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.
ReGEN-TAD detects anomalies in financial time series with interpretable models.
problem Detecting anomalies in complex financial time series with high-dimensional data.
method Integrates machine learning with econometric diagnostics in a refined convolutional--transformer architecture.
result Unified anomaly score without labeled data, robust to structured deviations.
TimeTrail detects financial fraud patterns through temporal correlation analysis.
problem Detecting and explaining complex financial fraud patterns.
method Temporal data enrichment, dynamic correlation analysis, interpretable pattern visualization.
result TimeTrail outperforms conventional methods in accuracy and interpretability.
This paper tackles interpretability of LLMs in finance.
problem Complexity and lack of transparency of LLMs in finance.
method Mechanistic interpretability to understand LLM behavior.
result Demonstrates practical relevance of mechanistic interpretability in financial use cases.
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.
Ploutos predicts stock movements with financial LLM, improving interpretability.
problem Combining textual and numerical data for stock prediction and lack of interpretability.
method Proposes Ploutos framework combining PloutosGen and PloutosGPT for interpretable predictions.
result Framework outperforms state-of-the-art methods in prediction accuracy and interpretability.
Study financial market graphs with Laplacian constraints.
problem Learning undirected graphs in financial markets.
method Proposes algorithms to estimate graphs accounting for financial data properties.
result Guidelines for estimating graphs in financial markets.
The study improves Bitcoin price prediction using hybrid machine learning and enhances interpretability.
problem Improving Bitcoin price prediction accuracy and interpretability.
method Hybrid machine learning algorithms (OLS, LASSO, LSTM, decision tree regressors) and preprocessing techniques for time-series data.
result Linear regression achieves the best performance in predicting Bitcoin prices.
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.
LDA-XGB1 balances fairness and accuracy in lending models.
problem Fair lending practices and model interpretability in binary classification.
method Biobjective optimization using binning and information value, leveraging XGBoost.
result Achieves effective balance between accuracy, fairness, and interpretability.
Algorithm improves SLR efficiency in financial narratives.
problem Fragmented understanding of financial narratives.
method NLP, clustering, interpretability tools.
result Unified narrative modeling approaches needed.
Interpretable AI model boosts investment confidence and profitability.
problem Challenges in financial forecasting and interpretability in decision-making models.
method SHAP-based explainability technique for interpretable AI models.
result Notable enhancement in investor's portfolio value.
Proposes a novel evolutionary model for stock price prediction.
problem Challenges in financial markets, such as adaptability and interpretability.
method Trader-Company method, which aggregates suggestions from multiple weak learners (Traders) to predict stock returns.
result Shows the effectiveness of the method through experiments on real market data.
CMTF improves financial market forecasting by fusing multiple data types.
problem Lack of effective integration of diverse financial data sources.
method Transformer-based deep learning framework with tensor interpretation and auto-training.
result CMTF outperforms classical and deep learning models in price direction classification.
In an informal way, a number of thoughts on the financial crisis 2008 are presented from a physicist's viewpoint, considering the problem as a nonergodicity transition of a spin-glass type of system. Some tentative suggestions concerning the way out of the crisis are also discussed, concerning Keynesian "deficit spendi…
The paper explores AI in finance, focusing on XAI's role in enhancing interpretability and trust.
problem The need for AI in finance and the importance of XAI for better decision-making.
method Tracing AI's evolution in finance, highlighting XAI's role, and demonstrating through simulations.
result XAI enhances trust in AI systems, leading to more responsible decision-making.
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.
Proposes PRMs for interpreting financial risk concept drift.
problem Concept drift in high-stakes predictions like credit risk.
method Probabilistic Rule Models (PRMs) using Markov Logic Networks.
result Interpretable rules explain borrower risk changes.
EMDLOT predicts bond defaults better than traditional methods.
problem Lack of interpretability and irregular temporal dependencies in financial data.
method Integrates time-series and textual data, uses Time-Aware LSTM, soft clustering, and multi-level attention.
result EMDLOT outperforms traditional and deep learning benchmarks in recall, F1-score, and mAP.
Interprets deep learning models for rough volatility pricing.
problem Lack of interpretability in deep learning models for financial models.
method Detailed analysis of neural network learned inverse map between rough volatility model parameters and implied volatilities.
result Provides insights into neural network outputs for rough volatility models.
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 uses XAI and transformers for stock price prediction of top 100 BIST banks.
problem Enhancing interpretability and accuracy of stock price predictions.
method Combines transformer-based time series models with XAI techniques.
result Transformer models show strong predictive capabilities and provide feature transparency.
GRTR framework uses graph regularization to improve financial forecasting.
problem High computational costs and economic domain knowledge loss in tensor models.
method Graph-Regularized Tensor Regression (GRTR) framework incorporating economic domain knowledge.
result Improved performance in multi-way financial forecasting with reduced computational costs.
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.
This review examines deep learning in financial fraud detection over 5 years.
problem Improving deep learning techniques for financial fraud detection.
method Systematic literature review of 57 studies using performance metrics.
result Deep learning models enhance fraud detection across various financial domains.
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice and manager interpretation.
problem Lack of consistent and specific financial advisor expertise in personalized investment advice.
method Grounds financial advisor personas in fund disclosures, holdings transitions, market context, and manager commentary through an agentic actor--scorer--patcher loop.
result Personas better recover portfolio decisions and grounded manager interpretation than generic baselines.
TRACER improves interpretability in high stakes analytics.
problem Interpreting complex models in high stakes applications.
method TRACER framework with TITV model for healthcare analytics.
result TRACER facilitates both accurate and interpretable analytics.
Study shows adding correlated features doesn't improve LSTM model interpretability for oil stocks.
problem Improving interpretability of LSTM models for predicting oil company stocks.
method Designed and trained Standard LSTM networks using various correlated datasets.
result Adding correlated features does not enhance LSTM model interpretability.
Developed an explainable DRL model for financial portfolio management.
problem Inability of DRL agents to provide interpretable financial investment policies.
method Integrating PPO with feature importance techniques (SHAP, LIME) to enhance transparency.
result Ability to interpret DRL agent actions in prediction time.
Method detects and visualizes changes in financial markets' asset relationships.
problem Detecting and explaining changes in financial markets' asset relationships.
method Construct co-occurrence networks, calculate Graph-Based Entropy, apply Differential Network.
result Visualization of changes in financial markets with high interpretability.
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).
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.
Trading-R1 uses LLMs for financial trading, improving risk-adjusted returns.
problem Lack of interpretability and trust in AI for finance.
method Supervised fine-tuning and reinforcement learning with a curriculum.
result Improved risk-adjusted returns and lower drawdowns compared to other models.
The thesis models financial returns using mixtures of generalized normal distributions.
problem Estimation issues in financial return analysis.
method Mixtures of generalized normal distributions (MGND), ECM/GEM algorithms, constrained mixture models (CMGND), GND-HMMs.
result Enhanced accuracy and interpretability in financial return modeling.
The paper applies information theory to financial markets, improving risk management and asset allocation.
problem Improving risk management and asset allocation in financial markets.
method Information-theoretic measures (entropy, mutual information, etc.) applied to financial time series.
result Normalized mutual information (NMI) is a powerful measure of temporal dependence in financial markets.
This paper reviews LLMs for credit risk assessment, creating a taxonomy.
problem Assessing credit risk using financial text analysis.
method Systematic review of 60 papers, focusing on model architectures, data types, and explainability mechanisms.
result Developed a taxonomy of LLM-based credit risk models.
Contextualizing financial news improves stock price predictions.
problem Predicting stock prices from financial news requires understanding historical context.
method Proposed a method using a large language model for main articles and a small model for historical context.
result Historical context significantly improves model performance across methods and time horizons.
We consider dynamics of financial markets as dynamics of expectations and discuss such a dynamics from the point of view of phenomenological thermodynamics. We describe a financial Carnot cycle and the financial analogue of a heat machine. We see, that while in physics a perpetuum mobile is absolutely impossible, in ec…
Study detects and explains positional bias in financial LLMs.
problem Positional bias in financial decision-making using LLMs.
method Unified framework and benchmark for detecting and quantifying bias in Qwen2.5 models.
result Positional bias is pervasive, scale-sensitive, and resurfaces under nuanced prompt designs.
We compare correlations and coherent structures in nuclei and financial markets. In the nuclear physics part we review giant resonances which can be interpreted as a coherent structure embedded in chaos. With similar methods we investigate the financial empirical correlation matrix of the DAX and Dow Jones. We will sho…
Paper investigates existence of deflators in financial markets.
problem Existence of equivalent local martingale deflators in semimartingale markets.
method Characterization of deflators using modified semimartingale characteristics.
result Existence of deflators can be characterized by modified semimartingale characteristics.
New method evaluates financial graphs for stock trend forecasting.
problem Lack of dynamic stock relationship graphs and evaluation methods.
method SPNews dataset and novel evaluation methods independent of downstream tasks.
result Evaluation methods can differentiate between various financial relationship graphs.
This paper explores good practices for AI explainability in finance.
problem Complex financial models lack transparency and interpretability.
method Exploring good practices for deploying explainability in AI-based financial systems.
result Developing effective XAI tools for the financial industry.
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.
BERT models outperform GPT in financial engineering sentiment analysis.
problem Using sentiment from news events for commodity trading.
method Benchmarked Transformer models (BERT, GPT) on financial engineering task.
result CopBERT models outperform GPT and vanilla BERT models.
A new copula minimizes distance between distributions.
problem Arbitrariness in copula choice.
method Minimizes Wasserstein distance; linear programming estimation.
result Natural copula provides parsimonious estimation.
KACDP model improves credit default prediction with enhanced interpretability.
problem Insufficient interpretability and limited performance in credit default prediction.
method Kolmogorov-Arnold Networks (KANs) for handling complex multi-dimensional data.
result KACDP model outperforms mainstream models in performance metrics.
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.