Modeling investor behavior from financial advisor notes using NLP.
problem Identifying behavioral coaching opportunities for financial advisors.
method Topic modeling and supervised classification model.
result Predicting investor needs during adverse market conditions.
Study explores factors influencing saving behavior among Dhaka employees.
problem Factors influencing saving behavior among Dhaka employees.
method Quantitative approach with cross-sectional survey design, structured questionnaire, descriptive statistics, reliability analysis, regression analysis.
result Only financial management practices had a significant positive relationship with saving behavior.
Empirical evidence supports new financial market definitions.
problem Investor risk attitudes in financial markets.
method Developed a new method to analyze risk attitudes.
result Risk-averse behavior in equity investors, risk-loving behavior in risk-free asset investors.
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.
Study examines if LLMs' trading styles match real market behavior.
problem Lack of behavioral consistency in LLMs' trading strategies.
method Year-long simulations with LLMs, operationalizing behavioral finance drivers, and comparing with financial theory.
result LLMs' strategy switching is only partially consistent with behavioral finance theories.
Study uses ML to analyze financial behavior in big data.
problem Challenges in analyzing financial big data.
method Applied machine learning to financial behavioral data.
result ML models can effectively estimate performance in financial markets.
New measure quantifies financial erratic behavior.
problem Measuring similarity between erratic financial time series.
method Combining probability distributions and Bayesian change point detection.
result Greater similarity among sectors than countries in erratic behavior.
A new oscillator measures trending behavior of financial instruments.
problem Detecting underlying deterministic components in financial market prices.
method Financial market geometry and tube oscillator derived from past history.
result Simple trading strategy based on tube oscillator leads to consistent positive returns.
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.
Herd behavior is an important economic phenomenon, especially in the context of the recent financial crises. In this paper, herd behavior in global stock markets is investigated with a focus on intercontinental comparison. Since most existing herd behavior indices do not provide a comparative method, we propose a new h…
We study the phase transition of dynamical herd behaviors for the yen-dollar exchange rate in the Japanese financial market. It is obtained that the probability distribution of returns satisfies the power-law behavior with three different values of the scaling exponent 3.11 (one time lag τ = 1 minute), 2.81 (30 minut…
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.
This study analyzes the collective behavior of world stock markets using RMT.
problem Understanding the collective behavior and structure of interconnected world stock markets.
method Analysis of cross-correlation matrix using Random Matrix Theory (RMT).
result Emergence of a world financial market structure with three communities.
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.
New econometric results for financial duration models under varying tail behaviors.
problem Estimation and inference challenges in financial durations models with random event counts.
method Analysis of likelihood estimators for ACD models, focusing on tail behavior and stationarity.
result Asymptotic normality breaks down for tail indices smaller than one, leading to mixed Gaussian estimators with non-standard rates of convergence.
Deep learning predicts risky behavior in retail investors for financial risk management.
problem Predicting profitable trading behavior in retail investors.
method Developed a deep learning model to predict trader profitability.
result Deep learning outperforms conventional machine learning methods in predicting trader behavior.
Study models human investors' sub-rational behavior in financial markets.
problem Lack of a comprehensive model for human sub-rationality in financial markets.
method Flexible reinforcement learning model incorporating five human sub-rational aspects.
result Model accurately reproduces human behavior and reveals insights into market dynamics.
Agent-based models now use data-driven parameters to explain financial market dynamics.
problem Understanding the complex behavior of financial markets through agent interactions.
method Data-driven approach to model parameters, combining big data with agent-based modeling.
result Agent-based models can now simulate financial market dynamics using real data.
The paper studies financial market coherence and incoherence using phase oscillators.
problem Understanding the dynamics of financial market coherence and incoherence.
method A coupled dynamical system of phase oscillators to model financial price fluctuations.
result Financial markets exhibit a coexistence of coherent and incoherent collective behavior.
A new approach to the understanding of complex behavior of financial markets index using tools from thermodynamics and statistical physics is developed. Physical complexity, a magnitude rooted in Kolmogorov-Chaitin theory is applied to binary sequences built up from real time series of financial markets indexes. The st…
Examines financial risks' impact on EU-15 economic growth.
problem The impact of financial risks on economic growth in EU-15.
method Panel estimated generalized least squares method with additional control variables.
result Financial risks significantly impact economic growth in EU-15.
A new approach to the understanding of the complex behavior of financial markets index using tools from thermodynamics and statistical physics is developed. Physical complexity, a magnitude rooted in the Kolmogorov-Chaitin theory is applied to binary sequences built up from real time series of financial markets indices…
Paper models market dynamics using bull and bear forces.
problem Complex market dynamics influenced by biases and narratives.
method Bias to Behavior from Bull-Bear Dynamics (B4) model.
result Model predicts market trends with superior performance and interpretable insights.
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.
The paper models insurance market dynamics under uncertainty and financial frictions.
problem Modeling insurer behavior under uncertainty and financial frictions.
method Dynamic equilibrium model of insurance market with competitive insurers maximizing shareholder value.
result Investment can lead to lower insurance prices and negative loadings under certain conditions.
Proposes a model combining order book data and herd behavior to replicate long-range memory in financial returns.
problem Replicating long-range memory in financial returns and trading activity.
method Combines empirical order book data and financial herd behavior model.
result Model successfully replicates long-range memory in absolute returns and trading activity.
Model predicts synchronized crowd behavior with tipping points.
problem Understanding and predicting synchronized crowd behavior.
method Introduces order parameter and identifies tipping point.
result Crowd behavior is driven by active, volatile agents.
Generative AI reduces herd behavior in trading, but can also lead to optimal herding.
problem Impact of generative AI on financial stability and herd behavior.
method Laboratory experiments with large language models replicating human trading behavior.
result AI agents make more rational decisions than humans, reducing herd behavior but also potentially leading to optimal herding.
New RL framework simulates financial market dynamics.
problem Complex financial market dynamics under various scenarios.
method Two RL families learn simultaneously, using Deep RL and parametrized reward.
result Agents learn a shared policy for diverse behaviors.
Survey visual analytics methods for detecting anomalous user behaviors.
problem Understanding and detecting anomalous user behaviors in various domains.
method Survey and classification of visual analytics methods in four categories.
result Discussion of findings and potential research directions.
Study large financial markets' pricing and hedging of financial claims.
problem Pricing and hedging of financial claims in large financial markets.
method Examined large Black-Scholes model, connected arbitrage and α-quantile price. result Connection between asymptotic arbitrage and behavior of α-quantile price. A characteristic feature of complex systems in general is a tight coupling between their constituent parts. In complex socio-economic systems this kind of behavior leads to self-organization, which may be both desirable (e.g. social cooperation) and undesirable (e.g. mass panic, financial "bubbles" or "crashes"). Abund…
A new algorithm adapts to changing user behaviors in finance.
problem Adapting to changing user behaviors in financial recommendations.
method History-Augmented Collaborative Filtering using a custom neural network.
result The algorithm provides dynamic financial recommendations.
Leverage is strongly related to liquidity in a market and lack of liquidity is considered a cause and/or consequence of the recent financial crisis. A repurchase agreement is a financial instrument where a security is sold simultaneously with an agreement to buy it back at a later date. Repurchase agreements (repos) ma…
Modeling bank leverage dynamics to understand systemic risk in financial markets.
problem Understanding systemic risk in financial markets triggered by bank leverage dynamics.
method Developed a dynamical model of bank leverage, analyzing coupled dynamics in isolated and interconnected bank models.
result Identified a procyclical feedback loop between asset prices and leverage, leading to chaotic dynamics.
Extended speculation game improves Hurst exponent of financial time series.
problem Anti-persistent market price behavior resulting in small Hurst exponent.
method Introduced a perturbative part to price change considering additional effects.
result Improved Hurst exponent value of financial time series.
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.
Model assesses credit risk using behavioral data from Experian and Bank of Italy.
problem Improving credit risk assessment in financial institutions.
method Statistical and machine learning techniques applied to behavioral data from Experian and Bank of Italy.
result Demonstrates transferability of the model from private to central data.
Modeling financial markets with memory using fractional calculus and Brownian motion.
problem Capturing memory effects in financial markets using stochastic models.
method Fractional Langevin equation with colored noise generated by fractional Brownian motion.
result Anomalous marginal glass phase observed in some regions of the system.
LLMs prefer Bitcoin under crisis frames, affecting financial decisions.
problem Testing whether LLMs have built-in biases towards specific financial assets.
method Developed a three-level audit protocol to examine Bitcoin's representation and influence in LLMs.
result An identifiable internal feature in LLMs can be perturbed to move financial choices, but only within measurable limits.
Study uses cohomology theory to analyze 2008 financial crisis in Thai stock market.
problem Analyzing the 2008 financial crisis in the Thai stock market.
method Hybrid mathematical superstructure with cohomology theory, Pauli matrix, and Wilson loop.
result Identified the 2008 financial market crash using cohomology group of sphere over tensor field.
Study uses OT to simulate markets, revealing power-law returns are driven by informational effect.
problem Reproduce power-law returns in financial markets using realistic simulations.
method Constructed artificial markets, used optimal transport (OT) to measure similarity, incrementally introduced behavioral components.
result Informational effect of prices is dominant in reproducing power-law returns, and multiple components interact synergistically.
Modeling financial market dynamics with noise and fundamentalist agents.
problem Understanding opinion formation and market behavior in financial markets.
method Agent-based model with Erdös-Rényi random graph structure, incorporating anxiety parameter.
result Model accurately reproduces key market features like fat-tailed returns and volatility clustering.
Modeling stock price adjustments due to irrational agent behavior.
problem Understanding and measuring irrational behavior in financial markets.
method Irrational Fractional Brownian Motion model to analyze agent reactions to news.
result Observation of kink-like effect suggesting soliton-like behavior in stock price adjustments.
In this paper, making use of recent statistical physics techniques and models, we address the specific role of randomness in financial markets, both at the micro and the macro level. In particular, we review some recent results obtained about the effectiveness of random strategies of investment, compared with some of t…
Graph Ricci flow reveals hidden hierarchies in stock market correlations.
problem Detecting hidden structures in the complex stock market graph.
method Using graph Ricci curvature and flow techniques to analyze the NASDAQ 100 index.
result Algorithm detects hidden hierarchies, community behavior, and clustering in financial markets.
Measures risk contagion in financial networks using CoVaR.
problem Assessing stability of complex financial systems.
method Financial network model with bipartite graph of institutions and assets, heavy-tailed distributions, copula models, CoVaR and ECI.
result Proposes the Extreme CoVaR Index (ECI) for capturing risk contagion strength.
Deep learning model predicts online fraud using customer behavior data.
problem Predicting online financial fraud from customer behavior data.
method Recurrent Neural Network (RNN) integrated with Markov Transition Field (MTF).
result The proposed model significantly improves fraud prediction compared to traditional methods.