Detection of False Investment Strategies Using Unsupervised Learning Methods ArXiv ID: ssrn-3167017 “View on arXiv” Authors: Unknown Abstract Most investment strategies uncovered by practitioners and academics are false. This partially explains the high rate of failure, especially among quantitative h Keywords: quantitative finance, investment strategies, backtesting bias, market efficiency, quantitative strategies Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 2.0/10 Quadrant: Lab Rats Why: The paper introduces a complex unsupervised learning algorithm involving probability distributions and multiple testing corrections, but lacks specific implementation details, code, or detailed backtesting results, focusing more on theoretical and statistical methodology. flowchart TD A["Research Goal:<br>Detect false quantitative investment strategies"] --> B["Methodology:<br>Unsupervised Learning (e.g., Clustering)"] B --> C["Data Inputs:<br>Strategy Returns, Factor Loadings, Backtest Metrics"] C --> D["Computational Process:<br>Identify Outliers & Anomalies in Strategy Space"] D --> E["Key Findings:<br>Strategies are often noise; high failure rate due to backtesting bias"]

Initial Coin Offerings ArXiv ID: ssrn-3166709 “View on arXiv” Authors: Unknown Abstract This paper examines the market for initial coin offerings (ICOs). ICOs are smart contracts based on blockchain technology that are designed for entrepreneurs to Keywords: Initial Coin Offerings (ICOs), Smart Contracts, Blockchain, Cryptocurrency, Entrepreneurial Finance, Cryptocurrency/Blockchain Assets Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper appears to be an empirical study of a new financial market (ICOs) using observational data, which typically involves descriptive statistics, regression analysis, and event studies rather than advanced mathematical derivations. While it uses real-world data, the focus is on market analysis and implications rather than backtest-ready algorithmic trading code or rigorous performance metrics. flowchart TD A["Research Goal<br>Examine the ICO Market<br>via Blockchain Smart Contracts"] --> B["Methodology: Data Collection<br>Token Attributes, Issuer Info, Market Data"] B --> C["Methodology: Market Analysis<br>Price, Liquidity, Returns"] C --> D["Computational Process<br>Statistical Analysis of<br>Token Economics & Issuance"] D --> E{"Key Findings & Outcomes"} E --> F["ICOs as Efficient<br>Entrepreneurial Finance Tools"] E --> G["Token Price Determinants<br>Identified"] E --> H["Blockchain Transparency<br>Enhances Market Trust"]

Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2018 Edition ArXiv ID: ssrn-3140837 “View on arXiv” Authors: Unknown Abstract The equity risk premium is the price of risk in equity markets and is a key input in estimating costs of equity and capital in both corporate finance and valuat Keywords: Equity Risk Premium (ERP), Cost of Equity, Capital Budgeting, Valuation Models, Equities Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 4.0/10 Quadrant: Philosophers Why: The paper focuses on conceptual discussions and determinants of the equity risk premium with limited advanced mathematical derivations, and while it uses historical and implied data, it does not present a systematic backtesting framework with implementation details. flowchart TD A["Research Goal: Determine &<br>Estimate Equity Risk Premium"] --> B["Key Methodology<br>Historical & Fundamental Analysis"] B --> C["Data & Inputs<br>Dividend Yields, Earnings Growth<br>Bond Yields, Inflation"] C --> D["Computational Process<br>Build DCF Models &<br>Deconstruct ERP Components"] D --> E{"Key Findings & Outcomes"} E --> F["ERP is Dynamic<br>Highly Sensitive to<br>Macro Conditions"] E --> G["CRP: Consumer Risk<br>Premium is a Key<br>Determinant"] E --> H["Valuation Outcome<br>Cost of Equity Capital<br>Estimates for Investment"]

Pulling the Goalie: Hockey and Investment Implications ArXiv ID: ssrn-3132563 “View on arXiv” Authors: Unknown Abstract Click link for full abstract. Keywords: Unknown Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 1.5/10 Quadrant: Philosophers Why: The paper uses discrete-time recursion and basic probability for a simplified model, but it is fundamentally a conceptual discussion on decision-making, with no backtesting or complex statistical methods. flowchart TD A["Research Goal:<br>Do optimal abandonment<br>strategies apply to finance?"] --> B{"Methodology"} B --> C["Data: NHL Pull-Goalie Events"] B --> D["Input: Market Options Data"] C & D --> E["Computation:<br>Real Options Analysis"] E --> F["Outcome:<br>Market analogies identified"] F --> G["Findings:<br>Validates strategic<br>abandonment logic"]

Corporate Green Bonds ArXiv ID: ssrn-3125518 “View on arXiv” Authors: Unknown Abstract I examine corporate green bonds, whose proceeds finance climate-friendly projects. These bonds have become more prevalent over time, especially in industries wh Keywords: Green Bonds, Sustainable Finance, Climate Finance, Bond Issuance, ESG Metrics, Fixed Income (Corporate Bonds) Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper uses standard econometric methods (event studies, matching) rather than advanced mathematics, but is heavily data-driven with a comprehensive dataset from Bloomberg and rigorous empirical analysis of market reactions and firm performance. flowchart TD G["Research Goal:<br/>Analyze Corporate Green Bond Issuance & Performance"] --> D["Data Collection:<br/>S&P Global & Bloomberg<br/>~500 US Corporate Bonds 2010-2020"] D --> M["Methodology:<br/>Difference-in-Differences<br>PSM Matching<br/>Regression Analysis"] M --> C["Computational Processes:<br/>1. Yield Spread Estimation<br/>2. ESG Impact Modeling<br/>3. Certification Analysis"] C --> F["Key Findings:<br/>1. Certified Green Bonds<br/> have 20-25 bps lower yields<br/>2. ESG factors drive issuance<br/>3. Liquidity premium varies<br/>4. No 'Greenium' for non-certified"]

Deep Value ArXiv ID: ssrn-3122327 “View on arXiv” Authors: Unknown Abstract We define “deep value” as episodes where the valuation spread between cheap and expensive securities is wide relative to its history. Examining deep v Keywords: Deep Value, Value Investing, Valuation Spreads, Asset Pricing Anomalies, Quantitative Equity, Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses straightforward descriptive statistics and historical analysis of valuation spreads, with minimal advanced mathematics, but appears heavily reliant on real market data and backtesting scenarios for its conclusions. flowchart TD A["Research Goal: Identify & model "Deep Value" episodes<br>widest valuation spreads relative to history"] --> B["Data & Inputs"] B --> B1["Panel of US Stocks"] B --> B2["Valuation Metrics<br>e.g., B/M, E/P"] B --> B3["Historical Time Series<br>for spread distribution"] B --> B4["Market Cap & Returns"] B --> C["Key Methodology: Deep Value Definition"] C --> C1["Compute cross-sectional valuation spread<br>e.g., Value - Growth spread"] C --> C2["Define Deep Value episodes<br>periods where spread > 90th percentile of history"] C --> D["Computational Process: Portfolio Construction"] D --> D1["Sort stocks into Value quantiles"] D --> D2["Go long Cheapest (Deep Value) decile<br>Short Expensive decile"] D --> D3["Calculate factor returns & alphas<br>controlling for momentum/quality"] D --> E["Key Findings & Outcomes"] E --> E1["Deep Value spreads are cyclical & persistent<br>predicting long-term returns"] E --> E2["Value factor returns significantly higher<br>during Deep Value episodes"] E --> E3["Returns decay over short horizons<br>but rebound over 3-5 years"] E --> E4["Out-of-sample performance robust<br>across regions and time"]

Decoding Alipay: Mobile Payments, a Cashless Society and Regulatory Challenges ArXiv ID: ssrn-3103751 “View on arXiv” Authors: Unknown Abstract The financial industry has witnessed the so-called “fintech revolution” in recent years. Due to the emergence of information technologies such as cloud computin Keywords: Fintech, Blockchain, Digital Payments, Regulatory Technology (RegTech), Financial Services Complexity vs Empirical Score Math Complexity: 0.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is descriptive and legal/policy-oriented with no mathematical modeling, empirical formulas, or backtesting data, focusing instead on industry analysis and regulatory commentary. flowchart TD A["Research Goal: <br>How does Alipay drive <br>a cashless society?"] --> B{"Methodology"} B --> C["Data Collection"] B --> D["Regulatory Analysis"] C --> E["Computation: <br>Market Adoption & Usage"] D --> E E --> F["Key Findings"] F --> G["FinTech Innovation"] F --> H["Regulatory Challenges"] F --> I["Future of Cashless Society"] subgraph Inputs C D end subgraph Outcomes G H I end

Advances in Financial Machine Learning (Chapter 1) ArXiv ID: ssrn-3104847 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: machine learning, deep learning, algorithmic trading, predictive modeling, Financial Technology Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The excerpt focuses on practical implementation and real-world data challenges in finance with an empirical approach, but does not present dense mathematical derivations or advanced formulas. flowchart TD A["Research Goal:<br>Application of ML in Finance"] --> B["Key Methodology:<br>Algorithmic Trading &<br>Predictive Modeling"] B --> C["Computational Process:<br>Deep Learning &<br>ML Algorithms"] C --> D["Data Input:<br>Financial Market Data"] D --> C C --> E["Key Findings:<br>ML replacing expert human tasks<br>in FinTech & Finance"]

The 10 Reasons Most Machine Learning Funds Fail ArXiv ID: ssrn-3104816 “View on arXiv” Authors: Unknown Abstract The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of ass Keywords: Financial Machine Learning, Quantitative Finance, Asset Management, Predictive Analytics, Trading Strategy, Quantitative Finance / Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.5/10 Quadrant: Philosophers Why: The paper focuses on high-level methodological pitfalls and organizational paradigms in financial machine learning, with minimal advanced mathematical formalism. It lacks empirical backtests, statistical code, or implementation-heavy data analysis, making it more of a conceptual framework than a backtest-ready study. flowchart TD Q["Research Question:<br>Why do ML funds fail?"] --> D["Data: Financial ML<br>papers & strategies"] D --> M["Methodology: Cross-sectional<br>analysis of failures"] M --> C["Computational Process:<br>Identify recurring pitfalls"] C --> F["Findings: 10 systemic reasons<br>e.g., overfitting, data snooping"] F --> O["Outcome: Risk management<br>framework for ML funds"]

Metcalfe’s Law as a Model for Bitcoin’s Value ArXiv ID: ssrn-3078248 “View on arXiv” Authors: Unknown Abstract This paper demonstrates that bitcoin’s medium- to long-term price follows Metcalfe’s law. Bitcoin is modeled as a token digital currency, a medium of exchange w Keywords: Metcalfe’s Law, Bitcoin, Network Effects, Cryptocurrency Valuation, Cryptocurrency Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper uses high-level concepts like Metcalfe’s law (n^2) and Gompertz curves but presents them without heavy derivations or complex mathematics. Empirical work is discussed (price fits, manipulation investigation) but the excerpt lacks detailed backtesting methodology, code, or robust statistical metrics. flowchart TD A["Research Goal: Model Bitcoin's value via Metcalfe's Law"] --> B["Methodology: Time-series Regression Analysis"] B --> C["Data Input: Historical Bitcoin Price & Active Addresses"] C --> D["Computational Process: Log-linear Regression of Price vs Network Value"] D --> E{"Key Findings/Outcomes"} E --> F["Strong correlation confirms Metcalfe's Law applies"] E --> G["Price follows power law: P ~ n²"] E --> H["Valuation tool for long-term trends"]