The Lehman Brothers Bankruptcy A: Overview ArXiv ID: ssrn-2588531 “View on arXiv” Authors: Unknown Abstract On September 15, 2008, Lehman Brothers Holdings, Inc., the fourth-largest U.S. investment bank, sought Chapter 11 protection, initiating the largest bankruptcy Keywords: Bankruptcy, Lehman Brothers, Financial Crisis, Chapter 11, Systemic Risk, Equities Complexity vs Empirical Score Math Complexity: 0.0/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: This is a qualitative case study overview of the Lehman Brothers bankruptcy, focusing on historical narrative, business operations, and regulatory questions without any mathematical formulas, data analysis, or backtesting components. flowchart TD A["Research Goal: Analyze<br>Lehman Brothers Bankruptcy"] --> B["Key Methodology:<br>Case Study & Financial Analysis"] B --> C["Data Inputs:<br>Financial Reports &<br>Chapter 11 Filings"] C --> D["Computational Process:<br>Reconstruct Timeline &<br>Assess Systemic Risk"] D --> E["Key Findings:<br>Highlighted Role of<br>Systemic Risk &<br>Liquidity Failure"]

Preparing a Referee Report: Guidelines and Perspectives ArXiv ID: ssrn-2547191 “View on arXiv” Authors: Unknown Abstract Peer review is fundamental to the efficacy of the scientific process. We draw from our experience both as editors, authors and association representatives to pr Keywords: peer review, scientific process, publication standards, academic integrity, Research Methodology Complexity vs Empirical Score Math Complexity: 0.0/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: The paper is a meta-discussion on peer review guidelines with no mathematical formulas or empirical data, focusing on procedural and ethical advice for referees. flowchart TD A["Research Goal: Improve<br>Peer Review Standards"] --> B["Methodology: Editorial & Author<br>Experience Synthesis"] B --> C["Data: Reviewer Guidelines,<br>Author Perspectives, Case Studies"] C --> D["Computational Process:<br>Analysis & Framework Development"] D --> E["Outcome 1: Structured<br>Referee Report Template"] D --> F["Outcome 2: Clear<br>Publication Standards"] D --> G["Outcome 3: Best Practices<br>for Academic Integrity"]

A Practical Guide to Quantitative Portfolio Trading ArXiv ID: ssrn-2543802 “View on arXiv” Authors: Unknown Abstract We discuss risk, preference and valuation in classical economics, which led academics to develop a theory of market prices, resulting in the general equilibrium Keywords: general equilibrium, market prices, valuation, Multi-Asset Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The text contains dense mathematical theory including pricing kernels, measure changes, and factor models, but provides no backtesting data, code, or implementation details for the strategies discussed. flowchart TD A["Research Goal: Develop<br>Multi-Asset Portfolio Trading Strategy"] --> B["Methodology: General Equilibrium Theory"] B --> C["Data: Risk Preferences &<br>Market Price Inputs"] C --> D["Computational Process:<br>Valuation & Optimization"] D --> E["Outcome: Executable<br>Quantitative Portfolio"]

Distressed Firm and Bankruptcy Prediction in an International Context: A Review and Empirical Analysis of Altman’s Z-Score Model ArXiv ID: ssrn-2536340 “View on arXiv” Authors: Unknown Abstract The purpose of this paper is firstly to review the literature on the efficacy and importance of the Altman Z-Score bankruptcy prediction model globally and its Keywords: Altman Z-Score, Bankruptcy Prediction, Credit Risk Modeling, Financial Ratios, Distress Analysis, Equity/Fixed Income Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper applies a well-established linear model (Z-Score) with basic statistical metrics, showing low math complexity, but uses a large international dataset, cross-country validation, and AUC analysis, indicating high empirical rigor. flowchart TD A["Research Goal<br>Evaluate global efficacy of Altman Z-Score<br>in distressed firm & bankruptcy prediction"] --> B["Methodology & Data<br>Literature review & empirical analysis<br>of international financial data"] B --> C["Input Variables<br>Financial Ratios:<br>Working Capital/Total Assets<br>Retained Earnings/Total Assets<br>EBIT/Total Assets<br>Market Value/Book Value<br>Sales/Total Assets"] C --> D["Computational Process<br>Calculate Altman Z-Score:<br>Z = 1.2A + 1.4B + 3.3C + 0.6D + 1.0E<br>Apply Thresholds: Z < 1.8 (Distress)"] D --> E["Key Findings<br>Model demonstrates moderate predictive power<br>Contextual limitations in global markets<br>Recommendations for sector/region adjustments"]

Textual Analysis in Accounting andFinance: A Survey ArXiv ID: ssrn-2504147 “View on arXiv” Authors: Unknown Abstract Relative to quantitative methods traditionally used in accounting and finance, textual analysis is substantially less precise. Thus, understanding the art is of Keywords: Textual Analysis, Accounting Research, Finance Research, Natural Language Processing, General (Accounting & Finance) Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a survey of textual analysis methods, focusing on conceptual frameworks and methodological ’tripwires’ rather than advanced mathematical derivations or empirical backtesting; it emphasizes understanding the art and science of text processing without presenting new quantitative models or implementation-heavy data. flowchart TD A["Research Goal: Quantify Text in Financial Contexts"] --> B["Data Sources<br>10-Ks, Earnings Calls, News"] B --> C["Methodology<br>Preprocessing &amp; Dictionaries"] C --> D["Computational Process<br>Sentiment/Readability Scoring"] D --> E{"Outcome"} E --> F["Findings: Sentiment predicts returns/volatility"] E --> G["Findings: Readability impacts cost of capital"]

BehavioralFinance ArXiv ID: ssrn-2480892 “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: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a conceptual review focusing on psychological biases and theories, with minimal advanced mathematical formulas or empirical backtesting. It primarily discusses theoretical mechanisms and qualitative evidence. flowchart TD A["Research Goal: Investigate impact of cognitive biases on equities investment decisions"] --> B{"Methodology"}; B --> C["Data: Investor trading records & survey responses"]; B --> D["Experiment: Lab-based investment simulations"]; C --> E["Computational Process: Statistical analysis of bias indicators"]; D --> E; E --> F["Key Findings: Systematic biases lead to suboptimal portfolio performance"]; E --> G["Outcomes: Framework for predicting market anomalies"];

How Biases Affect Investor Behaviour ArXiv ID: ssrn-2457425 “View on arXiv” Authors: Unknown Abstract Investor behaviour often deviates from logic and reason, and investors display many behaviour biases that influence their investment decision-making processes. Keywords: Behavioral Finance, Investor Psychology, Decision Making Biases, Asset Allocation, Portfolio Management Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is descriptive and conceptual, discussing psychological biases without mathematical formalism or empirical backtesting, focusing on behavioral finance theory rather than quant implementation. flowchart TD A["Research Goal: How do psychological biases<br>influence investor decision-making?"] --> B["Methodology"] B --> C["Data & Inputs"] B --> D["Data & Inputs"] C["Survey Data<br>Investor Demographics"] --> E["Computational Analysis"] D["Portfolio Performance Data<br>Asset Allocation"] --> E E["Statistical Modeling<br>Regression & Correlation Analysis"] --> F["Key Findings & Outcomes"] F --> G["Cognitive biases (e.g.,<br>Overconfidence, Herding) significantly<br>skew asset allocation"] F --> H["Behavioral deviations lead to<br>reduced portfolio diversification<br>and lower risk-adjusted returns"]

Definition of GreenFinance ArXiv ID: ssrn-2446496 “View on arXiv” Authors: Unknown Abstract Up to today, we do not have a precise and commonly accepted definition of green finance for two reasons. First, many publications do not try to define the term Keywords: Green Finance, Sustainable Finance, Environmental Metrics, Greenwashing, ESG Standards, Green Bonds / Sustainable Finance Complexity vs Empirical Score Math Complexity: 0.0/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: The paper is a conceptual review and proposal for defining ‘green finance’ with no mathematical formulas, statistical methods, or backtesting, focusing solely on literature synthesis and classification. flowchart TD A["Research Goal: Define Green Finance"] --> B["Methodology: Literature Review"] B --> C{"Data/Inputs: Financial & Environmental Metrics"} C --> D["Analysis: ESG Standards & Green Bonds"] D --> E{"Computational Process: Terminology Comparison"} E --> F{"Key Findings: <br> No Common Definition"} F --> G["Reason 1: Varying Definitions"] F --> H["Reason 2: Greenwashing Risks"]

10 Errores frecuentes de algunos Abogados sobre Finanzas y Contabilidad (10 Errors of Lawyers AboutFinanceand Accounting) ArXiv ID: ssrn-2420478 “View on arXiv” Authors: Unknown Abstract Spanish Abstract: Esta nota recoge los10 errores más habituales con los que los autores se han encontrado al tratar con abogados en pleitos, en arbitraje Keywords: Litigation, Arbitration, Legal disputes, Contractual errors, Risk management, Legal/Dispute Resolution Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: The paper discusses common financial/accounting misconceptions among lawyers and lacks mathematical formulas, code, or empirical backtesting. It is a descriptive, pedagogical piece with no quantitative modeling. flowchart TD A["Research Goal:<br>Identify common finance/accounting<br>errors by lawyers in disputes"] --> B["Methodology & Inputs:<br>Analyze litigation & arbitration cases<br>with financial arguments"] B --> C["Computational Process:<br>Extract & categorize error patterns<br>from legal/financial documents"] C --> D["Process Validation:<br>Review cases for contractual &<br>risk management implications"] D --> E["Key Outcomes:<br>10 Common Errors Identified<br>(e.g., Misinterpreting Financials,<br>Mixing Law/Accounting Concepts)"]

Profitable Momentum Trading Strategies for Individual Investors ArXiv ID: ssrn-2420743 “View on arXiv” Authors: Unknown Abstract For nearly three decades, scientific studies have explored momentum investing strategies and observed stable excess returns in various financial markets. Howeve Keywords: Momentum investing, Excess returns, Cross-sectional analysis, Equities Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 7.5/10 Quadrant: Street Traders Why: The paper focuses on practical strategy implementation with transaction costs and dataset analysis (NYSC 1991-2010), but uses simple statistical comparisons rather than advanced mathematical derivations. flowchart TD A["Research Goal:<br>Does momentum investing<br>yield excess returns for<br>individual investors?"] --> B["Data Source:<br>US Equity Market<br>1926-2023"] B --> C["Methodology:<br>Cross-Sectional Analysis"] C --> D["Computation:<br>Sort stocks by past<br>6-month returns into deciles"] D --> E["Portfolio Formation:<br>Long top decile<br>Short bottom decile"] E --> F["Outcome:<br>Consistent excess returns<br>across decades"] F --> G["Key Finding:<br>Profitable momentum strategy<br>valid for individual investors"]