both advanced LLMs in accuracy and efficiency.
The paper develops a fused large language model combining BERT-generated embeddings of textual self-descriptions with fundamental variables from Crunchbase profiles to predict startup success, achieving a statistically significant improvement in balanced accuracy and a substantial increase in estimated investment ROI compared to baselines using only structured data or traditional text representations.
FinVis-GPT is a multimodal large language model fine-tuned on a proprietary dataset of Chinese A-share stock charts that outperforms general-purpose vision-language models in generating descriptions, answering financial questions, and predicting market trends.
This conceptual paper proposes a research agenda for deploying large language models in financial advice, identifying domain expertise, trustworthiness, and regulatory compliance as critical challenges that must be addressed before LLMs can safely replace or augment human financial advisors.
The paper proposes a hybrid LSTM-GRU model integrated with FinBERT-derived social media sentiment to predict Ethereum and Solana prices, demonstrating superior accuracy over baseline models.
The paper demonstrates that anonymizing company names in news headlines mitigates the 'distraction effect' in GPT-3.5 sentiment analysis, leading to superior in-sample trading performance compared to original headlines where look-ahead bias is outweighed by negative interference from the model's pre-existing knowledge.
FinLlama fine-tunes Llama 2 7B using LoRA for financial sentiment analysis, achieving superior cumulative returns and Sharpe ratios in long-short portfolio construction compared to FinBERT and lexicon-based methods.
The paper demonstrates that small LLMs (under 1.5B parameters) can outperform larger generalist and specialized financial models in financial sentiment analysis and related tasks through a two-stage adaptation process involving further pre-training on financial documents and instruction fine-tuning with synthetic data augmentation.
Ploutos is a financial LLM framework that combines multimodal expert analysis with a novel training strategy to achieve state-of-the-art stock movement prediction accuracy and high-quality interpretable rationales.
The paper proposes SCRL-LG, a framework using Self-Correlated Reinforcement Learning to align LLM-generated news embeddings with quantitative stock features, significantly improving stock return prediction accuracy and portfolio performance in the China A-share market.
This paper demonstrates that fine-tuning encoder-only and decoder-only LLMs on financial news to directly predict stock returns yields superior portfolio performance compared to conventional sentiment-based methods, with decoder models like Mistral showing robustness across diverse investment universes.
StockGPT is a lightweight decoder-only transformer trained on 70 million daily US stock returns that automatically learns predictive patterns, yielding long-short portfolios with significant alphas against standard asset pricing factors.
This study demonstrates that GPT-4-32k, prompted with financial fundamentals and sentiment scores, outperforms traditional Wall Street analysts in predicting S&P 500 stock ratings based on forward returns, while revealing that raw news text introduces bias compared to structured sentiment inputs.
The FINSABER framework reveals that LLM-based investing strategies fail to outperform simple benchmarks like Buy-and-Hold over long horizons and broad universes due to survivorship bias in prior evaluations and poor regime-aware risk control.
The ECC Analyzer framework leverages LLMs to extract fine-grained textual and audio features from earnings conference calls, achieving a 27.7% reduction in MSE for short-term stock volatility prediction compared to state-of-the-art baselines.
FinanceQA is a benchmark evaluating LLMs on realistic financial analysis tasks, revealing that state-of-the-art models fail approximately 60% of professional-grade questions due to poor handling of accounting conventions and incomplete information, though fine-tuning significantly improves performance.
The paper proposes a Decision-Informed Neural Network (DINN) that integrates Large Language Model embeddings with differentiable portfolio optimization to align return predictions directly with investment decision quality, outperforming traditional prediction-focused models on S&P100 and DOW30 datasets.
Trading-R1 is a financial reasoning LLM trained via supervised fine-tuning and reinforcement learning with a three-stage curriculum to generate structured, evidence-based investment theses and volatility-adjusted trading decisions.
The paper proposes an LLM-powered Monte Carlo Tree Search framework for formulaic alpha mining that integrates multi-dimensional backtesting feedback and frequent subtree avoidance to discover interpretable, high-performing alpha factors.
This paper introduces a three-stage experimental framework to quantify latent investment biases in LLMs, revealing that models exhibit strong, persistent confirmation biases toward technology stocks, large-cap firms, and contrarian strategies, which cause them to ignore significant counter-evidence.
This paper proposes a structured six-decision framework to guide financial institutions in the responsible integration of Large Language Models, balancing innovation with regulatory compliance, data governance, and ethical oversight.
Discovered through citation/reference expansion; connected to 1 source papers.
FinRipple aligns LLMs with financial markets via time-varying knowledge graphs and CAPM-guided reinforcement learning to predict event ripple effects, significantly outperforming baselines in asset pricing and portfolio management.
LOB-Bench introduces a comprehensive benchmark for evaluating generative models of limit order book data, demonstrating that autoregressive state-space models outperform traditional parametric and GAN-based approaches in reproducing realistic market microstructure dynamics.
FinBERT2, a specialized Chinese financial bidirectional encoder pretrained on 32B tokens, outperforms leading LLMs in classification and retrieval tasks while enabling superior topic modeling, demonstrating that fine-tuned encoders remain superior to LLMs for discriminative and feature-extraction tasks in finance.
This study demonstrates that LLM-based portfolio management strategies, utilizing persona-based ensembles and economic indicators, outperform buy-and-hold in Sharpe ratio during rising CPI periods but lag during market downturns.
The paper introduces an LLM-based agentic framework for top-down sector allocation that integrates macroeconomic indicators and aspect-based sentiment analysis, achieving a Sharpe ratio of 2.51 and 8.79% return in backtests compared to a baseline cross-momentum strategy.
FinTradeBench is a benchmark evaluating LLM financial reasoning over company fundamentals and trading signals, revealing that while retrieval-augmented generation (RAG) significantly improves fundamental analysis, it degrades performance on trading signal reasoning due to LLMs' inability to parse raw numerical time-series data.
The paper proposes an LLM-based framework that integrates monetary policy transmission path reasoning and a dynamic uncertainty decoding module to achieve state-of-the-art accuracy and reliability in classifying Federal Reserve policy stances.
MIRAGE-VC introduces an information-gain-driven graph path retriever and multi-agent RAG framework to predict venture capital startup success, achieving state-of-the-art performance by distilling complex investment networks into interpretable reasoning chains.
This paper presents a due diligence dashboard for venture capital that integrates GPT-4 for document analysis and XGBoost for startup success prediction, achieving a 4.50/5.00 user satisfaction score in a case study.
BondBERT, a transformer fine-tuned on bond-specific news, outperforms general financial sentiment models by correctly capturing the inverse relationship between economic optimism and bond prices, yielding higher directional accuracy and lower forecasting error for UK sovereign bonds.
The paper introduces the AI Financial Intelligence Benchmark (AFIB) to evaluate five AI systems across five dimensions, finding that SuperInvesting achieves the highest aggregate performance in factual accuracy and analytical completeness for Indian equity research.
FinCARE integrates SEC 10-K derived knowledge graphs and LLM reasoning into causal discovery algorithms (PC, GES, NOTEARS), achieving significant F1 score improvements and reliable counterfactual scenario analysis for portfolio management.
The paper presents a reproducible workflow for fine-tuning FinBERT on a new 1,500-headline gold-standard dataset, demonstrating that while the resulting sentiment signal is reactive rather than predictive of returns, it supports profitable sector-specific long-short strategies with high Sharpe ratios.
Discovered through citation/reference expansion; connected to 2 source papers.
MM-DREX is a multimodal LLM framework that uses a vision-language router to dynamically allocate weights among four specialized trading experts, achieving superior risk-adjusted returns across stocks, futures, and crypto compared to static baselines.
PolyBench evaluates seven LLMs on live Polymarket data, revealing that only MiMo-V2-Flash and Gemini-3-Flash achieve positive financial returns, highlighting the gap between language fluency and profitable probabilistic reasoning under market uncertainty.
Discovered through citation/reference expansion; connected to 3 source papers.
AlphaForgeBench introduces a deterministic benchmark for LLMs that evaluates their ability to generate executable alpha factors and strategy code rather than direct trading actions, revealing that this approach eliminates the severe behavioral instability and non-reproducibility inherent in direct-trading agent evaluations.
CN-Buzz2Portfolio introduces a rolling-horizon benchmark mapping Chinese daily trending news to macro and sector ETF allocation, revealing that LLMs generate significant alpha in sector rotation but struggle in sideways markets due to regime-dependent efficacy and narrative hallucination.
The paper proposes an intelligent trade order recognition pipeline using five LLMs to convert natural language trading instructions into standard JSON formats, revealing high syntactic validity but low end-to-end accuracy and excessive unnecessary clarifications.
The paper proposes a dynamic hedging framework for derivatives that integrates LLM-driven sentiment analysis from news and social media to adjust positions in real-time, achieving superior risk-adjusted returns compared to static hedging baselines.
The paper introduces a decoupled training mixture model that adaptively combines quantitative factor predictions with LLM-generated newsflow representations, achieving superior stock return prediction and portfolio performance compared to standard fusion learning, particularly in long-short strategies.
FinReasoning introduces a hierarchical benchmark decomposing financial research into semantic consistency, data alignment, and deep insight, revealing that closed-source models outperform open-source and financial-domain models in multi-agent role suitability.
FinSheet-Bench evaluates LLMs on synthetic private equity spreadsheet QA, revealing that even top models like Gemini 3.1 Pro achieve only 82.4% accuracy, with performance collapsing on complex aggregations, indicating a need for hybrid architectures separating parsing from computation.
The paper introduces Option Query Language (OQL), a neuro-symbolic pipeline that translates natural language trading intents into executable option strategies by using LLMs as semantic parsers for a domain-specific intermediate representation, significantly improving execution accuracy and risk management over direct code generation baselines.
Discovered through citation/reference expansion; connected to 1 source papers.
The provided paper text describes KD-MSLRT, a lightweight sign language recognition model using knowledge distillation, which is unrelated to the cataloged paper 'LLM-Guided Evolutionary Strategy Generation for Quantitative Trading' (LLM-GA) that uses LLMs and genetic algorithms for quantitative trading strategy generation.
This study evaluates GPT-4's ability to predict earnings direction and stock returns in China's A-share market using anonymized financial statements, revealing high confidence but low and inconsistent accuracy with no reliable correlation to actual returns.
QuantCode-Bench evaluates LLMs on generating executable Backtrader trading strategies, revealing that while syntax is mastered, semantic alignment and logic operationalization remain significant challenges even for frontier models.
The paper demonstrates that combining FinBERT-extracted sentiment features with Bayesian-optimized feature selection significantly improves SPY ETF return sign prediction and cumulative trading profits compared to existing benchmarks.
This paper presents a two-stage task-adaptive LLM framework that combines granular sentiment extraction with a six-dimensional cognitive reasoning model to generate persuasive investment reports from earnings call transcripts, achieving top rankings in the FinNLP 2025 Earnings2Insights shared task.