Framework integrates financial and annual report data for better corporate credit ratings.
problem Lack of insights from non-financial data in credit rating models.
method Uses FinBERT to extract features from annual reports and combines them with financial data.
result Improves credit rating accuracy by 8-12%.
FinBERT-XRC model assesses financial report risk, offering transparent explanations.
problem Assessing post-event return volatility risk in financial reports.
method Deep-learning model FinBERT-XRC with explainability at word, sentence, and corpus levels.
result FinBERT-XRC outperforms state-of-the-art models in predictive accuracy.
Corporate distress models typically only employ the numerical financial variables in the firms' annual reports. We develop a model that employs the unstructured textual data in the reports as well, namely the auditors' reports and managements' statements. Our model consists of a convolutional recurrent neural network w…
Deep learning models improve earnings prediction accuracy.
problem Reliable prediction of future earnings per share (EPS).
method Comparison of LSTM and TCN models on financial data.
result Both LSTM and TCN models outperform naive models and analysts.
Study clusters Kenyan medical insurance companies based on financial performance and reporting consistency.
problem Identifying financial health and reporting consistency in Kenyan medical insurance companies.
method Advanced clustering techniques (KMeans, DTW) on financial ratios and time series data.
result Four distinct clusters identified, each representing different financial performance and reporting consistency combinations.
New framework tackles deep financial reporting bottleneck by improving hallucination and coherence.
problem Statistical smoothing trap in LLMs limits deep financial reporting quality.
method DeepNews Framework integrates information foraging, schema-guided planning, and adversarial prompting.
result DeepNews system achieves 25% acceptance rate in blind test, significantly outperforming SOTA.
MountainLion uses LLMs to interpret financial data and generate investment strategies.
problem Challenges in integrating heterogeneous data for financial trading.
method Multi-modal LLM-based agents that process textual and visual data.
result Improves returns and investor confidence through interpretable investment framework.
We report evidence of a deep interplay between cross-correlations hierarchical properties and multifractality of New York Stock Exchange daily stock returns. The degree of multifractality displayed by different stocks is found to be positively correlated to their depth in the hierarchy of cross-correlations. We propose…
RAG-IT automates financial analysis using LLMs and specialized datasets.
problem Manual financial analysis is time-consuming and requires expertise.
method Retrieval-Augmented Instruction Tuning (RAG-IT) fine-tunes an LLM for financial tasks.
result RAG-IT improves financial report generation performance compared to commercial systems.
New method uses LLMs to extract financial insights from Q&A sections of reports.
problem Scalability and accuracy issues in extracting valuable insights from financial report Q&A sections.
method Combines retrieval-augmented generation technique with metadata.
result Empirically demonstrates superior performance of the proposed method.
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.
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).
Generative AI boosts analyst reports but increases forecast errors.
problem Improving financial analyst reports with AI.
method Natural experiment using FactSet's AI platform.
result AI-assisted reports are more comprehensive but lead to higher forecast errors.
Algorithms are increasingly common components of high-impact decision-making, and a growing body of literature on adversarial examples in laboratory settings indicates that standard machine learning models are not robust. This suggests that real-world systems are also susceptible to manipulation or misclassification, w…
Improved stock selection through predictive fundamentals and uncertainty estimates.
problem Selecting stocks based on future financial data to outperform traditional factor models.
method Train deep nets to forecast future fundamentals, incorporate uncertainty estimates, and adjust portfolios to manage risk.
result Simulated annualized return of 17.7% and Sharpe ratio of 0.84 for uncertainty-aware model, significantly higher than 14.0% and 0.52 for standard factor models.
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.
System constructs public competitor graph from financial reports.
problem Time-consuming and expert-laden manual extraction of corporate relationships.
method Financial report processing to generate reliable knowledge graph of corporate relationships.
result More than 83% of S\&P 500 companies' competition relationships retrieved.
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…
Paper proposes FinAR-Bench to evaluate LLMs in financial analysis tasks.
problem Inaccurate financial analysis by LLMs leading to investment and regulatory issues.
method Proposes FinAR-Bench, a benchmark dataset with three steps: key info extraction, financial indicator calculation, and logical reasoning.
result LLMs perform better in key info extraction and indicator calculation but struggle with logical reasoning.
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.
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.
TinyXRA assesses financial risks from 10-K reports using a lightweight transformer model.
problem Comprehensive risk assessment from financial reports, distinguishing between upside and downside risk.
method Lightweight transformer model with dynamic attention, incorporating skewness, kurtosis, and Sortino ratio.
result State-of-the-art predictive accuracy and transparent risk assessments.
Deep learning shows ETF imbalances are more informative than market imbalances.
problem Determining causality between ETF and market imbalances.
method Deep learning econometric methodology applied to stock and ETF transactions.
result ETF imbalance messages are more informative than market imbalance messages.
Advanced AI model predicts stock movements post earnings reports.
problem Inaccurate stock predictions from earnings reports.
method Fine-tuned LLMs with QLoRA compression, integrating financial and market data.
result Significantly improved predictive accuracy compared to benchmarks.
Study evaluates neural networks for corporate credit rating assessment.
problem Improving machine learning algorithms for credit assessment.
method Analysis of four neural network architectures (MLP, CNN, CNN2D, LSTM) on financial data from energy, financial, and healthcare sectors.
result LSTM architecture consistently outperforms others in predicting corporate credit ratings.
Study shows how financial report sentiment impacts bank profitability.
problem Understanding causal effects of financial report sentiment on bank profitability.
method Causal forest machine learning methodology, FinancialBERT sentiment scores, SHAP analysis, comprehensive dataset.
result Statistically significant causal associations between balance sheet and expense management variables and profitability.
Weak predictability of stock price movement 2 days after annual report disclosure.
problem Predicting stock price movement after annual report disclosure.
method Used various models including decision tree, logistic regression, random forest, neural network, prototypical networks; used financial indicators from EastMoney.
result Maximum accuracy and precision of stock price movement prediction is around 59.6% and 0.56 respectively, with random forest performing best.
The financial rogue waves are reported analytically in the nonlinear option pricing model due to Ivancevic, which is nonlinear wave alternative of the Black-Scholes model. These solutions may be used to describe the possible physical mechanisms for rogue wave phenomenon in financial markets and related fields.
New attacks inflate earnings while reducing fraud scores, potentially millions at stake.
problem Manipulating financial reports to hide distress and gain.
method Maximum Violated Multi-Objective (MVMO) attacks that adapt search direction.
result Inflation of earnings by 100-200% while reducing fraud scores by 15% in 50% of cases.
This study analyzes financial equity research reports to identify frequently asked questions and automates 80% of them.
problem Insufficient empirical analysis of questions answered in financial equity research reports.
method Analyzed 72 financial equity research reports, classifying sentences into 169 unique question archetypes. Used public corporate reports to classify questions' potential for automation.
result Approximately 80% of financial equity research reports can be automated, with 78.7% of questions automatable.
On a periodic basis, publicly traded companies are required to report fundamentals: financial data such as revenue, operating income, debt, among others. These data points provide some insight into the financial health of a company. Academic research has identified some factors, i.e. computed features of the reported d…
EDINET-Bench evaluates LLMs on complex financial tasks using Japanese financial statements.
problem Challenges in evaluating LLMs on financial tasks due to specialized expertise and scarce benchmarks.
method Developed EDINET-Bench, an open-source Japanese financial benchmark for LLMs on tasks like fraud detection and earnings forecasting.
result State-of-the-art LLMs perform only marginally better than logistic regression in financial tasks, highlighting the need for more realistic benchmarks.
Paper uses LLMs to analyze annual reports for stock investment, improving efficiency.
problem Manual analysis of annual reports is time-consuming and requires expertise.
method Leverages Large Language Models to extract and analyze annual reports.
result Machine Learning model trained on LLM outputs outperforms S&P500 returns.
Study evaluates five LLMs for financial report analysis, revealing performance differences and variability.
problem Lack of understanding in reliability, consistency, and transparency of LLMs in financial analysis.
method Human evaluation, automated similarity metrics, and behavioral diagnostics applied to five transformer-based LLMs over U.S. 10-K filings.
result No single LLM consistently dominates across all evaluation perspectives, highlighting variability and need for interpretability.
Study evaluates financial misstatement detection methods, highlighting evaluation process impact.
problem Detecting financial reports with high misstatement risk.
method Proposes a new, realistic evaluation framework focusing on misstatement rarity, time dimension, and detection latency.
result Evaluation process significantly impacts system performance, revealing model and feature type effectiveness.
This paper uses RL for better financial trading.
problem Improving financial trading algorithms.
method Deep Q Learning applied to quantitative trading.
result RL can outperform traditional trading algorithms.
Myanmar is languishing at the bottom of key international indexes. United Nations considers the country as a structurally weak and vulnerable economy. Yet, from 2011 when Myanmar ended decades of military rule and isolationism and transited towards democracy, its breakneck development has led to many considering the co…
We examine the out-of-equilibrium phase reported by Plerou {\it et. al.} in Nature, {\bf 421}, 130 (2003) using the data of the New York stock market (NYSE) between the years 2001 --2002. We find that the observed two phase phenomenon is an artifact of the definition of the control parameter coupled with the nature of …
Financial statements contain quantitative information and manager's subjective evaluation of firm's financial status. Using information released in U.S. 10-K filings. Both qualitative and quantitative appraisals are crucial for quality financial decisions. To extract such opinioned statements from the reports, we built…
Among econophysics investigations, studies of religious groups have been of interest. On one hand, the present paper concerns the Antoinist community financial reports, - a community which appeared at the end of the 19-th century in Belgium. Several growth-decay regimes have been previously found over different time sp…
DRL automates stock market trading with a 2.68 Sharpe Ratio.
problem Automating profitable trades in the stock market.
method Formulated as a POMDP, solved with TD3 algorithm.
result 2.68 Sharpe Ratio on unseen data.
Study uses LLM to extract and compare segment disclosures from financial filings.
problem Challenges in completeness and comparability of segment disclosures in financial reports.
method Developed a large language model framework to extract and preserve segment information from Form 10-K filings.
result The LLM accurately extracts segment-level information and addresses cross-period knowledge questions.
We discuss a common suspicion about reported financial data, in 10 industrial sectors of the 6 so called "main developing countries" over the time interval [2000-2014]. These data are examined through Benford's law first significant digit and through distribution distances tests. It is shown that several visually anoma…
Research shows eco-innovation boosts earnings management, especially in constrained firms.
problem The impact of eco-innovation on earnings management in firms with financial constraints.
method Multi-method approach including entropy balancing, PSM, and Heckman Test correction.
result Eco-innovation positively correlates with earnings management, especially in firms facing financial constraints.
Enhanced volatility model using LSTM and realized volatility.
problem Volatility modeling in financial markets.
method Combining deep learning (LSTM) and realized volatility measures in a Bayesian framework.
result Superior predictive performance compared to benchmark models.
On 2 November 2009, the Financial Bubble Experiment was launched within the Financial Crisis Observatory (FCO) at ETH Zurich (\url{http://www.er.ethz.ch/fco/}). In that initial report, we diagnosed and announced three bubbles on three different assets. In this latest release of 23 December 2009 in this ongoing experime…
New tool uses computer vision to assess FOMC press conference complexity and its impact on equity returns.
problem Understanding the impact of FOMC press conferences on financial markets.
method Developed a measure of discussion complexity using video images and deep learning algorithms.
result Complex discussions during press conferences are associated with higher equity returns and lower volatility.
Study tests UK FTSE-listed companies' financial data for Benford's Law conformity.
problem Ensuring the fairness of public revenue collection and reducing tax avoidance risks.
method Utilised pre-tax income and total assets data from 567 FTSE companies, tested for Benford's Laws conformity using χ2 and MAD tests. result MAD test rejects Benford's Laws conformity, suggesting potential issues with reported financial data.