Survey of financial foundation models for diverse applications.
problem Challenges in applying general-purpose FMs to financial tasks.
method Review of financial foundation models (FFMs) in three modalities.
result Emergence of FFMs designed specifically for finance.
FinCast is a foundation model for financial time-series forecasting that outperforms existing methods.
problem Challenges in financial time-series forecasting due to temporal non-stationarity, multi-domain diversity, and varying temporal resolutions.
method FinCast is a foundation model specifically designed for financial time-series forecasting, trained on large-scale financial datasets.
result FinCast exhibits robust zero-shot performance, effectively capturing diverse patterns without domain-specific fine-tuning.
Foundation models improve volatility forecasting in finance.
problem Improving volatility forecasting in financial markets.
method Evaluation of TimesFM model, incremental fine-tuning, comparison with econometric benchmarks.
result Incremental fine-tuning improves forecast accuracy and outperforms traditional models.
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problem Outdated algorithms and lack of open financial benchmarks.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.
Chronos models improve financial forecasting by integrating multivariate data.
problem Improving financial forecasting accuracy using multivariate data.
method Evaluation of Chronos-2 on multivariate and univariate financial forecasting models.
result Multivariate forecasts consistently outperform univariate forecasts, especially for interest rates.
ProbFM provides principled uncertainty quantification for financial forecasting.
problem Lack of principled uncertainty quantification in financial applications.
method Probabilistic Time Series Foundation Model with Uncertainty Decomposition using Deep Evidential Regression (DER).
result DER maintains competitive forecasting accuracy while providing explicit epistemic-aleatoric uncertainty decomposition.
Foundation models outperform supervised methods in time series forecasting across various operational regimes.
problem Lack of domain-specific training and ongoing maintenance in supervised learning for time series forecasting.
method Evaluation of foundation models against standard supervised approaches across four operational regimes: periodic, physically constrained, stochastic, and demand forecasting.
result Foundation models are optimal for cold-start or long-tail scenarios and perform well in domains with transferable periodic structures.
Kronos improves financial time series analysis with a pre-trained model.
problem Limited application of large-scale models to financial candlestick data.
method Unified, scalable pre-training framework for financial K-line modeling.
result Kronos excels in financial tasks like price forecasting and volatility prediction.
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.
PRAGMA models financial event sequences for various banking tasks.
problem Handling diverse financial data for multiple applications.
method Pre-training a Transformer model on a large banking event corpus with a self-supervised objective.
result PRAGMA achieves superior performance across multiple financial domains from raw event sequences.
MarS simulates financial markets using generative models.
problem Simulating realistic financial market effects.
method Order-level generative foundation model (LMM) for realistic, interactive, and controllable order generation.
result Strong scalability and robust realism in MarS.
Fine-tuning a time series model improves financial price prediction accuracy.
problem Improving accuracy in predicting financial market prices using large models.
method Continual pre-training of a time series foundation model on financial data to fine-tune its performance for price prediction.
result The fine-tuned model outperforms the baseline in various financial metrics.
Establishes a microstructural foundation for a rough log-normal volatility model.
problem Developing a robust model for financial volatility under microstructural effects.
method Introduced a sequence of order-driven financial market models with Poisson process arrivals and analyzed their convergence to a log-normal rough volatility model.
result Weak convergence of price-volatility process to a log-normal rough volatility model with established weak error rates.
TSFMs improve financial forecasting from diverse datasets.
problem Challenges in forecasting financial time series due to noisy, non-stationary, and heterogeneous data.
method Empirical study of TSFMs in global financial markets, evaluating zero-shot inference, fine-tuning, and pre-training from scratch.
result Pre-trained TSFMs on financial data achieve substantial forecasting and economic improvements, highlighting the value of domain-specific adaptation.
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.
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.
Pretrained time-series models outperform train-from-scratch baselines in financial return forecasting.
problem Financial return forecasting
method Pretrained time-series foundation models
result Pretrained TSFMs dominate the ranking distribution, accounting for 8 of 10 task-level wins.
FinAgent tackles financial trading with multimodal data and advanced AI.
problem Challenges in handling multimodal financial data and limited generalizability.
method Multimodal foundational agent with tool augmentation, dual-level reflection, and diversified memory retrieval.
result Significantly outperforms state-of-the-art baselines in financial trading tasks.
Foundation for robust finance using rough path theory.
problem Mathematical models of financial markets under Knightian uncertainty.
method Introducing Property (RIE) for càdlàg paths, proving existence of rough integrals, verifying admissibility of trading strategies.
result Existence and stability of rough path integrals for non-gradient integrands.
TSFMs improve financial forecasting across diverse tasks with strong transferability.
problem Complex nonlinear relationships, temporal dependencies, and limited data in financial time series forecasting.
method Pretraining on diverse time series corpora followed by task-specific adaptation.
result Tiny Time Mixers (TTM) achieved 25-50% better performance on limited data and 15-30% improvements on longer datasets.
Prior to the financial crisis mortgage securitization models increased in sophistication as did products built to insure against losses. Layers of complexity formed upon a foundation that could not support it and as the foundation crumbled the housing market followed. That foundation was the Gaussian copula which faile…
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.
Survey of AI in finance covering models, strategies, and knowledge systems.
problem Challenges in applying AI to financial markets, especially in high-frequency trading.
method Systematic analysis of financial AI across predictive models, decision frameworks, and knowledge augmentation systems.
result Critical trade-offs and gaps between theoretical advances and practical implementation in financial AI.
TradeFM learns market microstructure from trade events, improving financial model accuracy.
problem Lack of generalizable models for market microstructure.
method Generative Transformer model trained on billions of trade events, using scale-invariant features and universal tokenization.
result TradeFM generates rollouts that match key stylized facts of financial returns and outperforms existing models.
Develops BPDS for better financial portfolio decisions.
problem Model uncertainty in financial time series forecasting.
method Bayesian dynamic modelling and predictive decision synthesis.
result Improved predictive and decision outcomes compared to traditional Bayesian analysis.
Quantum crypto-economics models price risks in blockchain technology.
problem Quantum technology's potential to undermine blockchain security.
method Building financial models to price quantum risk in blockchain scenarios.
result Quantum crypto-economics models can assess and price quantum risks in blockchain.
UCFE benchmarks LLMs in financial tasks with human feedback.
problem Evaluating LLMs' financial task performance and user satisfaction.
method Hybrid approach combining human expert evaluations and dynamic interactions.
result Significant alignment between benchmark scores and human preferences (Pearson correlation coefficient of 0.78).
Financial markets modeled like brain networks using dMNC.
problem Understanding latent dynamics in financial markets.
method Biologically inspired framework using dMNC.
result Structural persistence, regime shifts, and early warning signals identified.
The paper proposes a new risk model for foundation models in finance.
problem Understanding how foundation models affect trading strategies' risk and return.
method An extension of the CAPM, separating systematic and idiosyncratic risks.
result Monte Carlo dropout measures the epistemic risk of foundation models.
QA-Token improves tokenization for noisy data, boosting model performance.
problem Tokenization ignores data quality, limiting model effectiveness on noisy corpora.
method QA-Token combines signal quality with vocabulary construction through bilevel optimization and reinforcement learning.
result QA-Token achieves state-of-the-art performance on genomic and financial datasets.
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.
This paper argues that the fundamental principle of contemporary financial economics is balanced reciprocity, not the principle of utility maximisation that is important in economics more generally. The argument is developed by analysing the mathematical Fundamental Theory of Asset Pricing with reference to the emergen…
Delphyne improves financial time series models with pre-trained language models.
problem Lack of financial data and negative transfer effect in existing time-series pre-trained models.
method Delphyne is a pre-trained model for financial time series that addresses the lack of financial data and negative transfer effect.
result Delphyne achieves competitive performance and superior performances on various financial tasks.
Model financial default cascades on sparse graphs via hitting times.
problem Capturing systemic risk in large, sparsely-connected financial networks.
method Dynamic particle systems with hitting times and convergence theory.
result Characterization of default time distribution in tree-like networks.
Mathematical framework for differential machine learning in finance.
problem Theoretical assumptions in financial models and their impact on machine learning algorithms.
method Rigorous mathematical framework for differential machine learning in finance.
result Theoretical grounding enhances the predictive capabilities of neural networks in financial applications.
Proposes a new metric for financial risk based on volatility's local deviations.
problem Inefficiencies in classical risk metrics like volatility.
method Introduces pointwise regularity via the Hurst-Holder exponent.
result A more nuanced assessment of market inefficiencies and mechanisms for restoring equilibrium.
This paper benchmarks FinGPT for financial datasets using open-source large language models.
problem Challenges in integrating GPT-based models with financial datasets.
method Instruction Tuning paradigm for open-source large language models adapted for financial contexts.
result Demonstrates the effectiveness and adaptability of FinGPT in financial tasks.
Methodology measures financial impacts using existing credit loss infrastructure.
problem Measuring the impact of financial scenarios on expected credit losses.
method Captures scenario effects through changes in default probabilities; uses existing provisioning infrastructure.
result Methodology validated through standardized climate scenario exercise in Canada and Quebec.
The 1/3 Financial Rule helps prevent household bankruptcy through balanced spending, savings, and debt repayment.
problem Reducing household bankruptcy risk through effective financial planning.
method Mathematical modeling, game theory, behavioral finance, and technological analysis.
result The 1/3 Financial Rule emerges as a robust solution for supporting household financial stability.
Study uses MTD model to optimize portfolios by capturing complex financial asset relationships.
problem Capturing nonlinear and directional relationships in financial markets.
method Directed and weighted financial networks using Mixture Transition Distribution (MTD) model.
result Portfolio optimization with network-based assortativity measures outperforms classical methods.
We present the qGaussian generalization of the Merton framework, which takes into account slow fluctuations of the volatility of the firms market value of financial assets. The minimal version of the model depends on the Tsallis entropic parameter q and the generalized distance to default. The empirical foundation and …
We seek to deepen understanding of the micro-foundations of institutionalization while contributing to a sociological theory of markets by investigating the puzzle of price bubbles in financial markets. We find that such markets, despite textbook conditions of high efficiency -- perfect information, atomistic agents, n…
FinCARE combines financial data and AI reasoning to improve causal analysis of financial performance.
problem Correlation-based analysis fails to capture true causal relationships in financial performance.
method Hybrid framework integrating causal discovery algorithms with financial domain knowledge from SEC filings and LLM reasoning.
result KG+LLM-enhanced methods improve causal discovery across PC, GES, and NOTEARS by 36-366%.
Study uses LLMs for financial sentiment analysis without fine-tuning.
problem Lack of prescriptive knowledge to leverage generative models in FSA.
method Proposes a design framework with heterogeneous LLM agents based on Minsky's theory.
result Framework yields better accuracies, especially with substantial discussions.
Neural networks improve financial derivative pricing accuracy.
problem Improving accuracy in financial derivative pricing.
method Use neural networks to model drift and volatility in SDE models, optimize using SGD for European options and PDE for American options.
result Neural network models outperform traditional models in pricing derivatives.
We prove dual attainment for multi-asset financial derivatives pricing.
problem Model-independent pricing and hedging of complex financial derivatives.
method Established duality and attained optimizers for multimarginal, multi-asset martingale optimal transport.
result Existence of dual optimizers under mild conditions for arbitrary numbers of assets and time periods.
Unified framework for complex financial networks using lattice theory.
problem Complex financial networks with multiple currencies and dependencies.
method Recast classical financial clearing model into lattice liability networks.
result Lattice-valued clearing sections form a complete lattice, enabling tractable analysis.
Generative diffusion models improve financial LOB simulation and forecasting.
problem High noise and complexity in financial LOB data makes deep generative models ineffective.
method Convert LOB data to images, apply diffusion models with inpainting for long-term sequence generation.
result Our method achieves state-of-the-art performance on LOB-Bench, improving coherence over local details.