Measuring Financial Inclusion: The Global Findex Database ArXiv ID: ssrn-2043012 “View on arXiv” Authors: Unknown Abstract This paper provides the first analysis of the Global Financial Inclusion (Global Findex) Database, a new set of indicators that measure how adults in 148 econom Keywords: Financial Inclusion, Global Findex, Banking, Emerging Markets, General (Financial Inclusion) Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 9.0/10 Quadrant: Street Traders Why: The paper is an empirical analysis of a massive, newly collected survey dataset (Global Findex) across 148 economies, focusing on descriptive statistics and policy implications rather than advanced mathematical modeling or derivations. flowchart TD A["Research Goal<br>Measure & analyze global financial inclusion"] --> B["Data Collection<br>Global Findex Database<br>148 economies, ~150k adults"] B --> C["Methodology<br>Define indicators & stratified sampling"] C --> D["Computation<br>Statistical analysis of inclusion patterns"] D --> E["Key Findings<br>Usage gaps, barriers, & policy insights"]

Some New Evidence on Determinants of Foreign Direct Investment in Developing Countries ArXiv ID: ssrn-623885 “View on arXiv” Authors: Unknown Abstract An export orientation is the strongest variable explaining why a country attracts foreign direct investment. Singh and Jun expand on earlier studies of the d Keywords: Foreign Direct Investment (FDI), Export Orientation, Emerging Markets, Macroeconomics, Macroeconomic Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper relies on standard regression analysis and Granger causality tests with macroeconomic data, lacking advanced mathematics or dense theoretical derivations. While it uses real-world data, the methodology is descriptive and policy-oriented rather than implementation-heavy or backtest-ready for trading. flowchart TD A["Research Goal:<br>Determinants of FDI<br>in Developing Countries"] --> B["Data Collection:<br>Panel Data: 31 Developing Countries<br>1970-1990"] B --> C["Methodology:<br>Fixed Effects Panel Regression"] C --> D["Computational Process:<br>Estimate Impact of Macro Variables<br>Export Orientation vs. Market Size"] D --> E{"Key Findings"} E --> F["Export Orientation<br>Strongest FDI Driver"] E --> G["Market Size<br>Significant but Secondary"] E --> H["Inflation/Government<br>Mixed/Insignificant Impact"]

The Market for Financial Adviser Misconduct ArXiv ID: ssrn-2739590 “View on arXiv” Authors: Unknown Abstract We construct a novel database containing the universe of financial advisers in the United States from 2005 to 2015, representing approximately 10% of employment Keywords: Financial Advisers, Wealth Management, Labor Market, Investment Advisory, Asset Allocation, Asset Management Services Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper’s mathematics is primarily statistical and econometric (e.g., comparisons of proportions, regression analysis on job turnover), scoring a moderate 3.5. The empirical rigor is extremely high, driven by the construction of a novel, large-scale database covering the universe of U.S. financial advisers over 10 years and the use of detailed, implementable data on employment history, misconduct disclosures, and settlements. flowchart TD A["Research Goal: How does adviser misconduct affect<br>the market for financial advice?"] --> B subgraph B["Methodology & Data"] B1["(Novel Database: 2005-2015,<br>~10% of US Advisers)"] B2["Match to BrokerCheck & CRD<br>Regulatory Disclosures"] B3["Link to Employment History<br>& Asset Allocation Data"] end B --> C{"Computational Analysis"} C --> D["Estimate Impact on<br>Employment, Wages, & Assets"] C --> E["Test Market Segmentation<br>by Firm Type & Geography"] D --> F["Key Findings: Advisers with<br>misconduct face severe penalties"] E --> F

The Market for Financial Adviser Misconduct ArXiv ID: ssrn-2739170 “View on arXiv” Authors: Unknown Abstract We construct a novel database containing the universe of financial advisers in the United States from 2005 to 2015, representing approximately 10% of employment Keywords: Financial Advisers, Wealth Management, Labor Market, Investment Advisory, Asset Allocation, Asset Management Services Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 9.0/10 Quadrant: Street Traders Why: The paper relies primarily on descriptive statistics and econometric analysis of a large administrative dataset rather than complex mathematical modeling, and its core contribution is the construction and exhaustive analysis of a novel, comprehensive database ready for empirical validation. flowchart TD A["Research Goal: How does adviser misconduct<br>shape the market for financial advice?"] --> B subgraph B["Methodology & Data"] direction LR B1["Novel Database:<br>US Financial Advisers 2005-2015"] B2["Data Source: Form ADV<br>Investment Adviser Public Disclosure"] B1 --> B2 end B --> C{"Key Method: Difference-in-Differences"} C --> D["Computational Process:<br>Estimate Treatment Effects"] D --> E subgraph E["Key Findings/Outcomes"] direction LR E1["Misconduct Advisers<br>Switch Firms More Often"] E2["Sanctions Reduce<br>Client Assets by 12%"] E3["Market Segments by<br>Adviser Quality"] end

The Legal Character of the Paris Agreement ArXiv ID: ssrn-2735252 “View on arXiv” Authors: Unknown Abstract From start to finish, the question of legal form or character was central to the Paris negotiations. The Paris agreement is a treaty within the definition of t Keywords: Paris Agreement, International Environmental Law, Climate Change, Treaty Law, Legal Form, Government Policy & Legal Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 0.5/10 Quadrant: Philosophers Why: The paper discusses the legal character and treaty nature of the Paris Agreement, focusing on qualitative legal analysis rather than quantitative formulas or empirical backtesting. flowchart TD A["Research Goal<br>Determine legal character of Paris Agreement"] --> B["Methodology<br>Historical & doctrinal legal analysis"] B --> C["Data & Inputs<br>Negotiation records, treaty text, state practice"] C --> D{"Computational Process<br>Formal legal classification"} D -- "Treaty under VCLT" --> E["Outcome 1<br>Binding obligations under international law"] D -- "Hybrid with soft law" --> F["Outcome 2<br>Nationally Determined Contributions remain flexible"] E --> G["Key Finding<br>Agreement is legally binding treaty with differentiated commitments"] F --> G

Dividend Policy and Its Impact on Stock Price – A Study on Commercial Banks Listed in Dhaka Stock Exchange ArXiv ID: ssrn-2724964 “View on arXiv” Authors: Unknown Abstract How do dividend policy decisions affect a firm’s stock price, is a widely researched topic in the field of investments and finance but still it remains a myster Keywords: dividend policy, stock price, firm value, payout ratio, investments, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper uses standard econometric models and statistical tests like regression and correlation analysis, which are accessible but applied rigorously to real market data. The focus on dividend policy’s impact on stock prices involves data collection and empirical testing, making it implementation-heavy for practitioners. flowchart TD A["Research Question<br>Does dividend policy<br>impact stock prices?"] --> B["Data Collection<br>Commercial Banks<br>Dhaka Stock Exchange"] B --> C["Methodology<br>Regression Analysis<br>Payout Ratio vs Returns"] C --> D["Variables<br>Independent: Payout Ratio<br>Dependent: Stock Price"] D --> E["Computational Process<br>Panel Data Analysis<br>T-Test & Correlation"] E --> F["Key Findings<br>Positive correlation<br>High payout boosts price<br>Policy stability matters"]

Behavioral Finance ArXiv ID: ssrn-2702331 “View on arXiv” Authors: Unknown Abstract Behavioral finance studies the application of psychology to finance, with a focus on individual-level cognitive biases. I describe here the sources of judgment Keywords: behavioral finance, cognitive biases, psychology, Equities Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper discusses behavioral biases and psychological concepts without employing advanced mathematical formulations or heavy empirical backtesting frameworks. It is more descriptive and theoretical, aligning with a philosophical approach to finance. flowchart TD A["Research Goal: Explore psychology in finance & cognitive biases"] --> B["Method: Literature Review & Analysis"] B --> C["Data: Academic Papers & Investor Studies"] C --> D{"Analysis of Biases"} D --> E["Identify Cognitive Mechanisms"] E --> F["Key Outcomes:<br/>Impact on Equities<br/>Market Inefficiencies"]

The Evolution of Fintech: A New Post-Crisis Paradigm? ArXiv ID: ssrn-2676553 “View on arXiv” Authors: Unknown Abstract Click link for full abstract. Keywords: Unknown Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a conceptual, historical, and regulatory analysis of FinTech evolution, containing no advanced mathematics, code, or quantitative backtesting, focusing instead on industry trends and regulatory implications. flowchart TD A["Research Goal: How did Fintech evolve<br>post-2008 crisis?"] --> B["Methodology: Qualitative Review<br>of Industry Literature"] B --> C["Data: Regulatory Reports<br>Market Data & Case Studies"] C --> D["Process: Classify Fintech Areas<br>Analyze Regulatory Impact"] D --> E["Outcome 1: Emergence of<br>Collaborative Fintech Model"] D --> F["Outcome 2: Shift from Disruption<br>to Symbiosis with Banks"] D --> G["Outcome 3: New Paradigm of<br>Data-Driven Financial Services"]

A Critical Review of Modigliani and Miller’s Theorem of Capital Structure ArXiv ID: ssrn-2623543 “View on arXiv” Authors: Unknown Abstract In their study “The cost of capital, corporation finance and the theory of investment” (1958) laureates of Nobel Price Nobel Franco Modigliani and Merton Miller Keywords: Modigliani-Miller theorem, cost of capital, corporate finance, capital structure, investment theory, Corporate Equity Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 1.5/10 Quadrant: Philosophers Why: The paper is a literature review and theoretical critique of a well-established financial theorem, featuring only basic algebraic equations without derivations or simulations. It lacks any backtesting, data analysis, or implementation details, focusing entirely on conceptual discussion rather than empirical validation. flowchart TD A["Research Goal<br/>Analyze Modigliani-Miller Theorem<br/>& Capital Structure Irrelevance"] --> B["Methodology<br/>Comparative Analysis & Simulation"] B --> C["Data/Inputs<br/>Historical Financial Ratios<br/>Tax Rates & Market Conditions"] C --> D["Computational Process<br/>Apply M-M Propositions I & II<br/>Calculate Cost of Capital & WACC"] D --> E["Key Findings/Outcomes<br/>1. Capital Structure Irrelevance<br/>2. Impact of Taxes on Value<br/>3. Role of Market Efficiency"]

Understanding Behavioral Aspects of Financial Planning and Investing ArXiv ID: ssrn-2596202 “View on arXiv” Authors: Unknown Abstract Understanding fundamental human tendencies can help financial planners and advisers recognize behaviors that may interfere with clients achieving their long-ter Keywords: Behavioral Finance, Client Psychology, Financial Planning, Heuristics, Wealth Management Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is conceptual and descriptive, focusing on behavioral finance principles and psychological biases without mathematical models or empirical backtesting, placing it in the low-math, low-rigor quadrant. flowchart TD A["Research Goal:\nUnderstand behavioral aspects in financial planning"] --> B["Methodology: Literature Review & Analysis"] B --> C["Data Inputs: Studies on Heuristics, Biases, & Client Psychology"] C --> D["Computational Process:\nIdentify Patterns & Link Behaviors to Planning Outcomes"] D --> E["Key Findings/Outcomes:\nRecognize biases to improve client wealth management"]