The study examines how personal financial experiences shape investor behavior and market dynamics.
problem How do personal financial experiences affect investor behavior and market dynamics?
method Formalized experience-based learning in an OLG model, generating heterogeneity in beliefs, portfolio choices, and trade.
result The model produces new implications for asset holdings, trade volume, and investors' responses to financial crises.
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…
Proposes LSTM for financial market trend forecasting.
problem Challenges in financial market trend forecasting.
method Uses LSTM for financial market trend forecasting.
result Improves performance compared to traditional methods.
We construct a financial "Turing test" to determine whether human subjects can differentiate between actual vs. randomized financial returns. The experiment consists of an online video-game (http://arora.ccs.neu.edu) where players are challenged to distinguish actual financial market returns from random temporal permut…
Research shows Twitter is permeable to financial events, influencing its content and sentiment.
problem Investigating how Twitter reacts to financial events.
method Conducted experiments on a specific financial event (Tesco PLC and Booker Group PLC merger announcement).
result Twitter is permeable to financial events, affecting its content and sentiment.
Computable contracts simplify financial transactions and reduce legal costs.
problem Difficulty in querying, executing, and analyzing text-based financial contracts.
method Develop a Contract Definition Language and illustrate use cases.
result Substantial improvements in customer experience and cost reduction.
Sentiment analysis from LLMs improves financial trading performance.
problem Improving dynamic strategy optimization in financial markets.
method Integration of sentiment analysis from LLMs into RL frameworks.
result Sentiment-enhanced RL models outperform traditional RL models in net worth and cumulative profit.
HedgeAgents boosts financial trading with balanced strategies.
problem Inefficient trading strategies under rapid market changes.
method Integrates LLMs with multi-agent system for robust decision-making.
result 70% annualized return and 400% total return over 3 years.
This is the third installment of the Financial Bubble Experiment. Here we provide the digital fingerprint of an electronic document in which we identify 27 bubbles in 27 different global assets; for 25 of these assets, we present windows of dates of the most likely ending time of each bubble. We will provide that docum…
This is the second installment of the Financial Bubble Experiment. Here we provide the digital fingerprint of an electronic document in which we identify 7 bubbles in 7 different global assets; for 4 of these assets, we present windows of dates of the most likely ending time of each bubble. We will provide that documen…
FactorMiner discovers financial alpha factors with low redundancy.
problem Finding novel financial alpha factors in a vast search space.
method Modular Skill Architecture and Experience Memory to distill and guide exploration.
result FactorMiner constructs a diverse library of high-quality factors with competitive performance.
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.
Improved financial sentiment analysis using simple instruction tuning of LLMs.
problem Lack of accurate financial sentiment analysis by large language models.
method Instruction tuning of general-purpose LLMs with a small portion of financial sentiment data.
result Significant improvement in financial sentiment analysis, especially in complex scenarios.
Generative Adversarial Networks create realistic financial correlation matrices.
problem Creating realistic financial correlation matrices for practical applications.
method Generative Adversarial Networks (GANs) to model correlation matrices.
result GANs can recover known stylized facts about empirical correlation matrices.
In addition to constraining bilateral exposures of financial institutions, there are essentially two options for future financial regulation of systemic risk (SR): First, financial regulation could attempt to reduce the financial fragility of global or domestic systemically important financial institutions (G-SIBs or D…
CNN model predicts financial market movement with better performance.
problem Difficult to predict financial markets due to complex dynamics.
method Proposes a novel one-dimensional CNN model for financial market prediction.
result CNN model achieves more robust and profitable performance than previous approaches.
AI enhances financial forecasting with challenges in regulation and privacy.
problem Challenges in integrating AI with financial services and regulations.
method Integration of AI technologies like deep learning and reinforcement learning.
result AI improves financial forecasting but faces regulatory and privacy issues.
System detects financial misinformation and generates clear explanations.
problem Identifying and explaining fraudulent financial content.
method Combined large language models, pre-processing, and sequential learning.
result Achieved F1-score of 0.8283 for classification and ROUGE-1 of 0.7253 for explanations.
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.
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.
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.
TGN outperforms static GNNs in detecting financial fraud.
problem Anomaly detection in dynamic financial networks.
method Temporal Graph Networks (TGN) for capturing edge dynamics.
result TGN significantly outperforms static GNNs in AUC metrics.
Model explains stock price bubbles through debt crises and financial crashes.
problem Analyzing financial fragility and stock price bubbles.
method Stock-flow consistent model integrating macroeconomic and financial market dynamics.
result Model demonstrates how credit expansion and crash risk lead to recurrent boom-bust cycles.
Lab experiment reveals market imitation and win-stay lose-shift patterns in financial decision-making.
problem Understanding how people make decisions in financial markets.
method Lab-in-the-field experiment with financial information, statistical analysis, and cohort analysis.
result Market imitation and win-stay lose-shift strategies emerge as dominant behaviors in financial decision-making.
Study finds AI-generated financial advice influences life cycle investing patterns.
problem Understanding how AI-generated financial advice impacts life cycle investing.
method Sentiment analysis of prompts from AI-generated financial advice and simulation of lifetime effects.
result AI-generated financial advice leads to life cycle investing patterns, influenced by gender and AI experience.
GANs generate realistic financial data for research.
problem Creating realistic financial datasets for research and benchmarking.
method Proposed a novel type of GAN and methods for preprocessing and evaluation.
result GANs can replicate financial datasets with high fidelity.
M2VN forecasts financial volatility by fusing time series data with news embeddings.
problem Forecasting financial volatility with unstructured news data.
method Combines deep neural networks with open-source market features and news embeddings.
result M2VN outperforms existing models in financial volatility forecasting.
Survey of determinism issues in financial AI systems.
problem Vulnerabilities in reproducibility of financial AI systems.
method Literature review and first-party experiments on public financial datasets.
result Proposed a layered evaluation framework linking modality-specific metrics to audit readiness.
A new contrastive learning method extracts asset embeddings from financial time series.
problem Extracting meaningful latent features from noisy financial data.
method Contrastive learning framework using hypothesis testing for positive and negative samples.
result Effective asset embeddings significantly outperform existing methods on financial tasks.
Paper uses LLMs for financial forecasting, overcoming sequence reasoning and multi-modal challenges.
problem Challenges in financial time series forecasting, especially cross-sequence reasoning and multi-modal signals.
method Combines LLMs with financial data and news, using zero-shot/few-shot inference and instruction-based fine-tuning.
result LLMs can offer explainable financial forecasts, leveraging cross-sequence reasoning and multi-modal information.
Paper uses LLMs to detect financial anomalies.
problem Detecting irregular financial entries.
method Non-semantic financial data encoding with LLMs embeddings, tested 3 models.
result LLMs improve anomaly detection in financial data.
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.
New method detects heuristics in complex game strategies.
problem Understanding decision-making in games with infinite strategy spaces.
method Introducing decoupled strategies to detect convergence towards Nash equilibria.
result Predictive measure ΔD reveals participants' actions with high success rate.
DoubleEnsemble improves financial predictions by selecting key features and reweighting samples.
problem Overfitting and instability in financial data analysis.
method Sample reweighting and feature selection using learning trajectory and shuffling.
result DoubleEnsemble outperforms baseline methods in financial prediction tasks.
A deep RL framework improves cryptocurrency portfolio management.
problem Optimizing financial portfolio management in cryptocurrency markets.
method Reinforcement Learning framework with EIIE topology, PVM, OSBL, and reward function.
result Framework outperforms other strategies in backtest experiments.
Proving the existence of speculative financial bubbles even a posteriori has proven exceedingly difficult so anticipating a speculative bubble ex ante would at first seem an impossible task. Still as illustrated by the recent turmoil in financial markets initiated by the so called subprime crisis there is clearly an ur…
Intelligent financial data analysis system improves accuracy and efficiency.
problem Inefficient and inaccurate financial data analysis due to complex data and evolving contexts.
method Integrates LLMs with RAG technology for financial data analysis.
result Significant improvements in accuracy and recall (78.6% and 89.2%) compared to baseline.
Benchmark evaluates financial misinformation detection models, revealing weaknesses without external context.
problem Detecting financial misinformation without external references.
method RFC Bench at paragraph level, two tasks: reference-free detection and comparison-based diagnosis.
result Performance improves with comparative context, revealing model weaknesses in reference-free settings.
Financial institutions use LSTM models to predict customer goals.
problem Predicting customer goals and actions in financial services.
method Used LSTM models with state-space graph embeddings on historical customer traces.
result Demonstrated the effectiveness of LSTM models in predicting customer goals and actions.
NIFTY dataset for financial forecasting models.
problem Improving financial market forecasting with LLMs.
method Two datasets for supervised and reinforcement learning.
result Demonstrates applications in stock price prediction.
Method calculates financial distributions using recursive relationships.
problem Analyzing the distribution of financial functions at discrete points.
method Recursive method to calculate probability distributions.
result High accuracy demonstrated in numerical experiments.
This work discusses AAD for financial model calibration and its parallelization benefits.
problem Calibrating stochastic financial models using Automatic Adjoint Differentiation.
method Demonstrates the use of Automatic Adjoint Differentiation for functions in financial models and its parallelization potential.
result Theoretical and numeric results show that AAD allows perfect SIMD parallelization and is efficient.
FININ predicts financial markets by modeling news interactions and influence.
problem Complex diffusion of financial news into market prices.
method FININ is a novel model that captures news links and interactions, integrating market data and news articles.
result FININ outperforms advanced models with a 0.429 and 0.341 improvement in daily Sharpe ratio for S&P 500 and NASDAQ 100 respectively.
GraphShield uses dynamic graph learning to detect and visualize financial risks.
problem Detecting and mitigating risks in financial networks.
method Enhanced Cross-Domain Information Learning, Advanced Risk Recognition, Risk Propagation Visualization.
result GraphShield effectively identifies and visualizes hidden 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.
Market events such as order placement and order cancellation are examples of the complex and substantial flow of data that surrounds a modern financial engineer. New mathematical techniques, developed to describe the interactions of complex oscillatory systems (known as the theory of rough paths) provides new tools for…
This paper distills financial indicators into neural networks to reduce noise and improve accuracy.
problem Reduction of non-stationary noise in financial time series data.
method Co-distillation of smaller networks trained on indicators to transfer prior knowledge and reduce overfitting.
result The proposed method outperforms traditional methods in terms of speed and accuracy on real financial datasets.
Study compares nine deep learning architectures for multi-horizon financial forecasting.
problem Evaluating the performance of deep learning architectures for multi-horizon financial forecasting.
method Conducted 918 experiments across cryptocurrency, forex, and equity markets using nine architectures.
result ModernTCN achieves the best mean rank (1.333) with a 75 percent first-place rate.