K-means algorithm improves financial market risk prediction accuracy.
problem High error rate and low precision in financial market risk prediction.
method Applied K-means algorithm in machine learning to financial market risk forecasting.
result Achieved a 94.61% accuracy rate in financial market risk prediction.
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.
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.
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problem Outdated algorithms and lack of open financial benchmarks.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.
DeRisk improves credit risk prediction using deep learning.
problem Challenges in training deep neural networks with real-world financial data.
method DeRisk, an effective deep learning framework for credit risk prediction.
result DeRisk outperforms statistical learning methods in credit risk prediction.
A new explainable CBR system predicts financial risks with interpretability and good performance.
problem Predicting financial risks with interpretability and good performance.
method A novel explainable case-based reasoning (CBR) approach.
result The CBR system provides a good prediction performance and interpretability.
Paper proposes a CNN model for improved multi-asset portfolio risk prediction.
problem Challenges in risk management of multi-asset portfolios due to limited correlation capture.
method Uses CNN and image processing to convert financial data into images for enhanced feature extraction.
result CNN model significantly outperforms traditional methods in risk prediction accuracy.
Causal-NECO VaR improves financial risk assessment under market turbulence.
problem Inaccurate risk assessment in volatile markets.
method Causal Network Contagion Value at Risk (Causal-NECO VaR) using causal network framework.
result Robust and invariant predictive power in unstable financial environments.
GAN improves financial risk prediction by generating synthetic minority events.
problem Data imbalance in financial market supervision.
method Generative Adversarial Networks (GAN) to generate synthetic data.
result GAN-generated synthetic data significantly improves prediction accuracy.
Model predicts risk-adjusted returns across various financial markets.
problem Stationary models fail in predicting risk-adjusted returns due to market regime changes.
method Asset-independent regime-switching model using hidden Markov models.
result Accurately detects bull, bear, and high volatility periods for improved risk-adjusted returns.
Model predicts global financial market risks and asset allocation.
problem Predicting downside risk and market regime shifts.
method Dynamic regime switching model based on GARCH-DCC-Copula.
result Significantly improves risk and alpha-based asset allocation strategies.
Model predicts default risk based on company's financial forecasts and credit conditions.
problem Estimating the risk of a company defaulting on its financial obligations.
method Developed an equilibrium model linking interest rates to corporate performance and credit supply.
result Estimates idiosyncratic default risk and provides forward-looking probability of default (PD).
This paper surveys enterprise financial risk analysis from Big Data and LLMs perspectives.
problem Predicting future financial risk of enterprises.
method Systematic literature review of enterprise financial risk analysis approaches from Big Data and LLMs perspectives.
result Offers a holistic synthesis of research methods and key insights.
New method for interpreting financial model risks.
problem Fairly allocating risk in financial models.
method Extending Shapley value framework for axiomatic risk attribution.
result Risk can be well allocated in financial models.
Study combines quantum and classical deep learning for better credit risk assessment.
problem Enhancing accuracy and efficiency in credit risk evaluation.
method Hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis.
result Proposed framework enhances predictive models for different loan categories.
Paper uses Time Series Transformer for bank stability prediction.
problem Predicting bank stability using complex financial data.
method Time Series Transformer model with self-attention mechanism.
result Time Series Transformer model outperforms other models in MSE and MAE.
Optimizes forecast distributions for financial risk management.
problem Improving risk management through better forecast distributions.
method Optimizes forecast distributions using scoring rules relevant to financial risk management.
result Tail-focused predictive distributions yield better outcomes in hedging strategies involving VIX futures.
Unified model predicts stock and systemic risks from diverse financial data.
problem Isolating financial tasks leads to missed cross-scale dependencies.
method Shared Transformer backbone with modular task heads for cross-modal attention and multi-task optimization.
result Uni-FinLLM significantly outperforms baselines in stock forecasting, credit-risk assessment, and systemic-risk detection.
Enhances Transformers for better risk assessment in finance.
problem Transformer models lack sensitivity to extreme financial losses.
method Integrates Loss-at-Risk function with Value at Risk (VaR) and Conditional Value at Risk (CVaR).
result Improves risk prediction and management in financial datasets.
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.
MassMutual uses neural network embeddings from financial news to predict downgrade risk.
problem Predicting downgrade risk in financial institutions using alternative data sources.
method Proposes a predictive downgrade model using neural network embeddings of financial news.
result Improves performance of benchmark model by more than 5 percent in terms of AUC and recall rate.
Model predicts Mozambique bank failures, aiding risk management.
problem Lack of bankruptcy prediction model in Mozambique banking sector.
method Linear Discriminant Analysis method, using financial indicators.
result Model accurately predicted 84% of bank failures 1 year before Central Bank intervention.
This paper proposes a deep learning model combining CNN and Transformer for improved credit default prediction.
problem Traditional machine learning models struggle with complex financial data and risk patterns.
method Combines CNN for local feature extraction and Transformer for global dependency modeling.
result The CNN+Transformer model outperforms traditional models in accuracy, AUC, and KS value.
This paper proposes a new framework for financial risk that considers predictability rather than volatility.
problem Volatility's limitations as a risk measure, especially in complex strategies and non-stationary markets.
method Developed a new paradigm based on stochastic processes and the Multifractional Process with Random Exponent (MPRE) framework.
result A formal definition of 'fair volatility' that aligns with market efficiency and provides a measure of market inefficiency.
This paper uses graph neural networks to predict SME default risk using transaction and ownership networks.
problem Predicting credit risk for SMEs facing limited financial histories and collateral constraints.
method Graph Neural Networks applied to multilayer network data of SME transactions and ownership.
result Combining network data with traditional data improves credit scoring and models contagion risk.
AlphaSharpe uses LLMs to improve financial metrics robustness and predictive power.
problem Traditional financial metrics struggle with robustness and generalization in volatile markets.
method Iterative optimization of financial metrics using LLMs, including crossover, mutation, and evaluation.
result AlphaSharpe discovers enhanced risk-return metrics with 3x predictive power and 2x portfolio performance.
An artificial agent for financial risk and returns' prediction is built with a modular cognitive system comprised of interconnected recurrent neural networks, such that the agent learns to predict the financial returns, and learns to predict the squared deviation around these predicted returns. These two expectations a…
New model predicts financial tail events using RIA-EVT-Copula.
problem Predicting financial tail events for risk management.
method RIA-EVT-Copula framework combining POT, RIA, and copulas.
result Improved accuracy in predicting financial extremes.
This paper develops a machine learning model to assess credit risk in UAE commercial banks.
problem Lack of precision in conventional credit rating tools for accurate credit risk prediction.
method Constructs a credit risk assessment model using Linear Discriminant Analysis.
result Demonstrates improved accuracy in predicting good and bad creditors compared to conventional methods.
Study combines intra-risk and contagion risk for SME bankruptcy prediction.
problem Predicting bankruptcy risk of SMEs considering both intra-risk and contagion risk.
method Proposes a novel model using Graph Neural Networks to combine intra-risk and contagion risk.
result Model outperforms state-of-the-art methods in bankruptcy prediction.
New method recovers BSDE from financial data without ergodicity.
problem Discovering probabilistic laws from financial data.
method Stochastic SINDy method under risk-neutral measure.
result Recovery of BSDE from limited financial data.
Study introduces new financial ratios for better predicting company performance.
problem Lack of progress in predicting company performance and assessing financial risks.
method Developed new financial and macroeconomic ratios, supervised learning models, and Bayesian models.
result New proposed variables improve model accuracy and FNN performs best across multiple tasks.
DSVM model predicts financial market volatility with better accuracy.
problem Predicting financial market volatility accurately.
method Deep latent variable models with variational inference.
result DSVM outperforms GARCH models in predicting volatility.
Graph machine learning and Super-App data improve credit risk prediction for financial inclusion.
problem Improving credit risk prediction for financial inclusion.
method Two graph-based experiments using centrality, behavior, and transactionality features.
result Graph features enhance credit risk models, leading to more inclusive financial systems.
Paper proposes MMW distribution for better financial risk modeling.
problem Modeling non-normal stock returns for risk estimation.
method Mixture of mirrored Weibull (MMW) distribution for flexible risk modeling.
result MMW model outperforms Gaussian and t-mixture models in VaR estimation.
Deep learning improves credit risk assessment without new data.
problem Improving credit risk assessment in banking without new data.
method Sequential deep learning using temporal convolutional networks.
result Sequential deep learning outperformed tree-based models in credit risk assessment.
Deep learning improves asset pricing and risk premium measurement.
problem Improving asset pricing and risk premium measurement using deep learning.
method Investigates various deep learning methods for asset pricing, especially for risk premia measurement.
result RNNs with memory mechanism and attention have the best performance in terms of predictivity.
This research predicts cryptocurrency price volatility using deep learning models.
problem Predicting the volatility of cryptocurrency prices to mitigate investment risk.
method Used CNN, LSTM, BiLSTM, and GRU models to predict the risk factor of 20 cryptocurrency parameters.
result Developed a new model with RMSE of 0.0089, significantly outperforming existing models.
Recent financial disasters have emphasised the need to accurately predict extreme financial losses and their consequences for the institutions belonging to a given financial market. The ability of econometric models to predict extreme events strongly relies on their flexibility to account for the highly nonlinear and a…
Study uses neural networks to predict credit risk in banks.
problem Credit risk management in commercial banks.
method Backpropagation neural network model.
result Neural network model improves credit risk prediction.
Advanced ML/DL models predict stock prices using technical analysis.
problem Accurately predicting stock prices in a complex market.
method Use of deep learning models for stock price prediction.
result Deep learning models can predict stock prices with high accuracy.
Federated learning predicts financial distress across U.S. states without centralizing data.
problem Predicting financial distress across U.S. states using sensitive data without centralization.
method Cross-silo federated learning, interpretable AI techniques, machine learning model for categorical data.
result Identifies both global and state-specific predictors of financial hardship.
RAGIC predicts stock intervals with risk considerations, improving prediction accuracy and coverage.
problem Limited success in predicting stock market outcomes due to stochastic nature and risk oversight.
method RAGIC uses a GAN with a risk module and temporal module to generate risk-sensitive stock intervals.
result RAGIC achieves a consistent 95% coverage with narrow interval widths, balancing accuracy and risk.
Bayesian GPR model predicts extreme stock market losses.
problem Forecasting rare but impactful extreme negative returns in equity markets.
method Developed a Bayesian Generalised Pareto Regression model linking scale parameter to market volatility.
result The Cauchy prior provides the best balance between predictive accuracy and model simplicity.
New method uses G-expectation for financial risk measurement.
problem Measuring uncertainty in financial time series.
method Introducing G-normal distribution, applying max-mean estimators, and using autoregressive models.
result G-VaR model outperforms other VaR predictors in risk prediction.
Paper proposes embedding models to capture semantic similarities of categorical attributes in financial bonds.
problem Challenges in finding similar bonds due to overshadowing of categorical non-financial attributes.
method Embedding models to capture semantic similarities of categorical attributes.
result Improves risk modeling and curve construction via sparse-issuer augmentation.
Bayesian and simulation methods predict credit default probabilities.
problem Assessing credit risk in large customer portfolios.
method Two-phase approach: Bayesian estimation followed by Monte Carlo simulations.
result Estimation of true default rates through simulations.
GAS models have been recently proposed in time-series econometrics as valuable tools for signal extraction and prediction. This paper details how financial risk managers can use GAS models for Value-at-Risk (VaR) prediction using the novel GAS package for R. Details and code snippets for prediction, comparison and back…