FinGPT democratizes financial data for LLMs, enabling innovation.
problem Limited financial text datasets and disparities between general and financial text data.
method Automates collection and curation of real-time financial data from diverse Internet sources, fine-tuning with RLSP and LoRA.
result Democratizes access to financial data for LLMs, enabling innovation.
System detects relevant financial news and predictions from unstructured text.
problem Manual extraction of relevant financial information from news is cumbersome and error-prone.
method Topic modeling with LDA, co-reference resolution, multi-paragraph segmentation, and temporal analysis.
result ROUGE-L values for relevant text and predictions/forecasts were 0.662 and 0.982, respectively.
EmTract extracts emotions from financial social media text.
problem Understanding investor emotions in financial markets.
method Annotated data, DistilBERT model, embedding space augmentation.
result EmTract outperforms existing emotion classifiers.
This paper analyzes text in financial disclosures to improve financial analysis.
problem Insufficient analysis of unstructured text in financial disclosures.
method Reviews and explores methods in computational linguistics and NLP.
result Highlights limitations of sentiment metrics and suggests future research areas.
This study reviews text-based stock market analysis methods.
problem Insufficient analysis of unstructured textual data in stock market predictions.
method Reviews existing literature, covers data types, representation techniques, and analysis methods.
result Identifies open problems and suggests future research directions.
LLMs compress financial texts, but distort decision-making.
problem LLMs compress financial texts, altering decision-making.
method Analyzed two diagnostic patterns: decontextualization and model dependency. Proposed Agentic Context Compression.
result LLM-compressed financial texts alter decision-making.
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.
While many models are purposed for detecting the occurrence of significant events in financial systems, the task of providing qualitative detail on the developments is not usually as well automated. We present a deep learning approach for detecting relevant discussion in text and extracting natural language description…
FinDKG uses LLMs to detect financial trends from news articles.
problem Detecting global financial trends from unstructured text data.
method Fine-tuned LLMs for generating DKGs, KGTransformer for analysis.
result KGTransformer outperforms existing thematic ETFs in financial thematic investing.
This paper improves retrieval for LLMs in financial document Q&A.
problem Suboptimal text chunk retrieval by RAG causes inaccuracies in LLM responses.
method Sophisticated chunking techniques, query expansion, metadata annotations, re-ranking algorithms, and embedding fine-tuning.
result Enhanced retrieval quality improves LLM performance and reliability.
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.
The study distills news sources to analyze stock reactions, finding sentiment has asymmetric and sector-specific effects.
problem Analyzing the influence of financial text sources on stock reactions.
method Mixed text sources from professional platforms, blogs, and message boards were distilled using different lexica to analyze sentiment variables.
result Sentiment has an asymmetric and sector-specific effect on stock reactions.
Model learns from multiple data sources for D2T and T2D tasks.
problem Limited performance due to single-source corpora.
method Variational auto-encoder with disentangled style and content variables.
result Model outperforms single-source counterpart on multiple datasets.
NewsNet-SDF uses deep learning to integrate financial news with financial data for better asset pricing.
problem Combining unstructured text with structured financial data for accurate asset pricing.
method Adversarial networks and pretrained language model embeddings.
result Substantially outperforms alternatives with a Sharpe ratio of 2.80.
Study integrates climate and text data to improve credit default prediction.
problem Improving credit risk assessment for mSEs with limited financial histories.
method Multimodal framework using LSTM, GRU, and transformer models.
result Integration of multiple data modalities improves credit default prediction.
Estimates watermarked content proportions in mixed-source texts.
problem Optimally estimating the proportion of watermarked content in texts with mixed sources.
method Casting the problem as estimating a proportion parameter in a mixture model based on pivotal statistics.
result Proposes efficient estimators for watermark proportion and shows their accuracy through evaluations.
Paper tackles source attribution for LLM-generated texts.
problem Identify the data provider of synthetic texts generated by LLMs.
method Watermarking to embed information about data providers in synthetic texts.
result Proposes and evaluates a source attribution framework that achieves effective source attribution.
New distress dictionary improves bankruptcy prediction from disclosure text.
problem Bankruptcy prediction from financial disclosures.
method Proposes a distress dictionary based on managers' sentences, quantifies linguistic features, and builds predictive models.
result Predictive models based on the distress dictionary outperform existing methods.
The study improves sentiment analysis of 10-K filings, revealing aggregation effects on accuracy and correlation with market outcomes.
problem Lack of sentiment analysis for 10-K filings, particularly for risk disclosures.
method Supervised lexicon-learning approach applied to 10-K filings and Item 1A risk-factor sections, trained against return and volatility labels at different levels of aggregation.
result Sentiment analysis of Item 1A sections performs better at the individual-firm level, while full-filing text is more accurate at sector and portfolio levels.
Mining financial text documents and understanding the sentiments of individual investors, institutions and markets is an important and challenging problem in the literature. Current approaches to mine sentiments from financial texts largely rely on domain specific dictionaries. However, dictionary based methods often f…
Anonymization reduces economic signal extraction from financial texts.
problem Reducing meaningful economic signals from financial texts due to anonymization.
method Analyzed the impact of anonymization on textual understanding and economic signal extraction.
result Information loss due to anonymization is severe and pervasive, outweighing its benefits in certain financial applications.
Decision analytics commonly focuses on the text mining of financial news sources in order to provide managerial decision support and to predict stock market movements. Existing predictive frameworks almost exclusively apply traditional machine learning methods, whereas recent research indicates that traditional machine…
System detects financial news temporality combining NLP and ML.
problem Separate context from predictions in financial news.
method Combines NLP and ML, extracts dominant tenses.
result High detection precision compared to baseline.
Anonymizing company names in financial news improves trading performance, contrary to initial expectations.
problem Look-ahead and distraction biases in sentiment analysis of financial news.
method Investigated trading strategies based on original and anonymized headlines, comparing performance.
result Anonymized headlines outperform original in-sample, suggesting distraction effect is stronger.
News is a pertinent source of information on financial risks and stress factors, which nevertheless is challenging to harness due to the sparse and unstructured nature of natural text. We propose an approach based on distributional semantics and deep learning with neural networks to model and link text to a scarce set …
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.
Model earnings call transcripts for better stock price prediction.
problem Predicting future stock price movements using earnings call transcripts.
method Deep learning framework with an attention mechanism to encode text data into vectors for predicting stock price movements.
result The proposed model outperforms traditional machine learning methods in stock price prediction.
FinReflectKG benchmarks financial QA by linking relevant context from a financial KG, improving model performance and efficiency.
problem Efficiently retrieving and navigating relevant financial information across diverse sources and years.
method A benchmark built on a temporally indexed financial KG, generating QA pairs via pattern-specific prompts and quality control, evaluating retrieval scenarios.
result KG-guided retrieval yields substantial gains in correctness and token utilization, improving model performance by 24%.
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.
BioFinBERT analyzes sentiment of biotech press releases and financial text around inflection points.
problem Analyzing sentiment of biotech press releases and financial text around inflection points.
method Finetuning BioBERT on financial datasets to create BioFinBERT for sentiment analysis.
result BioFinBERT accurately analyzes sentiment of biotech press releases and financial text around inflection points.
Enhanced regime shifts detection using unstructured text and financial data.
problem Detecting regime shifts in financial markets is challenging due to noisy and multicollinear data.
method Combines LLM reasoning on unstructured text and statistical validation on financial time series.
result Framework achieves F1 score of 0.82, outperforming pure data-driven methods.
Open-FinLLMs tackle financial tasks with multimodal capabilities.
problem Financial LLMs lack multimodal capabilities and real-world applicability.
method Developed Open-FinLLMs, an open-source multimodal financial LLM suite.
result Open-FinLLMs outperform advanced financial and general LLMs in diverse tasks.
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.
FinEAS models financial sentiment using BERT embeddings.
problem Financial sentiment analysis in markets.
method Supervised fine-tuning of BERT embeddings for financial texts.
result FinEAS outperforms vanilla BERT, LSTM, and FinBERT.
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.
AMI framework improves text generation by optimizing mutual information between source and target.
problem Previous MI approaches ignored the backward network, leading to loose variational bounds.
method AMI is a saddle point optimization framework that iteratively promotes and demotes generated instances.
result AMI significantly outperforms baselines on various text generation tasks.
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.
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.
Following the financial crisis of the late 2000s, policy makers have shown considerable interest in monitoring financial stability. Several central banks now publish indices of financial stress, which are essentially based upon market related data. In this paper, we examine the potential for improving the indices by de…
Study uses LLMs to categorize financial tweets, revealing useful sentiment signals.
problem Discovering meaningful sentiment signals from unstructured financial social media data.
method Leveraged LLMs to automatically label financial tweets with event categories and aligned with returns.
result Certain event labels consistently yield negative alpha, with statistically significant Sharpe ratios and information coefficients.
UniFinEval benchmarks financial models across text, images, and videos.
problem Challenges in evaluating financial multimodal models across text, images, and videos.
method Proposes UniFinEval, a unified multimodal benchmark for financial scenarios.
result Gemini-3-pro-preview achieves best performance but still lags behind experts.
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.
Explainable machine learning (ML) enables human learning from ML, human appeal of automated model decisions, regulatory compliance, and security audits of ML models. Explainable ML (i.e. explainable artificial intelligence or XAI) has been implemented in numerous open source and commercial packages and explainable ML i…
Compact models match or exceed GPT's performance in financial news sentiment analysis.
problem Improving financial sentiment analysis models without large computational costs.
method Fine-tuning non-generative, small-sized models (FinBERT, FinDRoBERTa) on a novel market score database.
result Fine-tuned models outperform GPT-3.5 and GPT-4 in zero-shot learning for financial news sentiment analysis.
BERTopic improves financial text analysis with FinTextSim's contextual embeddings.
problem Analyzing financial text data for insights and predictions.
method Integrates BERTopic with FinTextSim for topic modeling and clustering.
result BERTopic performs better with FinTextSim's embeddings, improving topic clarity and reducing misclassification.
We investigated the training of a shared model for both text-to-speech (TTS) and voice conversion (VC) tasks. We propose using an extended model architecture of Tacotron, that is a multi-source sequence-to-sequence model with a dual attention mechanism as the shared model for both the TTS and VC tasks. This model can a…
Graph auto-encoders improve financial clustering using news and stock data.
problem Improving clustering of financial entities using multiple data sources.
method Applying graph deep learning to a finance graph with news co-occurrence and stock price data.
result Dual data sources (news and stock price) improve clustering purity to 64% compared to 32% and 42% for single data sources.
StonkBERT predicts stock price movements using company text data.
problem Can language models predict medium-run stock price movements?
method Fine-tuning transformer-based language models (BERT) on company text data (news articles, blogs, annual reports) for stock price performance classification.
result StonkBERT shows substantial improvement in predictive accuracy compared to traditional models, with news articles providing the best results.