Cryptocurrency

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"]

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"]

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"]

Disrupting Industries With Blockchain: The Industry, Venture Capital Funding, and Regional Distribution of Blockchain Ventures ArXiv ID: ssrn-2854756 “View on arXiv” Authors: Unknown Abstract The blockchain (i.e., a decentralized and encrypted digital ledger) has the potential to disrupt many traditional business models. This study investigates the e Keywords: Blockchain, Distributed Ledger Technology (DLT), Disruptive Innovation, Digital Assets, Smart Contracts, Cryptocurrency/Blockchain Assets Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses descriptive statistics and regression analysis with real-world datasets on blockchain ventures, indicating solid empirical rigor, but the mathematical models are basic econometrics without advanced theory. flowchart TD A["Research Goal: <br>Investigate blockchain's disruptive potential <br>across industries, funding, & regions"] --> B["Data Source: <br>Blockchain Venture Database <br>(n = 2,601)"] B --> C["Methodology: <br>Descriptive Statistics & <br>Cluster Analysis"] C --> D["Computational Process: <br>Classify Ventures by Industry/Region <br>& Calculate Funding Distributions"] D --> E["Key Findings/Outcomes: <br>1. Non-Financial sectors emerging <br>2. Strong VC concentration <br>3. Regional Innovation Hubs"]