Study compares LLMs vs classical models for financial sentiment analysis.
problem Improving sentiment analysis in financial market news.
method Comparative analysis of LLMs and classical models.
result LLMs outperform classical models in sentiment analysis of financial news.
Study constructs a Japanese financial LLM benchmark.
problem Need for domain-specific benchmarks for LLMs.
method Constructed a benchmark with multiple Japanese and financial domain tasks.
result GPT-4 outperforms other models in the benchmark.
FINCH dataset enables financial Text-to-SQL tasks, improving model evaluation.
problem Lack of a large-scale financial dataset for Text-to-SQL tasks.
method Curated financial dataset (FINCH), benchmarking reasoning and language models.
result Proposed FINCH Score for more accurate financial model evaluation.
BloombergGPT is a large language model trained on financial data, outperforming existing models on financial tasks.
problem Lack of specialized large language models for finance.
method Trained on a 363 billion token dataset augmented with 345 billion tokens from general datasets, using a 50 billion parameter model.
result BloombergGPT outperforms existing models on financial tasks without sacrificing performance on general LLM benchmarks.
This paper benchmarks FinGPT for financial datasets using open-source large language models.
problem Challenges in integrating GPT-based models with financial datasets.
method Instruction Tuning paradigm for open-source large language models adapted for financial contexts.
result Demonstrates the effectiveness and adaptability of FinGPT in financial tasks.
LLM Pro Finance Suite enhances financial NLP with instruction-tuned models.
problem Limited NLP capabilities for financial tasks in generalist models.
method Instruction-tuned large language models fine-tuned on financial data.
result Consistent improvement over state-of-the-art baselines in finance tasks.
Shapley values explain financial language models, aligning with domain knowledge.
problem Lack of explainability in financial applications of large language models.
method Shapley value analysis for financial textual data.
result Shapley values provide consistent explanations with financial reasoning.
Survey of financial LLMs in finance.
problem Limited research on Financial LLMs (FinLLMs).
method Chronological overview of PLMs, comparison of techniques, performance evaluations, and advanced tasks.
result Compilation of accessible datasets and benchmarks for AI research in finance.
LLMs improve financial sentiment analysis in finance.
problem Defining and measuring financial sentiment.
method Investigation of sentiment measurement methods and LLMs.
result LLMs enhance financial sentiment analysis.
Improved financial sentiment analysis using simple instruction tuning of LLMs.
problem Lack of accurate financial sentiment analysis by large language models.
method Instruction tuning of general-purpose LLMs with a small portion of financial sentiment data.
result Significant improvement in financial sentiment analysis, especially in complex scenarios.
The study enhances financial rule matching using NLP without datasets.
problem Performing semantic matching between financial rules and policies.
method Outperforming pre-trained models with NLP techniques using free resources.
result Improved semantic matching between financial rules and policies.
Paper introduces NumLLM for better financial text understanding with numeric variables.
problem Poor performance of existing financial large language models in numeric financial text.
method Constructed financial corpus, fine-tuned with LoRA modules, merged into foundation model.
result NumLLM achieves best performance on financial question-answering benchmark, especially with numeric questions.
Survey of LLMs in finance tasks, including adoption and performance.
problem Utilizing large language models in financial tasks.
method Review of current approaches, decision framework for adoption.
result Synthesizes state-of-the-art for LLMs in finance.
Paper fine-tunes LLMs for financial tasks using data fusion.
problem Improving LLMs for financial analysis tasks.
method Fine-tuned Llama3-8B and Mistral-7B using PEFT and LoRA, combined datasets for data fusion.
result Enhanced model performance across financial tasks.
This paper analyzes financial sentiment using LLMs and FinBERT, improving accuracy with few-shot examples.
problem Financial sentiment analysis for market evaluation.
method Application of large language models and FinBERT, with focus on prompt engineering and few-shot learning.
result GPT-4o achieves similar sentiment classification accuracy to FinBERT with fewer examples.
This study uses NLP to detect financial risks from documents.
problem Detecting and predicting financial risks in documents.
method NLP model design, text preprocessing, feature extraction, machine learning.
result NLP model effectively identifies and predicts financial risks.
FinGPT is an open-source financial LLM for democratizing financial data.
problem Accessing high-quality financial data for LLMs.
method Data-centric approach with automatic data curation and low-rank adaptation.
result FinGPT provides accessible and transparent financial data for FinLLMs.
German FinBERT improves financial text analysis performance.
problem Capturing domain-specific nuances in financial text.
method German FinBERT is a pre-trained German language model trained on a large corpus of financial data.
result German FinBERT outperforms standard models on finance-specific tasks.
Survey examines LLMs for financial data analysis.
problem Challenges in processing multifaceted financial data.
method Synthesizes recent LLM developments.
result Promises new avenues for financial data analysis.
Delphyne improves financial time series models with pre-trained language models.
problem Lack of financial data and negative transfer effect in existing time-series pre-trained models.
method Delphyne is a pre-trained model for financial time series that addresses the lack of financial data and negative transfer effect.
result Delphyne achieves competitive performance and superior performances on various financial tasks.
LLMs improve financial analysis by processing large data sets.
problem Traditional financial analysis methods struggle with large data volumes.
method Integrating LLMs for enhanced data processing and analysis.
result LLMs offer new capabilities for real-time financial decision-making.
FLARKO uses LLMs, KGs, and KTO to generate profitable, behaviorally aligned financial recommendations.
problem Financial recommendation systems often fail to account for behavioral and regulatory factors.
method FLARKO integrates LLMs, KGs, and KTO to generate profitable and behaviorally aligned recommendations.
result FLARKO consistently outperforms state-of-the-art recommendation baselines on behavioral alignment and joint profitability.
Study uses LLMs to simplify financial regulation interpretation.
problem Complex financial regulations are hard to interpret and implement.
method Developed prompts to guide LLMs in extracting key information from regulations.
result GPT-4 outperforms other LLMs in processing and executing regulatory requirements.
LLMs simulate financial markets, revealing consistent trading strategies and market dynamics.
problem Testing financial theories with AI trading agents.
method Simulated stock market with LLMs using a persistent order book and varied strategies.
result LLMs can simulate different trading strategies and market dynamics.
Survey examines LLMs in financial trading.
problem Using LLMs to outperform professional traders in finance.
method Comprehensive review of current research on LLMs in financial trading.
result LLMs can potentially outperform professional traders in backtesting.
LLMs produce volatile sentence-level sentiment classifications that affect financial decision-making.
problem Volatile outputs from LLMs impact financial text understanding tasks.
method Case study on US equity market investing via news sentiment analysis.
result Volatile LLM outputs lead to significant variations in portfolio construction and returns.
This study uses AI to analyze financial market coverage from YouTube videos.
problem Challenges in analyzing a large number of financial market videos.
method Used Whisper model to generate text from videos, applied natural language processing.
result Highlights dynamics of financial market coverage and identifies trending topics.
Study evaluates LLMs like ChatGPT and GPT-4 on financial analysis tasks.
problem Assessing financial reasoning capabilities of large language models.
method Mock CFA exam questions, Zero-Shot, Chain-of-Thought, Few-Shot scenarios.
result LLMs perform well on financial analysis tasks, but have limitations.
RiskLabs uses LLMs to predict financial risks from multimodal data.
problem Financial risk prediction using AI techniques.
method Integrates multimodal financial data (textual, vocal, time series, news) into LLMs for prediction.
result Empirical results show effectiveness in forecasting market volatility and variance.
UCFE benchmarks LLMs in financial tasks with human feedback.
problem Evaluating LLMs' financial task performance and user satisfaction.
method Hybrid approach combining human expert evaluations and dynamic interactions.
result Significant alignment between benchmark scores and human preferences (Pearson correlation coefficient of 0.78).
StockTime predicts stock prices more accurately using LLMs and time series data.
problem Challenges in integrating time series data and natural language for stock price prediction.
method StockTime is a specialized LLM architecture that integrates textual and time series data to predict stock prices.
result StockTime outperforms recent LLMs in predicting stock prices with more accuracy.
FinPT uses large pretrained models to predict financial risks.
problem Outdated algorithms and lack of open financial benchmarks.
method Profile Tuning on large pretrained foundation models.
result Demonstrated effectiveness on FinBench datasets.
FinRobot opens-source AI for financial tasks, breaking down complex problems.
problem Barriers to AI adoption in finance due to proprietary data and specialized knowledge.
method Develops open-source AI agent platform with four layers: Financial AI Agents, LLM Algorithms, LLMOps/DataOps, and Foundation Models.
result FinRobot democratizes AI access for financial analysis.
Study builds a Japanese financial-specific LLM through continual pre-training.
problem Lack of domain-specific Japanese financial LLMs.
method Continual pre-training on Japanese financial-focused datasets using a base Japanese LLM.
result Tuned model outperforms original model on Japanese financial benchmarks.
InvestLM is a financial domain LLM tuned on LLaMA-65B for investment advice.
problem Improving financial text understanding and advice generation for investment.
method Curated financial instruction dataset, LLaMA-65B, less-is-more-for-alignment approach.
result InvestLM provides comparable responses to state-of-the-art commercial models.
ChatGPT predicts stock market reactions from news headlines without financial training.
problem Predicting stock price movements using non-financial data.
method Used post-knowledge-cutoff headlines to train ChatGPT-4, which forecasts stock market reactions.
result ChatGPT-4 can predict stock market reactions with high accuracy, especially for small stocks and negative news.
FinBloom enhances LLMs for real-time financial queries.
problem Limited access to real-time financial data by LLMs.
method Developed a custom 7B parameter LLM, Financial Context Dataset, and a Financial Agent.
result Significantly improved LLMs' capability to handle dynamic financial tasks.
Large language models learn company embeddings from SEC filings.
problem Lack of a rigorous definition of company similarity.
method Pre-trained and finetuned large language models (LLMs) to learn embeddings from SEC filings.
result LLMs can reproduce GICS classifications and indicate similar financial performance.
Summarizes financial news for better investment decisions.
problem Information overload from financial news hinders timely investment decisions.
method Personalized Chain-of-Thought summarization framework integrating user-specified keywords.
result Personalized summaries highlight relevant market signals, improving investment narratives.
LLMs improve stock price forecasting from financial news and reports.
problem Predicting stock prices with high accuracy and robustness.
method Analyzing financial news, reports, and transcripts using LLMs.
result LLMs can improve stock price forecasting but face practical challenges.
FinHEAR combines LLMs with human expertise for better financial decision-making.
problem Challenges in financial decision-making for language models.
method Multi-agent framework with specialized LLMs for historical analysis, event interpretation, and expert retrieval.
result FinHEAR outperforms baselines in financial tasks with higher accuracy and risk-adjusted returns.
LLMs perform well in financial sentiment analysis without fine-tuning.
problem Challenges in financial terminology, emotions, and ambiguous expressions.
method In-context learning methods for financial document-sentiment pairs.
result LLMs can generalize in-context demonstrations to new financial documents.
Study introduces KorFinMTEB for Korean financial texts, revealing model limitations.
problem Limited evaluation benchmarks for low-resource domains, especially Korean.
method Developed KorFinMTEB, a tailored benchmark for Korean financial texts.
result Models perform better on translated benchmarks than on domain-specific ones.
LLMs outperform human analysts in predicting earnings direction.
problem Evaluating financial statements without narrative or industry-specific information.
method Trained GPT4 on standardized, anonymous financial statements and instructed to predict earnings direction.
result LLMs predict earnings directionally with accuracy comparable to narrowly trained ML models.
BreakGPT predicts asset price surges using LLMs.
problem Predicting sharp upward movements in volatile financial markets.
method Adapts LLMs for time series forecasting, combining LLM capabilities with Transformer models.
result BreakGPT effectively captures local and global temporal dependencies.
LLMs struggle with financial reasoning but can outperform the market with human oversight.
problem Financial reasoning failures in LLM-generated stock market predictions.
method Evaluated four LLMs using three prompting strategies and compared to human oversight.
result LLMs require human oversight to fully realize their potential in financial markets.
Study evaluates if LLMs have company-specific biases in financial sentiment analysis.
problem Evaluating if large language models exhibit company-specific biases in financial sentiment analysis.
method Comparing sentiment scores with and without company names, constructing economic models, and empirical analysis.
result LLMs show company-specific biases in sentiment analysis, impacting investor behavior and stock prices.
Transformer models improve financial sentiment measurement.
problem Capturing nuanced sentiment from financial news articles.
method Transformer-based language models for sentiment classification and aggregation.
result Transformer models outperform traditional dictionary-based methods in sentiment classification.