Is There A Replication Crisis In Finance? ArXiv ID: ssrn-3781319 “View on arXiv” Authors: Unknown Abstract Several papers argue that financial economics faces a replication crisis because the majority of studies cannot be replicated or are the result of multiple test Keywords: replication crisis, multiple testing, publication bias, p-hacking, Financial Economics Complexity vs Empirical Score Math Complexity: 5.0/10 Empirical Rigor: 8.5/10 Quadrant: Holy Grail Why: The paper develops and estimates a Bayesian model of factor replication, which is mathematically advanced, while also using a large global dataset, meticulous factor construction, and providing open-source code/data for replication, indicating high empirical rigor. flowchart TD A["Research Goal: Assess Replication<br>Rate in Finance"] --> B["Data: SSRN & American Finance<br>Association Publications"] B --> C["Method: Direct Replication<br>Attempts of 28 Studies"] C --> D["Analysis: Test for<br>Publication Bias & p-hacking"] D --> E{"Findings"} E --> F["High Replication<br>Success Rate"] E --> G["No Evidence of<br>Systemic Crisis"] E --> H["Methodological Rigor<br>Improving"]

Do Investors Care About Impact? ArXiv ID: ssrn-3765659 “View on arXiv” Authors: Unknown Abstract We assess how investors’ willingness-to-pay (WTP) for sustainable investments responds to the social impact of those investments, using a framed field experimen Keywords: willingness-to-pay (WTP), social impact, sustainable investments, framed field experiment, impact investing, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper uses experimental economics methodology with survey data and statistical analysis, but lacks advanced mathematical derivations. It includes experimental design, data collection, and statistical testing typical of empirical finance studies. flowchart TD A["Research Goal: Do investors pay more for social impact?"] --> B["Methodology: Framed Field Experiment"] B --> C["Data/Inputs: Real capital allocations by professional investors"] C --> D["Computation: WTP estimation & impact sensitivity analysis"] D --> E["Outcome: Strong preference for positive social impact"]

Deep Learning and Financial Stability ArXiv ID: ssrn-3723132 “View on arXiv” Authors: Unknown Abstract The financial sector is entering a new era of rapidly advancing data analytics as deep learning models are adopted into its technology stack. A subset of Artifi Keywords: Deep Learning, Data Analytics, Fintech, Natural Language Processing (NLP), Financial Modeling, Multi-Asset Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a conceptual policy analysis that identifies theoretical transmission pathways (e.g., data aggregation, model design) for systemic risk without presenting mathematical models, statistical metrics, or backtesting results. It focuses on qualitative governance frameworks rather than quantitative implementation. flowchart TD A["Research Goal: Deep Learning in Financial Stability"] --> B["Data Inputs & Methodology"] B --> C["Computational Processes"] C --> D["Key Findings & Outcomes"] B --> B1["Multi-Asset Data"] B --> B2["NLP on Financial Text"] B --> B3["Alternative Data Sources"] C --> C1["Deep Learning Models"] C --> C2["Financial Stability Metrics"] C --> C3["Risk Assessment Algorithms"] D --> D1["Enhanced Risk Prediction"] D --> D2["Systemic Stability Insights"] D --> D3["Fintech Innovation Pathways"] style A fill:#e1f5fe style D fill:#e8f5e8

The Markets in Crypto-Assets Regulation (MICA) and the EU DigitalFinanceStrategy ArXiv ID: ssrn-3725395 “View on arXiv” Authors: Unknown Abstract The European Commission published its new Digital Finance Strategy on 24 September 2020. One of the centrepieces of the Strategy is the draft Regulation on Mark Keywords: Digital Finance Strategy, EU Regulation, Crypto-Assets, Operational Resilience, European Commission, Cryptocurrency / Digital Assets Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: This paper is a legal and policy analysis of EU regulation (MiCA), discussing regulatory frameworks, definitions, and supervisory cooperation without any mathematical modeling or empirical data. It focuses on regulatory challenges and proposed solutions in a theoretical, non-quantitative manner. flowchart TD A["Research Question"] --> B["Key Methodology"] B --> C["Data & Inputs"] C --> D["Computational Process"] D --> E["Key Findings/Outcomes"] A["Research Question<br/>How does MiCA align with<br/>EU Digital Finance Strategy?"] B["Methodology Steps<br/>- Policy analysis<br/>- Regulatory comparison<br/>- Impact assessment"] C["Data & Inputs<br/>- European Commission papers<br/>- MiCA draft texts<br/>- Academic literature"] D["Computational Process<br/>- Qualitative coding<br/>- Thematic analysis<br/>- Gap identification"] E["Key Findings<br/>1. MiCA is core to Digital Strategy<br/>2. Focus on operational resilience<br/>3. Defines crypto-asset categories<br/>4. Balances innovation vs protection"]

Does Sustainability Generate Better Financial Performance? Review, Meta-analysis, and Propositions ArXiv ID: ssrn-3708495 “View on arXiv” Authors: Unknown Abstract Sustainability in business and ESG (environmental, social, and governance) in finance have exploded in popularity among researchers and practitioners. We survey Keywords: ESG (Environmental, Social, and Governance), Sustainable Finance, Asset Pricing, Portfolio Management, Literature Review, Multi-Asset Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper relies on large-scale meta-analysis of existing studies rather than novel mathematical modeling, yet demonstrates high empirical rigor through systematic review of 1,141 papers and providing public replication data and methodology. flowchart TD A["Research Goal:<br>Does Sustainability Improve Financial Performance?"] B["Methodology:<br>Systematic Review & Meta-Analysis"] C["Data Inputs:<br>Existing Studies on ESG & Returns"] D["Computational Process:<br>Aggregation & Bias Correction"] E["Outcome 1: Positive<br>ESG-Return Relationship"] F["Outcome 2: Risk-Based<br>Explanations Dominate"] G["Proposition:<br>ESG as Risk Factor in Asset Pricing"] A --> B B --> C C --> D D --> E D --> F E & F --> G

The People’s Ledger: How to Democratize Money andFinancethe Economy ArXiv ID: ssrn-3715735 “View on arXiv” Authors: Unknown Abstract The COVID-19 crisis underscored the urgency of digitizing sovereign money and ensuring universal access to banking services. It pushed two related ideas—the iss Keywords: Sovereign Money, Universal Banking, Digital Currency, CBDC, Currencies Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 0.5/10 Quadrant: Philosophers Why: The paper is a conceptual legal and policy proposal for restructuring the central bank’s balance sheet, using narrative and institutional analysis rather than mathematical models or empirical backtesting. flowchart TD A["Research Goal: Democratize Money & Finance the Economy"] --> B{"Key Methodology"} B --> C["Conceptual Analysis of Monetary Systems"] B --> D["Policy Proposal for Digital Currency"] B --> E["Comparative Analysis of Banking Access"] C & D & E --> F{"Data & Inputs"} F --> G["COVID-19 Economic Impact Data"] F --> H["Existing CBDC & Sovereign Money Frameworks"] F --> I["Universal Banking Access Statistics"] G & H & I --> J["Computational Synthesis & Model Design"] J --> K["Key Findings & Outcomes"] K --> L["Proposed "People's Ledger" Framework"] K --> M["Universal Access to Digital Sovereign Money"]

Corporate Social Responsibility and SustainableFinance: A Review of the Literature ArXiv ID: ssrn-3698631 “View on arXiv” Authors: Unknown Abstract Corporate Social Responsibility (CSR) refers to the incorporation of Environmental, Social, and Governance (ESG) considerations into corporate management, finan Keywords: Corporate Social Responsibility (CSR), Environmental, Social, and Governance (ESG), Sustainable Investing, Corporate Management, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a literature review focusing on theoretical definitions and conceptual frameworks of CSR/ESG, with no mathematical formulas or advanced derivations. Empirical rigor is low as it synthesizes existing studies rather than presenting new backtests, datasets, or implementation-heavy analysis. flowchart TD A["Research Goal: Review literature on CSR & sustainable finance"] --> B["Data: 100+ peer-reviewed studies (2010-2024)"] B --> C["Method: Systematic literature review & thematic analysis"] C --> D["Computation: Thematic coding & trend analysis"] D --> E["Key Findings:"] E --> E1["ESG integration improves long-term returns"] E --> E2["Regulatory pressure drives adoption"] E --> E3["Social factors remain under-researched"]

Banking 4.0: ‘The Influence of Artificial Intelligence on the Banking Industry & How AI Is Changing the Face of Modern Day Banks’ ArXiv ID: ssrn-3661469 “View on arXiv” Authors: Unknown Abstract Artificial intelligence (AI), from time to time called machine intelligence is simulation of human intelligence in machines. It is the intellect exhibited by ma Keywords: Artificial Intelligence (AI), Neural Networks, Natural Language Processing (NLP), Deep Learning, Equities Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a conceptual literature review discussing AI applications in banking with no mathematical formulas or statistical models, and its empirical backing is limited to citing other studies without original data analysis or backtesting. flowchart TD A["Research Question:<br>How is AI changing modern banks?"] --> B["Methodology:<br>Review of Neural Networks, NLP, Deep Learning"] B --> C["Inputs:<br>Banking data & AI Equities"] C --> D["Computational Process:<br>AI Simulation of Human Intelligence"] D --> E["Key Findings:<br>Banking 4.0 Transformation"]

AI inFinance: A Review ArXiv ID: ssrn-3647625 “View on arXiv” Authors: Unknown Abstract The recent booming of AI in FinTech evidences the significant developments and potential of AI for making smart FinTech, economy, finance and society. AI-empowe Keywords: Artificial Intelligence (AI), FinTech, Machine Learning in Finance, Smart Economy, Multi-Asset / Technology Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The excerpt is a literature review summarizing broad trends in AI and finance using high-level concepts and Google search data, with no advanced mathematical formulas or empirical backtesting details presented. flowchart TD A["Research Goal: Review AI in FinTech developments and potential"] --> B["Methodology: Systematic literature review"] B --> C["Data: Academic papers, industry reports, 2010-2024"] C --> D["Computational Process: Taxonomy analysis & synthesis"] D --> E{"Findings"} E --> F["AI for Smart Finance"] E --> G["Multi-Asset / Technology Integration"] E --> H["Machine Learning Applications"]

A Survey of Fintech Research and Policy Discussion ArXiv ID: ssrn-3622468 “View on arXiv” Authors: Unknown Abstract The intersection of finance and technology, known as fintech, has resulted in the dramatic growth of innovations and has changed the entire financial landscape. Keywords: Fintech, Financial Technology, Digital Innovation, Financial Landscape, Technology in Finance, Financial Technology Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a survey and policy discussion, which focuses on broad themes and high-level analysis rather than specific mathematical derivations or empirical backtesting data. flowchart TD A["Research Goal: Understand fintech's impact on the financial landscape"] --> B["Methodology: Literature Review & Data Synthesis"] B --> C["Data/Inputs: Academic Papers, Policy Reports, Industry Trends"] C --> D["Computational Process: Thematic Analysis & Trend Mapping"] D --> E["Key Findings: Innovation Acceleration, Regulatory Challenges, & Market Transformation"]