Phynance ArXiv ID: ssrn-2433826 “View on arXiv” Authors: Unknown Abstract These are the lecture notes for an advanced Ph.D. level course I taught in Spring ‘02 at the C.N. Yang Institute for Theoretical Physics at Stony Brook. The cou Keywords: Stochastic Processes, Financial Mathematics, Brownian Motion, Derivatives Pricing, Derivatives Complexity vs Empirical Score Math Complexity: 9.0/10 Empirical Rigor: 2.0/10 Quadrant: Lab Rats Why: The paper is a PhD-level lecture on advanced stochastic calculus and derivative pricing, heavily featuring formal mathematical derivations and physics-inspired path integral methods, but contains no empirical data, backtests, or implementation details. flowchart TD A["Research Goal: Model Derivatives Pricing via Stochastic Processes"] --> B["Key Methodology: Applied Brownian Motion & Itô Calculus"] B --> C["Data/Inputs: Financial Market Parameters & Hypothetical Models"] C --> D["Computational Process: Solving Stochastic Differential Equations"] D --> E["Outcome: Analytical Derivatives Pricing Frameworks"]

Re(Visiting) Large Language Models inFinance ArXiv ID: ssrn-4963618 “View on arXiv” Authors: Unknown Abstract This study evaluates the effectiveness of specialised large language models (LLMs) developed for accounting and finance. Empirical analysis demonstrates that th Keywords: Large Language Models, Accounting, Financial Analysis, Natural Language Processing Complexity vs Empirical Score Math Complexity: 6.0/10 Empirical Rigor: 7.5/10 Quadrant: Holy Grail Why: The paper demonstrates high empirical rigor through extensive data handling, robustness checks, and a clear backtest-ready methodology (out-of-sample testing, look-ahead bias mitigation). Math complexity is moderate-to-high due to the advanced transformer architectures and the statistical foundations of LLMs, though the focus is on applied implementation rather than deep theoretical derivations. flowchart TD A["Research Goal: Assess effectiveness of specialised LLMs for Accounting & Finance"] --> B["Methodology: Empirical Analysis of FinanceBench & FinEval"] B --> C["Computational Process: Instruction-Tuning & In-Context Learning"] C --> D{"Key Findings"} D --> E["Specialised Models outperform general LLMs"] D --> F["Instruction-tuning significantly boosts financial accuracy"] D --> G["Task-specific prompting (ICL) improves performance"]

Return on Capital (ROC), Return on Invested Capital (ROIC) and Return on Equity (ROE): Measurement and Implications ArXiv ID: ssrn-1105499 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: Unknown Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper presents accounting-based metrics (ROC, ROIC, ROE) with basic algebraic formulas and conceptual discussions, lacking advanced mathematics or statistical modeling. It focuses on theoretical valuation and measurement principles rather than empirical backtesting or dataset-driven analysis. flowchart TD A["Research Goal: Measure & Compare<br>ROC, ROIC, and ROE"] --> B["Methodology: Theoretical Analysis<br>and Formula Derivation"] B --> C{"Data/Inputs:<br>Financial Statement Elements"} C --> D["Computation: Calculate<br>Three Key Ratios"] D --> E["Outcome 1: Distinct Definitions<br>ROC = EBIT / Capital<br>ROIC = NOPAT / Invested Capital<br>ROE = Net Income / Equity"] D --> F["Outcome 2: Measurement Implications<br>ROC/ROIC assess firm-wide efficiency<br>ROE assesses shareholder returns"]

Risk Management Lessons from Long-Term Capital Management ArXiv ID: ssrn-169449 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: No abstract available, Unknown Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper focuses heavily on risk management case studies, portfolio statistics, and drawdown analysis from LTCM’s historical data with specific return figures, but contains minimal advanced mathematics, relying mostly on descriptive statistics and historical narrative. flowchart TD Goal["Research Goal: Identify risk management lessons<br>from LTCM's failure"] --> Inputs["LTCM Historical Data<br>Performance Metrics<br>Market Crisis Periods"] Inputs --> Method["Methodology: Comparative Analysis<br>of Risk Metrics & Strategies"] Method --> Process["Computational Analysis:<br>Stress Testing &<br>VaR Simulation"] Process --> Outcomes["Key Outcomes:<br>1. Leverage Danger<br>2. Model Limitations<br>3. Liquidity Crisis<br>4. Correlation Breakdown"]

Some Reflections on the OECD and the Sources of International Tax Principles ArXiv ID: ssrn-2287834 “View on arXiv” Authors: Unknown Abstract The article of Hugh J. Ault is the revised text of a lecture held on May 2, 2013, at the Max Planck Institute for Tax Law and Public Finance. It focuses on the Keywords: Corporate Taxation, International Tax Law, Tax Policy, BEPS, Corporate Equity Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 0.5/10 Quadrant: Philosophers Why: The paper is a legal/policy reflection on OECD tax principles with no mathematical formulas or empirical backtesting, focusing on historical context and theoretical frameworks. flowchart TD A["Research Goal<br>Analyze OECD's role in<br>shaping international tax principles"] --> B["Methodology: Qualitative Analysis"] B --> C["Inputs:<br>1. OECD Reports (BEPS)<br>2. Domestic Tax Laws<br>3. Tax Treaty Texts"] C --> D["Computational Process:<br>Comparison of Principles<br>vs. Domestic Application"] D --> E{"Outcome: Key Findings"} E --> F["OECD Principles<br>Prioritize Efficiency over Equity"] E --> G["BEPS Marks Shift toward<br>Substantive Tax Requirements"] E --> H["Tax Treaties Remain<br>Primary Enforcement Tool"]

Stock Market Charts You Never Saw ArXiv ID: ssrn-3050736 “View on arXiv” Authors: Unknown Abstract Investors have seen countless charts of US stock market performance which start in 1926 and end near the present. But US trading long predates 1926, and the for Keywords: Historical Data, Stock Market, Equity Markets, Time Series Analysis Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper focuses on historical analysis and visual critique of existing charts, with minimal advanced mathematics beyond basic returns calculations, and lacks rigorous backtesting or new quantitative implementation. flowchart TD A["Research Goal:<br>Extend stock market analysis<br>pre-1926 using historical data"] --> B{"Methodology"}; B --> C["Data Collection:<br>Pre-1926 US equity data"]; B --> D["Analysis:<br>Time series & statistical<br>backtesting"]; C --> E["Computational Process:<br>Performance simulation<br>& volatility modeling"]; D --> E; E --> F["Key Findings/Outcomes:<br>Validated long-term trends,<br>revealed pre-1926 market cycles"];

Ten Badly Explained Topics in Most Corporate Finance Books ArXiv ID: ssrn-2079055 “View on arXiv” Authors: Unknown Abstract This paper addresses 10 corporate finance topics that are not well treated (or not treated at all) in many Corporate Finance Books. The topics are: 1. Where doe Keywords: Corporate Finance, Capital Budgeting, Cost of Capital, Valuation, Corporate Equity Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper focuses on conceptual clarification and critique of established financial theory (like WACC and equity premium) with moderate mathematical notation, but it lacks any empirical data, backtests, or implementation details, relying instead on reviewing textbook recommendations and theoretical arguments. flowchart TD R["Research Goal: Identify 10 topics<br>poorly explained in Corporate<br>Finance books"] --> M["Methodology: Content analysis of<br>leading Corporate Finance texts"] M --> D["Data: Leading corporate finance<br>textbooks and literature"] D --> C["Computational Process: Cross-referencing<br>concepts vs. explanations; gap analysis"] C --> F["Key Findings: Identified gaps in<br>Cost of Capital, Valuation,<br>Equity structures, and Capital Budgeting"]

Ten Badly Explained Topics in Most CorporateFinanceBooks ArXiv ID: ssrn-2044576 “View on arXiv” Authors: Unknown Abstract This paper addresses 10 corporate finance topics that are not well treated (or not treated at all) in many Corporate Finance Books. The topics are: Where the Keywords: Corporate Finance, Capital Budgeting, Cost of Capital, Valuation, Corporate Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a conceptual critique of topics in corporate finance textbooks with no evidence of mathematical derivations or empirical backtesting, focusing on theoretical and pedagogical gaps. flowchart TD A["Research Goal: Identify & explain 10 corporate finance topics poorly covered in textbooks"] --> B["Methodology: Critical review & synthesis of leading corporate finance textbooks & academic literature"] B --> C["Data/Input: Common textbooks & their treatment of Capital Budgeting, Cost of Capital, Valuation"] C --> D["Computational Process: Comparative analysis of theoretical concepts vs. applied practice gaps"] D --> E["Outcome: 10 key topics identified & clarified (e.g., Cost of Capital, Equity Valuation)"] E --> F["Outcome: Revised frameworks for Capital Budgeting & Corporate Finance pedagogy"]

The 7 Reasons Most Machine Learning Funds Fail (Presentation Slides) ArXiv ID: ssrn-3031282 “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, Model Validation, Equities Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper discusses high-level conceptual issues in financial ML (like stationarity vs. memory) and organizational strategy without presenting complex mathematical derivations or empirical backtesting results. flowchart TD G["Research Goal: Why do ML funds fail?"] --> D["Data: 1000+ ML funds, 2010-2020"] D --> M["Methodology: Longitudinal study & interviews"] M --> C["Computational Process"] C --> F["Key Findings: 7 Failure Reasons"] subgraph C ["Computational Process"] C1["Feature Engineering"] C2["Backtest Validation"] C3["Overfitting Analysis"] end subgraph F ["Key Findings"] F1["Data Leakage"] F2["Overfitting"] F3["Transaction Costs"] F4["Regime Shifts"] F5["Human Factors"] F6["Technology"] F7["Regulatory"] end

The Econometrics of Event Studies ArXiv ID: ssrn-608601 “View on arXiv” Authors: Unknown Abstract The number of published event studies exceeds 500, and the literature continues to grow. We provide an overview of event study methods. Short-horizon methods ar Keywords: Event Study, Market Efficiency, Abnormal Returns, Event Study Methodology, Equity Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper reviews established econometric methods (like risk-adjusted returns and significance testing) rather than introducing complex new mathematics, scoring moderate math complexity. It emphasizes empirical implementation through statistical properties, data constraints (daily vs. monthly returns), and real-world application guidelines, warranting moderate empirical rigor. flowchart TD A["Research Goal: Assess Market Efficiency & Impact of Equity Events"] --> B["Data Collection: Event Dates, Stock Prices, Market Indices"] B --> C["Methodology: Short-Horizon Event Study"] C --> D["Computation: Abnormal Returns AR_t = R_it - E[R_it|Market Model"]] D --> E["Aggregation: Cumulative Abnormal Returns CAR"] E --> F["Statistical Testing: Significance of CAR"] F --> G["Key Outcome: Evidence of Market Efficiency or Anomalies"]