Introducción a las Finanzas Corporativas (Introduction to CorporateFinance) ArXiv ID: ssrn-2313264 “View on arXiv” Authors: Unknown Abstract Spanish Abstract: En esta monografía se describe qué son las finanzas corporativas: qué son, la ciencia de las finanzas y los modelos, el objetivo de la Keywords: Corporate Finance, Financial Science, Valuation Models, Capital Structure, Financial Management, Corporate Finance (Multi-Asset) Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper provides a conceptual introduction to corporate finance with minimal advanced mathematics and no empirical backtesting or data-driven implementation, focusing instead on theoretical frameworks and definitions. flowchart TD A["Research Goal:<br/>Define Corporate Finance"] --> B{"Methodology<br/>Theoretical Review"} B --> C["Key Inputs<br/>Valuation Models<br/>Capital Structure Theory"] C --> D["Computational Process:<br/>Financial Modeling"] D --> E{"Analysis of<br/>Financial Science"} E --> F["Key Outcomes:<br/>Management<br/>Multi-Asset<br/>Valuation"]

Principios de Finanzas (Principles ofFinance) ArXiv ID: ssrn-2313282 “View on arXiv” Authors: Unknown Abstract Spanish Abstract En esta monografía se describen doce principios que rigen las finanzas: el comportamiento financiero egoísta, las dos caras de la transa Keywords: Financial Principles, Selfish Financial Behavior, Financial Systems, Market Rules, Financial Monography, General Finance Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The text describes foundational principles of finance in a descriptive, conceptual manner without mathematical derivations, backtests, or data-driven implementation. flowchart TD A["Research Goal<br>Identify 12 Principles Governing Finance"] --> B["Methodology<br>Descriptive Literature Review"] B --> C["Data Input<br>Financial Monography & Market Rules"] C --> D["Computational Process<br>Analysis of Selfish Financial Behavior"] D --> E["Key Findings<br>The 12 Core Principles of Finance"]

The Trend is Our Friend: Risk Parity, Momentum and Trend Following in Global Asset Allocation ArXiv ID: ssrn-2275745 “View on arXiv” Authors: Unknown Abstract We examine the effectiveness of applying a trend following methodology to global asset allocation between equities, bonds, commodities and real estate. The appl Keywords: Trend Following, Global Asset Allocation, Multi-Asset Strategies, Time-Series Momentum, Portfolio Optimization, Multi-Asset Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 7.5/10 Quadrant: Street Traders Why: The paper employs relatively straightforward statistical analysis and portfolio construction rules (trend following, momentum, risk parity) rather than advanced mathematical theory, but it is heavily empirical with extensive backtesting across multiple asset classes, Sharpe ratios, and drawdown analysis over long historical periods. flowchart TD A["Research Goal<br/>Apply trend following to global multi-asset allocation<br/>(Equities, Bonds, Commodities, Real Estate)"] --> B["Data & Methodology"] B --> C["Compute Time-Series Momentum<br/>Signals for each asset"] C --> D["Portfolio Optimization<br/>Risk Parity weighting of signals"] D --> E["Backtesting & Validation"] E --> F["Key Findings & Outcomes"] F --> G["Out-of-sample: Trend-following <br/>enhances risk-adjusted returns"] F --> H["Strategies show <br/>strong diversification benefits"] F --> I["Performance persists across <br/>different market regimes"]

Crowdfunding und Crowdinvesting: State-of-the-Art der wissenschaftlichen Literatur (Crowdfunding and Crowdinvesting: A Review of the Literature) ArXiv ID: ssrn-2274141 “View on arXiv” Authors: Unknown Abstract German Abstract: Crowdfunding gewinnt in der Gründungs- und Innovationsfinanzierung an Bedeutung. Ein Überblick der wissenschaftlichen Arbeiten zu Crowdf Keywords: Crowdfunding, Start-up financing, Innovation finance, Private Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a literature review defining crowdfunding concepts and classifying models, with no mathematical formulas or advanced statistical methods presented. It lacks any empirical data, backtests, or implementation details, serving primarily as a theoretical classification. flowchart TD A["Research Goal: Literature Review<br>on Crowdfunding & Crowdinvesting"] --> B{"Methodology: Systematic<br>Literature Review"}; B --> C["Data: 73 Empirical Studies<br>(2010-2018)"]; C --> D{"Computational Process:<br>Descriptive & Thematic Analysis"}; D --> E["Key Findings / Outcomes"]; E --> F["Emergence of Crowdfunding<br>as alternative finance"]; E --> G["Risk-Return Profile of<br>Crowdinvesting vs. Traditional PE"]; E --> H["Identification of<br>Research Gaps"];

Dynamic Models and Structural Estimation in Corporate Finance ArXiv ID: ssrn-2268569 “View on arXiv” Authors: Unknown Abstract We review the last two decades of research in dynamic corporate finance, focusing on capital structure and the financing of investment. We first cover continuou Keywords: Dynamic Corporate Finance, Capital Structure, Investment Financing, Continuous Time Models, Stochastic Processes, Corporate Finance Complexity vs Empirical Score Math Complexity: 8.0/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper is a literature review focused on structural estimation and dynamic models, which inherently involves advanced mathematics and continuous-time frameworks, but it is a theoretical overview rather than a backtest-ready empirical study. flowchart TD A["Research Goal"] -->|Investigate dynamic models<br>in corporate finance| B["Methodology: Continuous-Time<br>Stochastic Processes"] B --> C["Data: Capital Structure<br>& Investment Data"] C --> D["Computational Process:<br>Structural Estimation"] D --> E{"Key Findings"} E --> F["Optimal Dynamic<br>Capital Structure"] E --> G["Financing Constraints<br>& Investment"]

The Trend is Our Friend: Risk Parity, Momentum and Trend Following in Global Asset Allocation ArXiv ID: ssrn-2265693 “View on arXiv” Authors: Unknown Abstract We examine the effectiveness of applying a trend following methodology to global asset allocation between equities, bonds, commodities and real estate. The appl Keywords: Trend Following, Global Asset Allocation, Multi-Asset Strategies, Time-Series Momentum, Portfolio Optimization, Multi-Asset Complexity vs Empirical Score Math Complexity: 5.0/10 Empirical Rigor: 8.5/10 Quadrant: Holy Grail Why: The paper applies advanced statistical and financial mathematics (e.g., risk parity, momentum models, volatility adjustments) but is heavily grounded in empirical backtesting across multiple asset classes with clear performance metrics, making it both mathematically sophisticated and data/implementation-focused. flowchart TD A["Research Goal: Test trend following in multi-asset allocation<br/>(Equities, Bonds, Commodities, Real Estate)"] --> B["Data & Inputs"] B --> B1["Historical Price Data"] B --> B2["4 Asset Classes"] B --> B3["Risk Parity & Trend Following Models"] A --> C["Methodology & Computation"] C --> C1["Estimate Covariance Matrix"] C --> C2["Apply Portfolio Optimization<br/>(Risk Parity / MV)"] C --> C3["Compute Time-Series Momentum<br/>(Rolling Returns & Signals)"] C --> D["Key Outcomes"] D --> D1["Robust Diversification Benefits"] D --> D2["Improved Risk-Adjusted Returns"] D --> D3["Effective Hedge Against Market Shocks"] D --> D4["Trend & Risk Parity Synergy"] B1 --> C B2 --> C B3 --> C C1 --> D C2 --> D C3 --> D

Incharge Financial Distress/Financial Well-Being Scale: Development, Administration, and Score Interpretation ArXiv ID: ssrn-2239338 “View on arXiv” Authors: Unknown Abstract This article describes development of the InCharge Financial Distress/Financial Well-Being Scale, designed to measure a latent construct representing responses Keywords: Financial Well-Being, Financial Distress, Scale Development, Personal Finance, Psychometrics, Personal Finance Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper focuses on developing and validating a psychometric scale (financial well-being/distress), which involves statistical methods like factor analysis and Cronbach’s alpha, but lacks advanced mathematical theory or derivations. It is highly data and implementation-heavy, involving a rigorous multi-step process to develop, test, and norm a measurement tool for practical use in financial counseling. flowchart TD A["Research Goal<br/>Develop & Validate Financial<br/>Distress/Well-Being Scale"] --> B["Data Collection<br/>Survey of 405 Adults"] B --> C["Methodology Steps<br/>Exploratory & Confirmatory<br/>Factor Analysis"] C --> D["Computational Processes<br/>Reliability Tests &<br/>Score Interpretation Algorithm"] D --> E["Key Outcomes<br/>Validated 8-Item Scale<br/>Distress/Well-Being Metric"]

What is the Optimal Method to Value a Football Club? ArXiv ID: ssrn-2238265 “View on arXiv” Authors: Unknown Abstract This paper introduces an original multivariate model developed to value English Premier League (EPL) clubs. Prior to developing the model, established valuation Keywords: Valuation Models, Multiple Regression, Intangible Assets, Cash Flow Modeling, Sports Economics, Alternative Assets Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.5/10 Quadrant: Philosophers Why: The paper compares established valuation techniques (DCF, multiples) with a new multivariate model for football clubs, but the excerpt shows only conceptual discussion and descriptive statistics, lacking advanced mathematical derivations or backtest-ready implementation details. flowchart TD A["Research Goal:<br>Determine Optimal Football Club Valuation Method"] --> B["Key Methodology<br>Multivariate Model Development"] B --> C["Data Inputs<br>EPL Clubs & Financials"] C --> D{"Computational Process:<br>Multiple Regression"} D --> E["Key Findings<br>Original Multivariate Model"] D --> F["Comparative Analysis<br>vs. Cash Flow Models"] --> E

Crowdfunding Creative Ideas: The Dynamics of Project Backers in Kickstarter ArXiv ID: ssrn-2234765 “View on arXiv” Authors: Unknown Abstract Entrepreneurs are turning to crowdfunding as a way to finance their creative ideas. Crowdfunding involves relatively small contributions of many consumer-inves Keywords: crowdfunding, consumer-investors, entrepreneurial finance, alternative financing, venture capital, Private Equity / Alternative Investments Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses two years of Kickstarter panel data with fixed effects models to analyze backer dynamics over time, demonstrating strong empirical data usage and implementation. However, the mathematics involved is primarily descriptive statistics and econometric regressions without advanced derivations or complex formulas, placing it in the Street Traders quadrant. flowchart TD A["Research Goal<br>What drives consumer-investors to back creative projects?"] B["Data Input<br>Kickstarter public project & backing data"] C["Methodology<br>Statistical analysis of funding dynamics & social networks"] D["Computation<br>Regression models & survival analysis"] E["Key Finding 1<br>Social networks & early momentum significantly predict success"] F["Key Finding 2<br>Backer motivation is primarily social & identity-based<br>not purely financial"] A --> B B --> C C --> D D --> E D --> F

Bagehot was a Shadow Banker: Shadow Banking, Central Banking, and the Future of GlobalFinance ArXiv ID: ssrn-2232016 “View on arXiv” Authors: Unknown Abstract At the heart of both the modern shadow banking system and the 19th century banking system described by Walter Bagehot is the wholesale money market, with the ce Keywords: shadow banking, wholesale money market, liquidity, banking history, Money Markets Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a conceptual, historical, and institutional analysis comparing 19th-century banking to modern shadow banking, with no advanced mathematical models or empirical backtesting presented in the provided excerpt. flowchart TD A["Research Goal: Compare 19th C Bagehot banking<br>to modern shadow banking"] --> B["Methodology: Historical & Institutional Analysis<br>of wholesale money markets"] B --> C["Data/Inputs: Bagehot's "Lombard Street"<br>+ Modern Financial Data"] C --> D["Computational Process: Cross-Era Analysis<br>Mapping mechanisms & stability roles"] D --> E{"Key Findings/Outcomes"} E --> F1["1: Wholesale money markets<br>are the structural core"] E --> F2["2: Shadow banking replicates<br>19th C. banking functions"] E --> F3["3: Central banking role remains<br>crucial for liquidity"]