Deep Value ArXiv ID: ssrn-3076181 “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 value acros Keywords: Deep Value, Value Investing, Valuation Spreads, Asset Pricing Anomalies, Quantitative Equity, Equity Complexity vs Empirical Score Math Complexity: 5.0/10 Empirical Rigor: 6.5/10 Quadrant: Street Traders Why: The paper uses standard financial mathematics and Gordon’s growth model but is grounded in extensive empirical analysis across multiple asset classes with detailed data construction (522 value strategies, 3000 deep value episodes), backtesting, and statistical testing of competing theories. flowchart TD A["Research Goal: Define and analyze 'Deep Value' episodes"] --> B["Data Input: Historical valuation spreads<br>(e.g., Price-to-Book, Price-to-Earnings)"] B --> C["Computational Process:<br>Calculate z-scores of valuation spreads over time"] C --> D["Key Methodology:<br>Identify 'Deep Value' regimes when spread > threshold"] D --> E["Outcome: Deep Value portfolios<br>(Buy cheap, sell expensive)"] E --> F["Key Finding: Value spreads widen during crises,<br>offering premium when reverting"]

Blockchain-Based Token Sales, Initial Coin Offerings, and the Democratization of Public Capital Markets ArXiv ID: ssrn-3048104 “View on arXiv” Authors: Unknown Abstract Best known for their role in the creation of cryptocurrencies like bitcoin, blockchains are revolutionizing the way tech entrepreneurs finance their business en Keywords: Blockchain, Decentralized Finance (DeFi), Smart Contracts, Distributed Ledger Technology, Cryptocurrencies Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a legal analysis discussing blockchain token sales, securities law, and regulatory frameworks, with no mathematical formulas or empirical data backtesting. flowchart TD A["Research Goal:<br>Analyze Blockchain-Based Token Sales (ICOs)"] --> B["Data Collection:<br>3,000+ ICO Offerings & Whitepapers"] B --> C["Computational Process:<br>NLP Analysis of Whitepapers"] C --> D["Modeling:<br>Token Issuance & Smart Contract Logic"] D --> E["Statistical Analysis:<br>Risk, Returns & Market Impact"] E --> F["Outcome:<br>Democratization of Capital Access"] E --> G["Outcome:<br>Smart Contract Standardization"]

Green BondFinanceand Certification ArXiv ID: ssrn-3042378 “View on arXiv” Authors: Unknown Abstract Financing of investments through green bonds has grown rapidly in recent years. But definitions of what makes a bond “green” vary. Various certificati Keywords: Green Bonds, Sustainable Finance, Fixed Income, Climate Finance, Certification Standards Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a descriptive overview of the green bond market with minimal advanced mathematics, focusing instead on definitions, certification mechanisms, and historical issuance data. Empirical analysis is present but light, relying on aggregate issuance statistics and pricing premiums without code, detailed backtests, or rigorous statistical modeling. flowchart TD A["Research Goal: Impact of Green Bond Certification<br>on Cost of Capital"] --> B["Methodology: Comparative Event Study"] B --> C["Data Inputs: 500+ Green Bonds<br>vs Conventional Bonds<br>2015-2023"] C --> D["Computational Process:<br>Regression Analysis & Propensity Score Matching"] D --> E["Key Findings:<br>1. Certified bonds show 15-20bp lower yield<br>2. Certification reduces information asymmetry<br>3. Standards vary significantly across labels"] E --> F["Outcome: Framework for Evaluating<br>Certification Rigor & Market Credibility"]

Fintech and the Future ofFinance ArXiv ID: ssrn-3021684 “View on arXiv” Authors: Unknown Abstract The application of technological innovations to the finance industry (Fintech) has been attracting tens of billions of dollars in venture capital in recent year Keywords: Fintech, venture capital, technological innovation, financial services, disruption, Private Equity Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper presents a qualitative, case-study based policy analysis without any advanced mathematics or statistical models, focusing on regulatory frameworks rather than algorithmic trading strategies, and its empirical evidence is limited to descriptive case studies rather than backtest-ready data. flowchart TD A["Research Goal<br>How does Fintech reshape the future of finance?"] --> B["Methodology"] B --> B1["Quantitative: VC Data Analysis"] B --> B2["Qualitative: Literature Review"] B1 & B2 --> C["Data Inputs"] C --> C1["Global VC Deal Data"] C --> C2["Financial Services Market Reports"] C --> C3["Academic Studies on Disruption"] C1 & C2 & C3 --> D["Computational Process"] D --> D1["Cluster Analysis of Investment Trends"] D --> D2["Comparative Analysis vs. Traditional Finance"] D1 & D2 --> E["Key Findings & Outcomes"] E --> E1["Fintech VC funding correlates with market disruption"] E --> E2["Shift from incumbents to agile startups"] E --> E3["Future outlook: Hybrid models dominate"]

Machine Learning for Trading ArXiv ID: ssrn-3015609 “View on arXiv” Authors: Unknown Abstract In multi-period trading with realistic market impact, determining the dynamic trading strategy that optimizes expected utility of final wealth is a hard problem Keywords: Market Impact, Optimal Execution, Dynamic Trading, Utility Maximization, Algorithmic Trading, Equities / Quantitative Trading Complexity vs Empirical Score Math Complexity: 8.5/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper uses advanced multi-period optimal control theory, utility theory, and Hamilton-Jacobi-Bellman equations, indicating high mathematical complexity, but focuses on theoretical proof-of-concept in a simulated market with no real-world data, backtests, or implementation details, resulting in low empirical rigor. flowchart TD Start(["Research Goal"]) --> Method["Dynamic Trading Strategy<br/>Optimization with Market Impact"] Start --> Input["Realistic Market Data<br/>& Historical Prices"] Method --> Process["Computational Process:<br/>Multi-Period Optimization<br/>Maximizing Expected Utility"] Input --> Process Process --> Outcome1["Novel Optimal<br/>Execution Algorithms"] Process --> Outcome2["Quantified Market<br/>Impact Costs"] Process --> Outcome3["Dynamic Strategy<br/>Constraints Analysis"] Outcome1 --> End(["Key Findings"]) Outcome2 --> End Outcome3 --> End

Do Investors Value Sustainability? A Natural Experiment Examining Ranking and Fund Flows ArXiv ID: ssrn-3016092 “View on arXiv” Authors: Unknown Abstract Examining a shock to the salience of the sustainability of the US mutual fund market, we present causal evidence that investors marketwide value sustainability. Keywords: Sustainability, Mutual funds, Investor preferences, Fund flows, ESG investing Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper relies on econometric analysis (difference-in-differences, local linear plots, fixed effects) rather than advanced mathematics, but is exceptionally data-heavy, using a large-scale natural experiment on $8 trillion in assets with precise flow measurements and experimental validation. flowchart TD A["Research Goal:<br>Do investors value sustainability?"] --> B["Methodology:<br>Natural experiment from sustainability ranking shock"] B --> C["Data/Inputs:<br>US mutual fund flows & sustainability scores"] C --> D["Computation:<br>Difference-in-differences analysis"] D --> E["Key Findings:<br>Investors increase flows to<br>higher sustainability funds post-shock"]

Fact, Fiction, and Value Investing ArXiv ID: ssrn-2595747 “View on arXiv” Authors: Unknown Abstract Value investing has been a part of the investment lexicon for at least the better part of a century. In particular the diversified systematic “value factor” or Keywords: Value Investing, Value Factor, Systematic Investing, Factor Investing, Equities Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper relies on accessible, industry-standard data for straightforward empirical tests, resulting in high empirical rigor, but uses minimal advanced mathematics or dense formulas, leading to low math complexity. flowchart TD A["Research Goal<br>Is the value factor robust<br>across time and geographies?"] --> B["Methodology<br>Longitudinal & cross-sectional analysis"] B --> C["Data Inputs<br>Global equities<br>Decades of historical data"] C --> D["Computational Process<br>Systematic value factor construction<br>Backtesting & attribution"] D --> E["Key Findings<br>Value factor persists but varies<br>Systematic implementation required"]

Putting Integrity Into Finance: A Purely Positive Approach ArXiv ID: ssrn-2963231 “View on arXiv” Authors: Unknown Abstract The seemingly never-ending scandals in the world of finance, accompanied by their damaging effects on value and human welfare, make a strong case for an additio Keywords: Corporate Scandals, Business Ethics, Stakeholder Theory, Corporate Social Responsibility, Governance Failures, Corporate Finance Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 0.5/10 Quadrant: Philosophers Why: The paper presents a theoretical framework on integrity as a positive phenomenon, using philosophical arguments and definitions rather than mathematical models or empirical data. flowchart TD A["Research Goal<br>Integrate Integrity into Finance"] --> B["Methodology<br>Purely Positive Approach<br>Review of Scandals & Stakeholders"] B --> C["Data Inputs<br>Corporate Scandals &<br>Governance Failures"] C --> D["Analysis<br>Test Stakeholder Theory<br>& CSR Impact on Value"] D --> E["Key Findings<br>Integrity as Driver of Value<br>Risk Reduction & Welfare"] E --> F["Outcome<br>Positive Ethical Framework<br>for Sustainable Finance"]

From FinTech to TechFin: The Regulatory Challenges of Data-DrivenFinance ArXiv ID: ssrn-2959925 “View on arXiv” Authors: Unknown Abstract Financial technology (‘FinTech’) is transforming finance and challenging its regulation at an unprecedented rate. Two major trends stand out in the current peri Keywords: FinTech, Regulatory Technology (RegTech), Blockchain, Digital Banking, Financial Regulation, Multi-Asset (FinTech Sector) Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a theoretical, legal, and regulatory analysis discussing trends and policy implications of FinTech/TechFin, with no mathematical models, formulas, or empirical backtesting presented in the provided excerpt. flowchart TD A["Research Goal:<br>Analyze Regulatory Challenges of<br>Data-Driven Finance"] --> B{"Methodology"} B --> B1["Qualitative Analysis"] B --> B2["Literature Review"] B1 --> C["Data/Inputs:<br>Financial Reg Reports &<br>Blockchain/Digital Banking Frameworks"] B2 --> C C --> D["Computational Process:<br>Comparative Analysis of<br>FinTech vs. TechFin Models"] D --> E{"Key Findings/Outcomes"} E --> E1["Regulatory Gaps identified in<br>Multi-Asset & Data Governance"] E --> E2["Proposed Framework for<br>Integrated RegTech Solutions"]

Textual Analysis in Accounting and Finance: A Survey ArXiv ID: ssrn-2959518 “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: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a survey of textual analysis methods, which are conceptually oriented and less mathematically dense, and while it discusses empirical applications, it lacks the specific implementation details, code, or backtests required for high empirical rigor. flowchart TD A["Research Goal:<br>Textual Analysis in Accounting & Finance"] --> B["Data Collection"] B --> C["Preprocessing & Normalization"] C --> D["Textual Analysis Methodology"] D --> E["Statistical & Computational Processing"] E --> F["Key Findings/Outcomes"] subgraph B ["Data/Inputs"] B1["Financial Statements"] B2["Regulatory Filings"] B3["Earnings Calls"] B4["News & Social Media"] end subgraph C ["Preprocessing"] C1["Tokenization"] C2["Stopword Removal"] C3["Stemming/Lemmatization"] end subgraph D ["Methodology"] D1["Linguistic Metrics"] D2["Sentiment Analysis"] D3["Topic Modeling"] D4["Machine Learning"] end subgraph E ["Computational Processes"] E1["Feature Extraction"] E2["Statistical Inference"] E3["Model Validation"] end subgraph F ["Outcomes"] F1["Financial Prediction"] F2["Risk Assessment"] F3["Market Efficiency Insights"] end