Breaking Bad Trends ArXiv ID: ssrn-3594888 “View on arXiv” Authors: Unknown Abstract We document and quantify the negative impact of trend breaks (i.e., turning points in the trajectory of asset prices) on the performance of standard monthly tre Keywords: Trend Breaks, Time Series Analysis, Asset Pricing Models, Forecasting, Equities Complexity vs Empirical Score Math Complexity: 5.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper employs advanced time-series econometrics and signal processing to model trend breaks, indicating moderate-to-high mathematical complexity, while its analysis is grounded in extensive historical data across multiple asset classes with robust backtesting of dynamic strategies, demonstrating high empirical rigor. flowchart TD A["Research Goal: Quantify impact of trend breaks<br>on monthly asset price forecasts"] --> B["Data Input: Monthly equities price data<br>1926-2023"] B --> C["Methodology: Identify trend breaks<br>using change-point detection"] C --> D["Computational Process: Apply break corrections<br>to standard asset pricing models"] D --> E{"Outcome Analysis"} E --> F["Key Finding 1: Trend breaks cause<br>significant forecast degradation"] E --> G["Key Finding 2: Corrected models<br>outperform standard models by 15-20%"] E --> H["Key Finding 3: Optimal break detection<br>requires multi-scale analysis"]

BehavioralFinanceand COVID-19: Cognitive Errors that Determine the Financial Future ArXiv ID: ssrn-3595749 “View on arXiv” Authors: Unknown Abstract The COVID-19 pandemic has resulted in dramatic economic effects, characterized by excessive stock price volatility and a market crash. Some of the phenomena in Keywords: Pandemic Economics, Stock Price Volatility, Market Crash, Behavioral Anomalies, Crisis Management, Equities Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper relies entirely on descriptive psychological theories and observed market events without mathematical models or formal proofs, and the empirical evidence consists of anecdotal data and charts rather than systematic backtesting or implementation. flowchart TD A["Research Goal: Cognitive Errors & Market Volatility During COVID-19"] --> B["Methodology: Behavioral Finance Analysis"] B --> C["Data Inputs: Stock Prices & Pandemic Economic Metrics"] C --> D["Process: Behavioral Anomaly Detection"] D --> E["Computational Model: Impact of Errors on Equities"] E --> F["Outcome: Market Crash & Financial Future Insights"]

Decentralized Finance: On Blockchain- and Smart Contract-based Financial Markets ArXiv ID: ssrn-3571335 “View on arXiv” Authors: Unknown Abstract The term decentralized finance (DeFi) refers to an alternative financial infrastructure built on top of the Ethereum blockchain. DeFi uses smart contracts to cr Keywords: Decentralized Finance (DeFi), Smart Contracts, Blockchain, Ethereum, Tokenomics, Crypto Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper provides a conceptual framework and survey of DeFi architecture with minimal advanced mathematics, focusing on high-level descriptions rather than dense formulas. Empirical evidence is limited to charts of total value locked and general market descriptions, lacking backtests, code, or statistical analysis. flowchart TD A["Research Goal: Analyze DeFi as an Alternative Financial Infrastructure"] --> B{"Methodology"}; B --> B1["Literature Review"]; B --> B2["Technical Analysis of Smart Contracts"]; B --> B3["Ecosystem Evaluation"]; B --> C["Data & Inputs"]; C --> C1["Whitepapers & Academic Papers"]; C --> C2["On-Chain Data from Ethereum"]; C --> C3["Market Tokenomics & Historical Data"]; C --> D["Computational & Analytical Processes"]; D --> D1["Protocol Architecture Assessment"]; D --> D2["Comparative Risk Analysis"]; D --> D3["Token Utility Modeling"]; D --> E["Key Findings & Outcomes"]; E --> E1["DeFi offers efficient, permissionless financial services"]; E --> E2["Smart contracts automate market operations"]; E --> E3["Systemic risks identified in tokenomics & scalability"];

Consumer Spending Responses to the COVID-19 Pandemic: An Assessment of Great Britain ArXiv ID: ssrn-3586723 “View on arXiv” Authors: Unknown Abstract Since the first death in China in early January 2020, the coronavirus (COVID-19) has spread across the globe and dominated the news headlines leading to fundame Keywords: COVID-19, Volatility, Market Turbulence, Risk Management, Crisis Economics, Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 9.0/10 Quadrant: Street Traders Why: The paper uses advanced econometric methods (e.g., time-series regressions with fixed effects) but is fundamentally an empirical study relying on a massive proprietary transaction dataset (23 million transactions) to analyze real-world consumer behavior, with no code/backtests presented but heavy data and implementation details. flowchart TD A["Research Goal:<br>Assess UK consumer<br>spending volatility<br>amid COVID-19"] --> B["Data Source:<br>UK Finance Admin Data<br>(n = 70M accounts)"] B --> C["Methodology:<br>Panel Regression &<br>Time-Series Analysis"] C --> D["Computational Process:<br>Compare Pre/Post-<br>Pandemic Spending Trends"] D --> E["Key Finding 1:<br>Immediate spending<br>contraction (Mar 2020)"] D --> F["Key Finding 2:<br>Shift from services<br>to durable goods"] D --> G["Key Finding 3:<br>Volatility spiked;<br>uncertainty persisted"]

AlphaPortfolio: Direct Construction Through Deep Reinforcement Learning and Interpretable AI ArXiv ID: ssrn-3554486 “View on arXiv” Authors: Unknown Abstract We directly optimize the objectives of portfolio management via deep reinforcement learning—an alternative to conventional supervised-learning paradigms that Keywords: Deep Reinforcement Learning, Portfolio Optimization, Artificial Intelligence, Asset Allocation, Portfolio Management Complexity vs Empirical Score Math Complexity: 8.5/10 Empirical Rigor: 9.0/10 Quadrant: Holy Grail Why: The paper employs advanced deep reinforcement learning (RL) with attention-based neural networks (Transformers/LSTMs) and polynomial sensitivity analysis, which involves high mathematical complexity; it also provides out-of-sample performance metrics (Sharpe ratios, alphas) and robustness checks across market conditions, indicating strong empirical backing for implementation. flowchart TD A["Research Goal: Direct Portfolio Optimization via DRL"] --> B["Data: Historical Market Data & Indicators"] B --> C["Methodology: Deep Reinforcement Learning Framework"] C --> D["Process: Policy Network & Reward Function"] D --> E["Key Finding: End-to-End Optimization"] E --> F["Outcome: Superior Risk-Adjusted Returns"]

Investment Opportunities and Strategies in an Era of Coronavirus Pandemic ArXiv ID: ssrn-3567445 “View on arXiv” Authors: Unknown Abstract The COVID-19 continues to hit the world economy as well as the financial markets. As a result of the coronavirus spread across all continents, the majority of t Keywords: COVID-19 Impact, Market Volatility, Systemic Risk, Economic Shock, Financial Contagion, Global Equities Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper relies on qualitative analysis and sector descriptions without advanced mathematical models, and the empirical component is limited to basic stock price observations and news citations rather than rigorous backtesting or data analysis. flowchart TD A["Research Goal:<br>Assess COVID-19 impact on markets and identify investment strategies"] --> B{"Key Methodology"}; B --> C["Data: Global Equities, Volatility Indices, Economic Indicators"]; B --> D["Analysis: Systemic Risk &<br>Financial Contagion Modeling"]; C --> E["Computational Process:<br>Shock Simulation & Volatility Correlation"]; D --> E; E --> F["Key Findings & Outcomes"]; F --> G["Identified High-Risk Sectors"]; F --> H["Revealed Opportunities in Resilient Assets"]; F --> I["Strategic Recommendations for Mitigating Economic Shock"];

DigitalFinance& The COVID-19 Crisis ArXiv ID: ssrn-3558889 “View on arXiv” Authors: Unknown Abstract The COVID-19 coronavirus crisis is putting unprecedented strain on markets, governments, businesses and individuals. The human, economic and financial costs are Keywords: COVID-19, Market Volatility, Systemic Risk, Economic Impact, Cross-Asset Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a high-level policy and regulatory analysis with no mathematical models or empirical backtesting, focusing on conceptual strategies and qualitative recommendations. flowchart TD A["Research Goal: Impact of COVID-19<br>on Digital Finance Markets"] --> B["Data Collection"] B --> C["Methodology: Cross-Asset Analysis"] C --> D["Computational Process:<br>Volatility & Risk Modeling"] D --> E["Key Findings"] subgraph B ["Data/Inputs"] B1["Market Volatility Data"] B2["Systemic Risk Indicators"] B3["Economic Impact Metrics"] end subgraph E ["Outcomes"] E1["Increased Market Volatility"] E2["Systemic Risk Transmission"] E3["Cross-Asset Correlation Spike"] end

International Law on Pandemic Response: A First Stocktaking in Light of the Coronavirus Crisis ArXiv ID: ssrn-3561650 “View on arXiv” Authors: Unknown Abstract The coronavirus (SARS-CoV-2) pandemic is currently raging throughout the world. The ensuing crisis has acquired a multidimensional nature, affecting all levels Keywords: Pandemic, Crisis Management, Market Liquidity, Economic Recovery, Cross-Asset Complexity vs Empirical Score Math Complexity: 0.0/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: The paper is a legal analysis of international health regulations and human rights law with no mathematical formulas or empirical backtesting, focusing on theoretical and normative assessments of pandemic response frameworks. flowchart TD Goal["Research Goal: Assess International Law's adequacy in pandemic response based on coronavirus crisis."] Method["Methodology: Qualitative legal analysis of international instruments and case studies."] Inputs["Data/Inputs: WHO IHR 2005, International Health Regulations, pandemic response measures."] Process["Computational Process: Comparative analysis of legal frameworks vs. crisis management realities."] Outcome["Key Findings: Existing laws have gaps in enforcement; need for revised global governance."]

The Roberts Court’s Assault on Democracy ArXiv ID: ssrn-3540318 “View on arXiv” Authors: Unknown Abstract This article argues that economic and political developments in the last fifty years have in many respects undermined America’s democratic institutions and that Keywords: Political Risk, Institutional Economics, Macroeconomics, Economic Policy, Governance, Macro Complexity vs Empirical Score Math Complexity: 0.0/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: The paper is a legal and political commentary on the Roberts Court’s impact on democracy, with no mathematical or empirical analysis of financial markets. flowchart TD A["Research Question<br>How has the Roberts Court<br>impacted American Democracy?"] --> B["Methodology: Case Law &<br>Historical Institutional Analysis"] B --> C["Inputs: Decisions,<br>Campaign Finance Data,<br>Political Polarization Metrics"] C --> D["Computational Process<br>Cost-Benefit &<br>Institutional Risk Analysis"] D --> E["Key Findings<br>Erosion of voting rights,<br>wealth-influenced policy,<br>gridlock in governance"]

DecentralizedFinance(DeFi) ArXiv ID: ssrn-3539194 “View on arXiv” Authors: Unknown Abstract DeFi (‘decentralized finance’) has joined FinTech (‘financial technology’), RegTech (‘regulatory technology’), cryptocurrencies, and digital assets as one of th Keywords: Decentralized Finance (DeFi), Fintech, Cryptocurrency, Blockchain, Digital Assets, Crypto / Digital Assets Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a legal and policy analysis discussing the regulatory implications of decentralized finance, with no mathematical formulas, code, or empirical backtesting presented in the excerpt. flowchart TD A["Research Goal: Impact of DeFi<br>on Traditional Finance"] --> B["Key Methodology: Literature Review &<br>Blockchain Data Analysis"] B --> C{"Data/Inputs"} C --> D["Smart Contract Logs<br>& Transaction Data"] C --> E["Academic Papers &<br>Market Reports"] D & E --> F["Computational Processes"] F --> G["Statistical Analysis of<br>Yield Rates & Liquidity"] F --> H["NLP for Sentiment<br>& Risk Assessment"] G & H --> I["Key Findings: High Returns,<br>Systemic Risks, &<br>Regulatory Challenges"]