Behavioral Economics

FinHEAR: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making ArXiv ID: 2506.09080 “View on arXiv” Authors: Jiaxiang Chen, Mingxi Zou, Zhuo Wang, Qifan Wang, Dongning Sun, Chi Zhang, Zenglin Xu Abstract Financial decision-making presents unique challenges for language models, demanding temporal reasoning, adaptive risk assessment, and responsiveness to dynamic events. While large language models (LLMs) show strong general reasoning capabilities, they often fail to capture behavioral patterns central to human financial decisions-such as expert reliance under information asymmetry, loss-averse sensitivity, and feedback-driven temporal adjustment. We propose FinHEAR, a multi-agent framework for Human Expertise and Adaptive Risk-aware reasoning. FinHEAR orchestrates specialized LLM-based agents to analyze historical trends, interpret current events, and retrieve expert-informed precedents within an event-centric pipeline. Grounded in behavioral economics, it incorporates expert-guided retrieval, confidence-adjusted position sizing, and outcome-based refinement to enhance interpretability and robustness. Empirical results on curated financial datasets show that FinHEAR consistently outperforms strong baselines across trend prediction and trading tasks, achieving higher accuracy and better risk-adjusted returns. ...

Financial Literacy Around the World: An Overview of the Evidence with Practical Suggestions for the Way Forward ArXiv ID: ssrn-2094887 “View on arXiv” Authors: Unknown Abstract Financial literacy programs are fast becoming a key ingredient in financial policy reform worldwide. Yet, what is financial literacy exactly and what do we know Keywords: Financial Literacy, Financial Education, Consumer Behavior, Policy Reform, Behavioral Economics, Personal Finance Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a literature review and policy discussion on financial literacy, focusing on definitions, survey results, and program effectiveness without advanced mathematical derivations or detailed backtesting/implementation frameworks. flowchart TD A["Research Goal: Assess global<br>financial literacy evidence &<br>identify policy best practices"] --> B["Methodology: Meta-analysis &<br>literature review of 25+ countries"] B --> C{"Data Inputs:"} C --> D["OECD/INFE Surveys"] C --> E["National Financial<br>Capability Studies"] C --> F["Behavioral Economics<br>Experiments"] D & E & F --> G["Computational Analysis:<br>Cross-country comparative<br>analysis & outcome modeling"] G --> H["Key Findings: 1) Financial literacy<br>correlates with better behavior<br>2) Demographic gaps persist<br>3) Education alone insufficient<br>4) Policy needs targeted, practical<br>approaches"]

Financial Literacy, Financial Education and Downstream Financial Behaviors (full paper and web appendix) ArXiv ID: ssrn-2333898 “View on arXiv” Authors: Unknown Abstract Policy makers have embraced financial education as a necessary antidote to the increasing complexity of consumers’ financial decisions over the last generation. Keywords: Financial Education, Consumer Finance, Behavioral Economics, Policy Intervention, Financial Literacy, Personal Finance / Policy Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper uses advanced statistical methods like meta-analysis and instrumental variables, but the mathematics is not dense or highly theoretical; it is data and implementation-heavy, focusing on large-scale empirical studies and backtesting policies. flowchart TD A["Research Goal: Does financial education<br>improve financial behaviors?"] A --> B["Methodology: Meta-Analysis &<br>Randomized Controlled Trials RCTs"] B --> C["Input: 20,000+ Obs from<br>198 Studies across 42 Countries"] C --> D["Computation: Impact Estimation<br>of Education vs. Control Groups"] D --> E{"Analysis by Outcome Category"} E --> F["Short-term: Knowledge<br>(Large Positive Effect)"] E --> G["Medium-term: Financial Outcomes<br>(e.g., Loan Terms, Small Effect)"] E --> H["Long-term: Asset Accumulation<br>(e.g., Retirement, Mixed/Null Effect)"]

The Psychology of Risk: The BehavioralFinancePerspective ArXiv ID: ssrn-1155822 “View on arXiv” Authors: Unknown Abstract Since the mid-1970s, hundreds of academic studies have been conducted in risk perception-oriented research within the social sciences (e.g., nonfinancial areas) Keywords: Risk Perception, Social Sciences, Behavioral Economics, Heuristics, Multi-Asset Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 0.5/10 Quadrant: Philosophers Why: The paper is a theoretical literature review that synthesizes existing behavioral finance concepts without introducing new mathematical models or conducting empirical backtests. flowchart TD A["Research Question<br>How do heuristics influence<br>risk perception in financial decisions?"] --> B["Methodology<br>Literature Review & Empirical Analysis"] B --> C["Data Inputs<br>Multi-Asset Market Data &<br>Social Science Risk Studies"] C --> D["Computational Process<br>Behavioral Modeling &<br>Heuristic Simulation"] D --> E["Key Findings<br>Cognitive biases distort risk<br>assessment across asset classes"] E --> F["Outcomes<br>Enhanced Behavioral Finance<br>Framework for Multi-Asset Investment"]

Financial Literacy: If it’s so Important, Why Isn’t it Improving? ArXiv ID: ssrn-923557 “View on arXiv” Authors: Unknown Abstract Financial literacy has assumed greater importance in our society as the result of the increasing complexity of financial products and the simultaneous cutting o Keywords: Financial Literacy, Consumer Protection, Financial Products, Behavioral Economics, Education, Multi-Asset / Personal Finance Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper’s focus is on survey data and socioeconomic analysis rather than advanced mathematical modeling or backtest-ready quantitative strategies. It lacks heavy formulas, code, or statistical implementations typical of high-rigor empirical studies. flowchart TD A["Research Question: Why isn't Financial Literacy improving despite its importance?"] --> B["Methodology: Literature Review & Empirical Analysis"] B --> C["Data Sources: National & International Surveys, Behavioral Economics Studies"] C --> D["Computational Process: Comparative Analysis of Literacy vs. Product Complexity"] D --> E{"Key Findings"} E --> F["Literacy scores remain stagnant"] E --> G["Product complexity outpaces education"] E --> H["Behavioral biases limit effectiveness"]

Behavioral Economics ArXiv ID: ssrn-245828 “View on arXiv” Authors: Unknown Abstract Behavioral Economics is the combination of psychology and economics that investigates what happens in markets in which some of the agents display human limitati Keywords: Behavioral Economics, Prospect Theory, Cognitive Biases, Heuristics, General (Economics) Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a theoretical and conceptual survey of behavioral economics, focusing on high-level ideas like bounded rationality and limits of arbitrage with minimal mathematical formalism or empirical data. It lacks backtests, datasets, or implementation details, positioning it as a philosophical/theoretical discussion rather than a quantitative trading strategy. flowchart TD A["Research Goal:<br>Understand deviations from<br>rational economic models"] --> B{"Methodology"} B --> C["Theoretical Modeling<br>e.g., Prospect Theory"] B --> D["Experimental Design<br>Labs & Field Studies"] C --> E["Data Inputs:<br>Psychological Heuristics &<br>Bias Observations"] D --> E E --> F["Computational Processes:<br>Agent-Based Simulation<br>& Probability Weighting"] F --> G{"Key Findings/Outcomes"} G --> H["Prospect Theory<br>Loss Aversion & Reference Dependence"] G --> I["Identified Cognitive Biases<br>e.g., Anchoring, Framing"] G --> J["Policy Implications<br>Nudges & Market Regulation"]

Behavioral Economics ArXiv ID: ssrn-245733 “View on arXiv” Authors: Unknown Abstract Behavioral Economics is the combination of psychology and economics that investigates what happens in markets in which some of the agents display human limitati Keywords: Behavioral Economics, Prospect Theory, Cognitive Biases, Heuristics, General (Economics) Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper involves established financial models and deterministic thresholds but lacks statistical backtesting or empirical datasets. The focus is on practical application and optimization within existing frameworks, fitting the Street Trader profile. flowchart TD A["Research Goal: Investigate market outcomes under human limitations"] --> B["Data: Experimental & field data on choices"] B --> C["Methodology: Prospect Theory & Cognitive Bias analysis"] C --> D["Computational Process: Heuristic decision modeling"] D --> E["Key Findings: Non-standard utility, systematic deviations, policy implications"]