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.
SusGen-GPT improves financial NLP and ESG report generation.
problem Lack of advanced NLP tools for finance and ESG domains.
method Developed SusGen-30K dataset and SusGen-GPT models.
result Achieved state-of-the-art performance in financial NLP tasks.
Framework quantifies financial NLP robustness under regime shifts.
problem Semantic and causal drift in financial news narratives.
method Four metrics: FCAS, PCS, TSV, NLICS.
result Transformer models are more affected by semantic drift.
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.
BERTopic enhances stock market prediction by analyzing sentiment in topic models.
problem Improving stock price prediction accuracy using sentiment analysis.
method Employed BERTopic for sentiment analysis of stock market comments integrated with deep learning models.
result Enhanced model performance through topic sentiment integration.
The paper improves cryptocurrency price forecasting using deep learning and NLP on financial, blockchain, and social media data.
problem Improving cryptocurrency price forecasting accuracy and profitability.
method Integrates financial, blockchain, and social media data; applies BART MNLI model for sentiment analysis; uses deep learning NLP models; compares with traditional methods; uses local extrema as predictive targets.
result Significantly improves forecasting accuracy and profitability of cryptocurrency price predictions.
Improved financial sentiment analysis using LLMs with retrieval augmentation.
problem Limited performance of traditional NLP models in financial sentiment analysis.
method Retrieval-augmented Large Language Models (LLMs) with instruction tuning.
result Achieved 15% to 48% performance gain in accuracy and F1 score.
Study analyzes deep learning models for financial sentiment in earnings calls.
problem Leveraging NLP for sentiment analysis in financial transcripts.
method Comparative analysis of BERT, FinBERT, and ULMFiT models.
result Models' strengths and limitations in financial sentiment analysis.
ST-GAN predicts stock trends using financial news and data.
problem Predicting financial trends in stock markets.
method ST-GAN combines NLP and technical indicators using GAN technology.
result Significant improvement over existing models in stock price forecasting.
Study improves cryptocurrency price prediction using unlabeled text data.
problem Predicting cryptocurrency returns from unlabelled text data.
method Introduced weak learning approach to finetune BERT on unlabeled text data.
result Finetuning pretrained NLP models with weak labels enhances forecast accuracy.
The Hype Index measures media attention to equities using NLP.
problem Quantifying media attention to equities for volatility analysis.
method Constructs News Count-Based and Capitalization Adjusted Hype Indices using NLP.
result The Hype Index family provides valuable tools for stock volatility analysis.
Deep learning models predict financial market trends from social media leaders.
problem Predicting financial market trends using social media data.
method Deep learning models trained on NLP analysis of leaders' Twitter handles.
result Substantial improvement in financial market prediction accuracy.
Modeling investor behavior from financial advisor notes using NLP.
problem Identifying behavioral coaching opportunities for financial advisors.
method Topic modeling and supervised classification model.
result Predicting investor needs during adverse market conditions.
Algorithm improves SLR efficiency in financial narratives.
problem Fragmented understanding of financial narratives.
method NLP, clustering, interpretability tools.
result Unified narrative modeling approaches needed.
System segments Form 10-K documents into Item sections for financial analysis.
problem Segmenting Form 10-K documents into Item sections for efficient financial analysis.
method Developed an automatic Form 10-K Itemization system using NLP techniques.
result System achieves a retrieval rate of 93% for segmenting Item sections.
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.
Deep models for financial transactions are vulnerable to adversarial attacks, especially when adding transaction tokens.
problem Vulnerability of deep models to adversarial attacks on financial transaction records.
method Examine adversarial attacks and defenses on transaction records data, considering black-box attacks and adding transaction tokens.
result A few generated transactions can fool a deep-learning model, highlighting the need for robustness improvements.
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.
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.
Detects systematic anomalies in consumer complaints using NLP.
problem Detecting small, frequent anomalies in consumer complaints.
method NLP conversion of narratives, followed by anomaly detection algorithm.
result Demonstrates effectiveness of NLP for detecting systematic anomalies.
Solves the challenge of retrieving item-specific financial information from Form 10-Q filings.
problem Retrieving item-specific information from Form 10-Q filings with varying formats and machine-readable hierarchy.
method Complements a rule-based algorithm with a Convolutional Neural Network (CNN) image classifier to itemize 10-Q files.
result Demonstrates a generalized pipeline for rapid data retrieval from a large volume of textual data.
FinAI-BERT classifies AI disclosures in financial reports with high accuracy.
problem Systematic detection of AI-related disclosures in financial reports.
method Fine-tuned transformer-based model on a curated dataset.
result Achieved near-perfect classification performance (99.37% accuracy).
Study shows integrating acoustic features in financial forecasting models can degrade performance.
problem Predicting stock market volatility from corporate earnings calls using speech features.
method Empirical investigation of acoustic feature extraction in teleconference environments using a two-stream late-fusion architecture.
result Integrating acoustic features via late fusion significantly degraded performance, reducing recall to 47.08%.
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.
DeepTrust uses NLP to quickly identify and verify financial anomalies on Twitter.
problem Unreliable information in financial markets leading to unexpected price changes.
method Machine learning for anomaly detection, NLP for information retrieval and reliability assessment.
result DeepTrust outperforms baseline classifiers in identifying financial anomalies.
Paper proposes a hybrid model for financial time series prediction using sentiment analysis.
problem Challenges in forecasting in non-stationary, complex environments with heterogeneous data.
method Hybrid model combining GANs with NLP-based sentiment analysis.
result Hybrid model enhances robustness in non-stationary environments.
System detects financial forecasts in tweets, achieving high precision.
problem Detecting financial forecasts in social media messages.
method Natural Language Processing and Machine Learning techniques for real-time analysis.
result Achieves over 90% precision for financial forecasts.
The paper analyzes word embeddings and their failure to distinguish polarized terms.
problem Word embeddings fail to correctly distinguish terms with opposite polarities.
method Mathematical analysis of word2vec model, synthetic corpus generation, empirical assessment.
result Word embeddings treat antonyms as frequentist synonyms, leading to mixed polarity terms.
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.
The paper analyzes how news sentiment of companies can affect market movements.
problem Understanding how news sentiment impacts market performance and volatility.
method Applied NLP techniques to analyze news sentiment of 87 companies over 7 years.
result Strong media sentiment towards one company can indicate significant changes in sentiment towards related companies.
Study categorizes mutual funds using natural language processing from unstructured data.
problem Categorizing mutual funds using unstructured data for financial analysis.
method Used natural language processing models to classify mutual funds from their investment strategy descriptions.
result High accuracy in categorizing mutual funds using NLP from unstructured data.
BondBERT improves sentiment analysis for bond markets.
problem Misleading sentiment models for bond markets due to domain-specific dynamics.
method Fine-tuned transformer model on bond-specific news.
result BondBERT produces positive correlations with bond returns and higher forecasting accuracy.
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.
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.
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.
StockEmotions dataset for financial sentiment and emotion analysis.
problem Limited resources for financial sentiment analysis.
method Collects 10,000 English comments from StockTwits, categorizes emotions into 12 classes.
result DistilBERT outperforms other models in sentiment classification, and Temporal Attention LSTM model achieves best performance in multivariate time series forecasting.
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.
A competition increases financial transaction models' robustness against attacks.
problem Neural networks used by banks are vulnerable to adversarial attacks in financial transaction data.
method A novel competition where participants propose attacks and defenses, simulating real-world conditions.
result Participants' strategies and outcomes provide insights into improving financial transaction models' robustness.
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.
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.
Quantitative model predicts Sri Lankan stock market using NLP, clustering, and time-series forecasting.
problem Predicting economic regimes and market signals in Sri Lankan stock indices.
method Integrates NLP, clustering, and time-series forecasting; uses FinBERT for sentiment analysis, UMAP/HDBSCAN for clustering, and GRU/LSTM for forecasting.
result GRU model achieves 80.1% R-squared for daily closing price forecasts.
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.
News embeddings improve volatility forecasts.
problem Improving volatility forecasting accuracy.
method Transformed news text into embeddings, evaluated standalone and combined with benchmarks.
result News contains useful predictive information, especially for stock-related content.
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.
QUACKIE creates a new benchmark for NLP interpretability.
problem Evaluating NLP interpretability methods is challenging due to biased ground truths.
method Formulated a custom classification task from question-answering datasets, generating unbiased ground truths.
result Demonstrated the effectiveness of current interpretability methods on the new benchmark.
System detects financial opportunities in tweets with high precision.
problem Detecting valuable financial insights in micro-blogging data.
method Stacked Machine Learning classification system with NLP features.
result System achieves precision up to 83% in detecting financial opportunities.
Study predicts market bubbles using machine learning and financial news sentiment.
problem Predicting market bubbles in the S&P 500 index.
method Three-step approach combining financial news sentiment and macroeconomic indicators.
result Proposed three-step ensemble approach significantly improves bubble prediction accuracy.
ChatGPT enhances GNN for stock movement prediction.
problem Predicting stock movements using textual data.
method Integrates ChatGPT's graph inference into GNN for stock movement forecasting.
result Model outperforms state-of-the-art benchmarks in stock movement forecasting.