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.
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.
German FinBERT improves financial text analysis performance.
problem Capturing domain-specific nuances in financial text.
method German FinBERT is a pre-trained German language model trained on a large corpus of financial data.
result German FinBERT outperforms standard models on finance-specific tasks.
Study builds a Japanese financial-specific LLM through continual pre-training.
problem Lack of domain-specific Japanese financial LLMs.
method Continual pre-training on Japanese financial-focused datasets using a base Japanese LLM.
result Tuned model outperforms original model on Japanese financial benchmarks.
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.
GPU acceleration speeds up financial machine learning training time.
problem Time-intensive classifier training in financial machine learning.
method Deployed NVIDIA GPUs for parallel high-speed arithmetic operations.
result Significantly faster training time achieved.
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.
Study examines cross-training neural networks for financial index prediction.
problem Predicting financial indexes from different markets using machine learning.
method Investigated various neural network architectures and trained them on one market index to predict another.
result Cross-training models on one market index improved prediction accuracy for another market index.
This research improves financial market predictions using LSTM networks.
problem Accurate real-time forecasting of financial time series.
method Sequentially trained many-to-one LSTMs with adaptive training epochs.
result Our approach maintains superior accuracy as predictions are made further in the future.
Generative model uses random convolutional features to create financial time series.
problem Generating realistic financial time series with limited data and avoiding overfitting.
method Train generators by matching random convolutional features of real and generated time series, using SOCK (SOft Competing Kernels) feature map.
result Generators trained with random SOCK features outperform baselines across various financial datasets.
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.
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.
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.
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.
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.
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.
ChatGPT predicts stock market reactions from news headlines without financial training.
problem Predicting stock price movements using non-financial data.
method Used post-knowledge-cutoff headlines to train ChatGPT-4, which forecasts stock market reactions.
result ChatGPT-4 can predict stock market reactions with high accuracy, especially for small stocks and negative news.
Generative model for financial time series using structured noise and signature learning.
problem Creating synthetic financial data to reflect real-world market dynamics.
method Structured noise, moving average model, signature transform, reinforcement learning.
result Model effectively captures key financial characteristics and outperforms existing methods.
FiNCAT tool automatically identifies financial numerals in documents.
problem Differentiating between in-claim and out-of-claim numerals in financial documents.
method Extracts context embeddings of numerals using BERT, then uses Logistic Regression to classify.
result Achieved a Macro F1 score of 0.8223 on validation set.
Global neural networks improve financial forecasting accuracy with larger, diverse datasets.
problem Mixed empirical performance in financial time series forecasting due to local model estimation.
method Global estimation strategy that pools information across cross-sections of over 10,000 global stocks.
result Forecasting accuracy improves with larger and more heterogeneous training datasets.
DHLNN improves deep hedging for financial derivatives with faster convergence and better stability.
problem Challenges in computational inefficiency, sensitivity to noisy data, and optimization complexity in deep hedging methods.
method Integrates periodic fixed-gradient optimization and linearized training dynamics to stabilize and accelerate deep learning model training.
result Demonstrates faster convergence, improved stability, and superior hedging performance across diverse market scenarios.
Deep learning for financial derivatives pricing and hedging.
problem Model-free pricing and optimal hedging of financial derivatives.
method Neural networks for offline training and online application.
result Accurate model-free price bounds and optimal hedging strategies.
IITK wins FinSim 2020 task on financial hypernym detection.
problem Classifying financial terms into hypernym concepts in an external ontology.
method Used context-dependent and context-independent word embeddings (Word2vec and BERT) for classification.
result Ranked 1st based on mean rank and accuracy metrics.
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.
PyFi uses adversarial agents to train VLMs on financial image understanding.
problem Training VLMs to understand complex financial questions.
method PyFi-600K dataset and adversarial MCTS mechanism.
result Fine-tuned VLMs improve by 19.52% and 8.06% on financial question accuracy.
MarketGPT models financial time series with realistic order flow data.
problem Creating accurate financial market simulations.
method Generative pre-trained transformer (GPT) for long sequence generation.
result Model reproduces key features of real financial markets and stylized facts.
Generative model solves financial market equilibria with stable reinforcement learning.
problem Financial market equilibria under realistic frictions and multiple agents.
method Generative adversarial reinforcement learning with decoupling feedback.
result Algorithm learns and predicts asset returns and volatilities.
A new neural network model simulates financial markets without assuming underlying dynamics.
problem Modeling financial time series without assuming underlying dynamics.
method Neural network based generative model using a parsimonious Variational Autoencoder framework.
result Works reliably in small data environments, providing a new performance evaluation metric.
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.
LLMs outperform human analysts in predicting earnings direction.
problem Evaluating financial statements without narrative or industry-specific information.
method Trained GPT4 on standardized, anonymous financial statements and instructed to predict earnings direction.
result LLMs predict earnings directionally with accuracy comparable to narrowly trained ML models.
Quantum models generate financial time series with desired properties.
problem Generating synthetic financial data with temporal correlations.
method Quantum generative adversarial networks (QGANs) with quantum and classical components.
result QGANs can generate financial time series with matching distribution and temporal correlations.
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.
KodeXv0.1 improves financial question answering over GPT-4.
problem Lack of specialized financial language models.
method Custom training on financial documents, RAG-aware 4bit LoRA tuning.
result KodeX-8Bv0.1 outperforms GPT-4 by up to 9.24% in financial tasks.
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.
An automatic program that generates constant profit from the financial market is lucrative for every market practitioner. Recent advance in deep reinforcement learning provides a framework toward end-to-end training of such trading agent. In this paper, we propose an Markov Decision Process (MDP) model suitable for the…
New method uses non-translation invariant risk measures for fair financial derivative pricing.
problem Inequalities in financial derivative pricing under traditional risk measures.
method Deep reinforcement learning with modified deep hedging algorithm.
result Effective pricing of financial derivatives without price inflation.
FinGPT democratizes financial data for LLMs, enabling innovation.
problem Limited financial text datasets and disparities between general and financial text data.
method Automates collection and curation of real-time financial data from diverse Internet sources, fine-tuning with RLSP and LoRA.
result Democratizes access to financial data for LLMs, enabling innovation.
Deep Bellman Hedging uses reinforcement learning to optimize financial portfolio hedging.
problem Optimizing financial portfolio hedging with derivatives and trading frictions.
method Actor-critic reinforcement learning algorithm with continuous state and action spaces.
result Trained model provides optimal hedge for any initial portfolio and market state.
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…
AMA-LSTM improves stock volatility prediction using adversarial training.
problem Predicting stock volatility from financial audio data is challenging due to stochasticity and bias.
method Adversarial training to generate perturbations that simulate stochasticity and bias.
result AMA-LSTM outperforms state-of-the-art methods in predicting stock volatility.
Proposes a method to improve financial time series forecasting using compact representations and contrastive loss.
problem Financial time series forecasting with small datasets and overfitting issues.
method Class-conditioned latent variable model, mutual information maximization, contrastive loss, deep autoregressive models.
result Empirical experiments show improved performance compared to state-of-the-art methods.
This paper explores deep learning for financial trading, integrating sentiment analysis.
problem Maximizing profit and minimizing loss in financial trading.
method Supervised and reinforcement learning schemes, integrating sentiment analysis.
result Demonstrates the effectiveness of deep learning methods in financial trading.
Improved financial VA intent classification accuracy.
problem Determining user intents for unseen open intents.
method Supervised pre-training of intent representations using prefix-tuning and fine-tuning.
result 1.63% - 2.07% higher accuracy on banking77 benchmark.
Unified LLM-agent with RL improves financial trading performance.
problem LLMs struggle with complex, multi-step financial tasks.
method Fusion of LLMs and gradient-based RL for policy optimization.
result Improves LLM performance in trading and other financial tasks.
Deep reinforcement learning improves trading performance in financial markets.
problem Improving trading performance in financial markets.
method Deep Q-network (DQN) for designing long-short trading strategies.
result Trained reinforcement learning agent outperformed an index benchmark in trading E-mini S&P 500 futures contracts.
Training deep learning models that generalize well to live deployment is a challenging problem in the financial markets. The challenge arises because of high dimensionality, limited observations, changing data distributions, and a low signal-to-noise ratio. High dimensionality can be dealt with using robust feature sel…
VAIOM models financial returns using continuous input and categorical output.
problem Modeling continuous, noisy, and heterogeneous financial data.
method VAIOM is a decoder-only Transformer that separates input representation from output likelihood.
result VAIOM models outperform fixed single-bar LightGBM baseline in both Test halves.
Market-GAN adds context control to financial market data generation.
problem Lack of context labels and precision in generating context-aligned financial data.
method Proposes Contextual Market Dataset and Market-GAN architecture integrating GAN, autoencoder, and supervisors.
result Market-GAN outperforms state-of-the-art models in Dow Jones data generation.