The Financial Instability Hypothesis ArXiv ID: ssrn-161024 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: No abstract found, Unknown Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The excerpt presents a theoretical discussion on financial stability and market phases without heavy mathematical derivations, backtests, or implementation details. flowchart TD A["Research Goal: Explain Financial Instability"] --> B["Methodology: Theoretical Model<br>Probit Analysis"] B --> C["Data Inputs: Interest Rates<br>Debt Ratios<br>Market Volatility"] C --> D["Computational Process:<br>Simulate Debt Accumulation &<br>Asset Price Dynamics"] D --> E["Key Outcomes:<br>1. Debt-Deflation Dynamics<br>2. Systemic Risk Path<br>3. Market Fragility"]

Trading on Terror? ArXiv ID: ssrn-4652027 “View on arXiv” Authors: Unknown Abstract Recent scholarship shows that informed traders increasingly disguise trades in economically linked securities such as exchange-traded funds (ETFs). Linking that Keywords: Informed Trading, Market Microstructure, ETFs, Information Asymmetry, Arbitrage, Equities Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper relies on statistical event studies and rank-order analysis rather than advanced mathematical modeling, placing it at the lower end of math complexity; however, it employs high-quality financial data (FINRA, TASE, SEC) and robust empirical methods (placebo tests, counterfactuals, statistical significance thresholds) to analyze real-world trading patterns, warranting high empirical rigor. flowchart TD A["Research Goal: How do informed traders disguise<br>trading in securities linked to terror events?"] --> B["Method: Event Study &<br>Multi-Asset Analysis"] B --> C["Data: Global Terror Events &<br>Equity/ETF Transaction Data"] C --> D["Process: Identify Abnormal Trading<br>in Linked Securities vs. Equities"] D --> E["Analysis: Cross-Sectional Regressions<br>controlling for Arbitrage Constraints"] E --> F["Finding: Increased informed trading<br>in linked ETFs during terror events"] F --> G["Outcome: Displacement of<br>information asymmetry via market linking"]

Understanding Modern Portfolio Construction ArXiv ID: ssrn-2740027 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: Unknown Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper reviews historical finance theory (MPT, CAPM, Fama-French) with minimal advanced math, focusing on conceptual discussion rather than new derivations or models. It lacks any backtests, datasets, or implementation details, serving primarily as a theoretical critique and framework proposal without empirical validation. flowchart TD A["Research Goal:<br>Understand Modern<br>Portfolio Construction"] --> B["Methodology:<br>Review Empirical<br>Asset Pricing Literature"] B --> C["Data Input:<br>Historical Asset<br>Returns & Factors"] C --> D["Computational Process:<br>Estimate Risk &<br>Optimize Weights"] D --> E["Key Finding:<br>Traditional 60/40<br>Portfolio Underperforms"] E --> F["Outcome:<br>Recommend Dynamic<br>Risk Parity Approach"]

Understanding Risk and Return, the CAPM, and the Fama-French Three-Factor Model ArXiv ID: ssrn-481881 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: N/A, Insufficient Data, No Abstract Complexity vs Empirical Score Math Complexity: 6.5/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper introduces and derives the mathematical formulas for the CAPM and beta, involving covariance and variance calculations, which is moderately complex. However, it lacks backtest results, code, or heavy implementation details, relying primarily on conceptual explanation and historical data charts for illustration rather than rigorous empirical testing. flowchart TD A["Research Goal: Understand Risk & Return<br>Test CAPM vs. Fama-French Model"] --> B{"Data Collection & Preparation"} B --> C["CRSP & Compustat Datasets"] C --> D["Market, Size, Value Factors"] D --> E["Portfolio Formation<br>Size/BM Sorted Portfolios"] E --> F["Computational Analysis<br>Time-Series Regressions"] F --> G["Key Outcomes"] G --> H["CAPM Fails to Explain<br>Returns (Size & Value Effects)"] G --> I["Fama-French 3-Factor Model<br>Significantly Improves Fit"]

Valoración de Empresas por Descuento de Flujos: lo fundamental y las Complicaciones Innecesarias (Valuing Companies by Cash Flow Discounting: Fundamental Ideas and Unnecessary Complications) ArXiv ID: ssrn-2089397 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: No abstract available, Unknown Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.5/10 Quadrant: Philosophers Why: The paper focuses on explaining fundamental DCF concepts and criticizing unnecessary complications, using basic arithmetic and algebra rather than advanced mathematics. It is theoretical and educational, lacking any backtesting, datasets, or implementation details. flowchart TD A["Research Goal<br>Identify essential vs. unnecessary complexities<br>in DCF valuation"] --> B["Methodology<br>Theoretical analysis of DCF models"] B --> C["Data/Inputs<br>Mathematical formulas & market assumptions"] C --> D["Computational Process<br>Simulate valuation outcomes under varying complexities"] D --> E{"Key Findings<br>Simple models (FCFF) often match<br>complex ones (FCFE/Dividends) in efficiency"}<br>Complexity adds computation cost, not accuracy E --> F["Outcome<br>Recommendation: Avoid unnecessary<br>complexity in valuation models"]

Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data ArXiv ID: ssrn-2701093 “View on arXiv” Authors: Unknown Abstract The growth of subscription-based commerce has seen a change in the types of data firms report to external shareholders. More than ever before, companies are dis Keywords: Subscription Economics, Financial Reporting, Corporate Disclosure, Revenue Recognition Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 6.5/10 Quadrant: Street Traders Why: The paper employs moderate statistical modeling (e.g., retention, acquisition models) and valuation mathematics (DCF) but is heavily grounded in applying these models to real-world, publicly available data from companies like Dish Network and Sirius XM, focusing on practical implementation and backtesting against disclosed metrics. flowchart TD A["Research Goal: Value Subscription Businesses Using Public Customer Data"] --> B{"Key Methodology"} B --> C["Data: Public Disclosures<br/>Subscribers, Churn, ARPU, LTV"] C --> D["Computational Model<br/>Discounted Cash Flow DCF"] D --> E["Estimate Customer Lifetime Value<br/>Model Revenue & Churn Dynamics"] E --> F["Key Findings/Outcomes"] F --> G["1. Methodology improves valuation transparency"] F --> H["2. Public data can approximate internal metrics"] F --> I["3. Non-GAAP metrics are value relevant"]

Venture Capital and Private Equity: A Course Overview ArXiv ID: ssrn-79148 “View on arXiv” Authors: Unknown Abstract Over the past fifteen years, there has been a tremendous boom in the private equity industry. The pool of U.S. private equity funds (partnerships specializing i Keywords: Private Equity, Venture Capital, Leveraged Buyouts, Fund Performance, Private Equity Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a course overview that discusses concepts like agency theory and valuation methods (Monte Carlo, options) but presents them descriptively without advanced derivations or formulas. It lacks any code, backtesting, datasets, or statistical metrics, focusing instead on institutional knowledge and pedagogical structure. flowchart TD A["Research Goal<br>Analyze Private Equity & Venture Capital<br>Industry Growth & Fund Performance"] --> B["Methodology<br>Conceptual Analysis & Course Overview"] B --> C["Data & Inputs<br>15-Year Industry Trends<br>LBO & VC Fund Structures"] C --> D["Computational Process<br>Comparative Framework<br>Performance Evaluation"] D --> E["Key Findings & Outcomes<br>Industry Boom Identified<br>Strategic Course Structure Defined"]

We Don’t Quite Know What We are Talking About When We Talk About Volatility ArXiv ID: ssrn-970480 “View on arXiv” Authors: Unknown Abstract Finance professionals, who are regularly exposed to notions of volatility, seem to confuse mean absolute deviation with standard deviation, causing an underesti Keywords: Volatility, Risk Management, Standard Deviation, Statistical Analysis Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper relies on a conceptual mathematical argument about Jensen’s inequality and the relationship between standard deviation and mean absolute deviation, but the core math is relatively simple. Empirical rigor is high due to the conducted survey (87 participants across three professional groups) with presented statistical results (frequency histograms, error ratios) and clear data collection methodology. flowchart TD A["Research Question: Do finance professionals<br>understand volatility?"] --> B["Key Methodology: Survey<br>and statistical analysis"] B --> C["Data/Inputs: Responses from<br>finance professionals"] C --> D["Computation: Calculate and compare<br>Mean Absolute Deviation vs Standard Deviation"] D --> E["Key Findings/Outcomes:<br>Confusion between MAD and SD<br>Underestimation of volatility"]

What is BehavioralFinance? ArXiv ID: ssrn-256754 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: N/A, Insufficient Data, No Abstract Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 0.5/10 Quadrant: Philosophers Why: The excerpt is a descriptive overview of behavioral finance concepts with no mathematical formulas or advanced statistical analysis, and it presents no data, backtests, or implementation details. flowchart TD A["Research Goal: Define Behavioral Finance"] --> B["Key Methodology: Review Existing Literature"] B --> C{"Data/Input: Academic Papers & Theory"} C --> D["Computational Process: Conceptual Analysis & Synthesis"] D --> E["Key Outcome: Framework of Behavioral Biases & Market Anomalies"]

Demystifying the trend of the healthcare index: Is historical price a key driver? ArXiv ID: 2601.14062 “View on arXiv” Authors: Payel Sadhukhan, Samrat Gupta, Subhasis Ghosh, Tanujit Chakraborty Abstract Healthcare sector indices consolidate the economic health of pharmaceutical, biotechnology, and healthcare service firms. The short-term movements in these indices are closely intertwined with capital allocation decisions affecting research and development investment, drug availability, and long-term health outcomes. This research investigates whether historical open-high-low-close (OHLC) index data contain sufficient information for predicting the directional movement of the opening index on the subsequent trading day. The problem is formulated as a supervised classification task involving a one-step-ahead rolling window. A diverse feature set is constructed, comprising original prices, volatility-based technical indicators, and a novel class of nowcasting features derived from mutual OHLC ratios. The framework is evaluated on data from healthcare indices in the U.S. and Indian markets over a five-year period spanning multiple economic phases, including the COVID-19 pandemic. The results demonstrate robust predictive performance, with accuracy exceeding 0.8 and Matthews correlation coefficients above 0.6. Notably, the proposed nowcasting features have emerged as a key determinant of the market movement. We have employed the Shapley-based explainability paradigm to further elucidate the contribution of the features: outcomes reveal the dominant role of the nowcasting features, followed by a more moderate contribution of original prices. This research offers a societal utility: the proposed features and model for short-term forecasting of healthcare indices can reduce information asymmetry and support a more stable and equitable health economy. ...