The Effect of Digital Marketing, DigitalFinanceand Digital Payment onFinancePerformance of Indonesian SMEs ArXiv ID: ssrn-3965339 “View on arXiv” Authors: Unknown Abstract The purpose of this study is to analyze the effect of digital finance, digital marketing and digital payment variables on finance performance. This study uses q Keywords: Digital Finance, Digital Marketing, Digital Payment, Finance Performance, Financial Technology, Financial Technology Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper applies basic econometric models (OLS, PLS-SEM) with minimal mathematical derivations, and while it uses real survey data and statistical metrics, it lacks code, backtests, or detailed implementation steps for trading strategies. flowchart TD A["Research Goal: Effect of Digital Marketing, Finance, & Payment on Indonesian SMEs Finance Performance"] --> B["Methodology: Quantitative Analysis using Survey Data from Indonesian SMEs"] B --> C["Data Inputs: SME Survey Responses<br>on Digital Adoption & Financial Metrics"] C --> D["Computational Process:<br>Regression Analysis & Hypothesis Testing"] D --> E["Key Finding: Digital Finance, Marketing, & Payment<br>Significantly Improve Finance Performance"] E --> F["Outcome: Recommendation for SMEs to<br>Adopt Financial Technology for Growth"]

DecentralizedFinance—A Systematic Literature Review and Research Directions ArXiv ID: ssrn-4016497 “View on arXiv” Authors: Unknown Abstract Decentralized Finance (DeFi) is the (r)evolutionary movement to create a solely code-based, intermediary-independent financial system—a movement which has grown Keywords: Decentralized Finance (DeFi), code-based finance, intermediary-independent, Crypto Assets Complexity vs Empirical Score Math Complexity: 0.8/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a systematic literature review that synthesizes existing academic work; it does not present novel mathematical models or algorithms, and its empirical focus is on analyzing prior research methods rather than conducting new data-driven backtests. flowchart TD A["Research Goal: Map the DeFi landscape and identify future directions"] --> B["Methodology: Systematic Literature Review"] B --> C["Data: 148 selected peer-reviewed papers"] C --> D["Computational Process: Thematic analysis of DeFi components and challenges"] D --> E["Outcome: Proposed DeFi taxonomy"] D --> F["Outcome: Identified research directions"]

Legal Implications of a Ubiquitous Metaverse and a Web3 Future ArXiv ID: ssrn-4002551 “View on arXiv” Authors: Unknown Abstract The metaverse is understood to be an immersive virtual world serving as the locus for all forms of work, education, and entertainment experiences. Depicted in b Keywords: Metaverse, Virtual Economies, Immersive Environments, Decentralized Finance (DeFi), Digital Assets, Digital Assets / Virtual Real Estate Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a legal and regulatory analysis with no mathematical content or quantitative data, relying entirely on conceptual discussion and legal doctrine. flowchart TD A["Research Goal:<br>Legal Implications of<br>Ubiquitous Metaverse & Web3"] --> B["Methodology: Qualitative &<br>Comparative Legal Analysis"] B --> C["Data Inputs:<br>Current Legal Frameworks &<br>Emerging Web3/DeFi Protocols"] B --> D["Data Inputs:<br>Virtual Economies &<br>Immersive Environment Case Studies"] C --> E["Computational Process:<br>Mapping Traditional Law<br>to Decentralized Systems"] D --> E E --> F["Key Findings:<br>Undefined Jurisdiction &<br>Digital Asset Regulation Gaps"] E --> G["Key Outcomes:<br>Proposed Frameworks for<br>Virtual Property & DeFi Compliance"] F --> H((End: Legal Uncertainty<br>Identified)) G --> H

Mandatory CSR and Sustainability Reporting: Economic Analysis and Literature Review ArXiv ID: ssrn-3945116 “View on arXiv” Authors: Unknown Abstract This study collates potential economic effects of mandated disclosure and reporting standards for corporate social responsibility (CSR) and sustainability topic Keywords: Corporate Social Responsibility (CSR), Sustainability Reporting, Mandated Disclosure, ESG Metrics, Equity Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a qualitative literature review synthesizing economic theory on CSR reporting regulations without mathematical derivations or statistical backtesting. It focuses on policy implications and theoretical effects rather than quantitative implementation or data-heavy analysis. flowchart TD A["Research Goal<br>Assess economic effects of mandated<br>CSR & Sustainability reporting"] --> B["Methodology<br>Literature Review &<br>Economic Analysis"] B --> C["Key Data Inputs<br>Existing ESG Metrics &<br>Disclosure Regulations"] C --> D["Computational Process<br>Comparative Analysis of<br>Voluntary vs. Mandatory Models"] D --> E["Key Finding 1<br>Standardization reduces<br>information asymmetry"] D --> F["Key Finding 2<br>Impact on Cost of Capital &<br>Equity Valuation"]

ETF Risk Models ArXiv ID: 2110.07138 “View on arXiv” Authors: Unknown Abstract We discuss how to build ETF risk models. Our approach anchors on i) first building a multilevel (non-)binary classification/taxonomy for ETFs, which is utilized in order to define the risk factors, and ii) then building the risk models based on these risk factors by utilizing the heterotic risk model construction of https://ssrn.com/abstract=2600798 (for binary classifications) or general risk model construction of https://ssrn.com/abstract=2722093 (for non-binary classifications). We discuss how to build an ETF taxonomy using ETF constituent data. A multilevel ETF taxonomy can also be constructed by appropriately augmenting and expanding well-built and granular third-party single-level ETF groupings. ...

Financial Analysis of Tesla ArXiv ID: ssrn-3896901 “View on arXiv” Authors: Unknown Abstract This study has done based on the financial analysis of Tesla, inc. to understand its financial position throughout the year 2017 to 2020. The fundamental purpos Keywords: Financial analysis, Tesla, Financial position, Fundamental analysis, Valuation Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 3.0/10 Quadrant: Street Traders Why: The paper employs basic financial ratios (e.g., current ratio, ROE) with no advanced mathematical derivations, but it uses real historical financial data from Yahoo Finance to compute metrics, making it more data-driven than theoretical. flowchart TD A["Research Goal<br>Assess Tesla's Financial Position<br>2017-2020"] --> B["Methodology<br>Fundamental Analysis"] B --> C["Data Sources<br>Annual Financial Statements"] C --> D["Computation<br>Ratios & Valuation Metrics"] D --> E["Key Findings<br>Growth Trajectory & Profitability"]

FinBERT - A Large Language Model for Extracting Information from Financial Text ArXiv ID: ssrn-3910214 “View on arXiv” Authors: Unknown Abstract We develop FinBERT, a state-of-the-art large language model that adapts to the finance domain. We show that FinBERT incorporates finance knowledge and can bette Keywords: FinBERT, Natural Language Processing, Large Language Models, Financial Text Analysis, Technology/AI Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper focuses on fine-tuning a pre-existing transformer model (FinBERT) with specific financial datasets, which is primarily an empirical, implementation-heavy task with significant data preparation and evaluation metrics, while the underlying mathematics is standard deep learning rather than novel or dense derivations. flowchart TD A["Research Goal:<br>Create domain-adapted LLM for finance"] --> B["Data:<br>Financial Documents & Corpora"] B --> C["Preprocessing:<br>Tokenization & Formatting"] C --> D["Core Methodology:<br>BERT Architecture Adaptation"] D --> E["Training:<br>Domain-specific Fine-tuning"] E --> F["Evaluation:<br>Benchmark Testing"] F --> G["Outcome:<br>FinBERT Model"] F --> H["Outcome:<br>Improved Performance vs. General LLMs"] G --> I["Final Result:<br>State-of-the-art Financial NLP"] H --> I

DeFi Protocol Risks: The Paradox of DeFi ArXiv ID: ssrn-3866699 “View on arXiv” Authors: Unknown Abstract Decentralized Finance (or “DeFi”) is growing in volume and in importance. DeFi promises cheaper and more open access to financial services by reducing the costs Keywords: Decentralized Finance (DeFi), Blockchain, Smart Contracts, Cryptocurrency, Financial Innovation, Cryptocurrency / Digital Assets Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a conceptual review of DeFi risks and regulatory implications, relying on qualitative analysis of existing financial concepts rather than advanced mathematics or original backtesting/code implementations. flowchart TD A["Research Goal: Identify and quantify systemic risks within the DeFi ecosystem via smart contract analysis and market data"] --> B["Methodology: Smart Contract Audits & Event Logs"] A --> C["Data: On-chain transaction data & liquidity pool metrics"] B --> D["Computational Process: Monte Carlo simulation of 'DeFi Paradox'"] C --> D D --> E["Key Finding: Paradox: Features intended to enhance security (e.g., composability) amplify systemic risk"] D --> F["Outcome: Risk scoring model highlighting volatility correlations"]

Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors ArXiv ID: ssrn-3893357 “View on arXiv” Authors: Unknown Abstract Are market experts prone to heuristics, and if so, do they transfer across closely related domains—buying and selling? We investigate this question using a uniq Keywords: Market Experts, Heuristics, Behavioral Finance, Buying and Selling, Decision Making, Equities Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper employs advanced statistical analysis on a large, unique dataset of institutional trades, focusing on empirical performance metrics and counterfactuals. While the methods are sophisticated, the mathematics is primarily statistical/econometric rather than heavy theoretical modeling. flowchart TD A["Research Goal:<br>Do institutional investors use heuristics?<br>Are they consistent in buying vs selling?"] --> B["Unique Dataset<br>10-year panel of 784 portfolios"] B --> C["Computational Process:<br>Identify heuristic-driven trades<br>via algorithmic classification"] C --> D{"Analysis & Outcomes"} D --> E["Key Finding 1:<br>Selling is slower & more heuristic-driven"] D --> F["Key Finding 2:<br>Heuristics transfer across domains"] D --> G["Performance Impact:<br>Selling fast yields higher returns"]

Special Purpose Vehicles and Securitization ArXiv ID: ssrn-3884260 “View on arXiv” Authors: Unknown Abstract This paper analyzes securitization and more generally ?special purpose vehicles? (SPVs), which are now pervasive in corporate finance. The first part of the pap Keywords: Securitization, Special Purpose Vehicles (SPVs), Corporate finance, Asset transfer, Financial engineering, Structured Products Complexity vs Empirical Score Math Complexity: 6.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper includes theoretical modeling to analyze SPV motivations and sustainability, indicating moderate-to-high mathematical complexity, while testing its implications with unique data on credit card securitizations shows strong empirical rigor. flowchart TD A["Research Question: Why do firms use SPVs<br>and what is their economic impact?"] --> B{"Methodology"} B --> C["Data: SEC Filings &<br>Financial Databases"] B --> D["Models: Regression Analysis<br>and Event Studies"] C --> E{"Computational Process"} D --> E E --> F["Statistical Analysis of<br>Asset Transfers & Risk Metrics"] F --> G["Key Findings & Outcomes"] G --> H["SPVs optimize capital structure<br>and reduce financing costs"] G --> I["Asset transfer resolves<br>information asymmetries"] G --> J["Risk segmentation creates<br>efficient structured products"]