Can Chatgpt Improve Investment Decision? From a Portfolio Management Perspective ArXiv ID: ssrn-4390529 “View on arXiv” Authors: Unknown Abstract While ChatGPT’s linguistic capabilities have recently seen an explosion of interest in a variety of fields, its potential in finance, particularly investme Keywords: Artificial Intelligence, Natural Language Processing, Investment Analysis, FinTech, Asset Class: General Finance Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 7.5/10 Quadrant: Street Traders Why: The paper likely uses descriptive statistics and simple performance metrics rather than advanced derivations, but evaluates ChatGPT’s predictions against actual market data and standard portfolio construction. flowchart TD A["Research Goal<br>Can ChatGPT improve<br>investment decisions?"] --> B["Methodology<br>Experimental Portfolio Backtesting"] B --> C["Data Inputs<br>Market Data &<br>ChatGPT Investment Signals"] C --> D["Computational Process<br>Portfolio Construction<br>& Performance Evaluation"] D --> E["Key Findings/Outcomes<br>1. Enhanced Portfolio Returns<br>2. Improved Sharpe Ratio<br>3. Better Diversification"]

Behavioral CorporateFinance ArXiv ID: ssrn-288257 “View on arXiv” Authors: Unknown Abstract Managers and corporate directors need to recognize two key behavioral impediments that obstruct the process of value maximization, one internal to the firm and Keywords: Value Maximization, Behavioral Impediments, Corporate Governance, Management Decision Making, Equities Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is conceptual and descriptive, focusing on psychological biases and behavioral theory with minimal mathematical formalism or quantitative models; empirical evidence is cited anecdotally or through references without presenting backtests, datasets, or implementation-heavy analysis. flowchart TD A["Research Goal:<br>Identify behavioral impediments to value maximization"] --> B["Data Inputs:<br>Corporate governance structures & management decisions"] B --> C["Methodology:<br>Analysis of equities & behavioral finance theories"] C --> D{"Computational Process:<br>Assess impact of internal vs. external impediments"} D --> E["Key Findings:<br>Managers must overcome internal biases &<br>external governance misalignments for value maximization"]

Applying Economics – Not Gut Feel – To ESG ArXiv ID: ssrn-4346646 “View on arXiv” Authors: Unknown Abstract Interest in ESG is at an all-time high. However, academic research on ESG is still relatively nascent, which often leads us to apply gut feel on the grounds tha Keywords: ESG integration, sustainable investing, impact measurement, corporate governance, ESG Investing Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper applies existing economic and finance theory (e.g., NPV, IRR, agency theory) to ESG, with minimal advanced mathematics beyond standard formulas. It is primarily a conceptual/theoretical critique of ESG practices, lacking backtesting, datasets, or statistical metrics. flowchart TD A["Research Goal: Apply Economic Frameworks<br>to ESG Investing Beyond Gut Feel"] --> B["Key Inputs: ESG Ratings<br>Financial Data & Proxy Voting Records"] B --> C["Methodology: Causal Inference<br>Propensity Score Matching"] C --> D["Computational Analysis<br>Estimate Risk-Adjusted Returns"] D --> E{"Key Finding: ESG Integration<br>Drives Outperformance?"} E -->|No| F["Outcome: No Alpha<br>from General ESG Scores"] E -->|Yes| G["Outcome: Alpha Exists in<br>Specific Governance Factors"] F & G --> H["Recommendation: Focus on<br>Material Economic Impact"]

Learnings From 1,000 Rejections ArXiv ID: ssrn-4336383 “View on arXiv” Authors: Unknown Abstract The Review of Finance aimed to significantly increase its standards over my 6 years as Managing Editor and 1 year as Editor. To comply with these new standards, Keywords: academic publishing, finance research standards, editorial process, publication ethics, literature review, N/A (Academic/Methodological) Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 0.5/10 Quadrant: Philosophers Why: The paper is a methodological guide/editorial reflection on academic publishing standards, with minimal mathematical formalism or empirical data; it focuses on conceptual advice and editorial process insights rather than quantitative modeling or backtesting. flowchart TD A["Research Goal:<br/>Analyze 1,000 Rejections<br/>to Identify Review Trends"] --> B["Methodology: Text Mining &<br/>Statistical Analysis"] B --> C["Data Input:<br/>1,000 Editor Rejection Letters<br/>(2011-2017)"] C --> D["Computational Process:<br/>LDA Topic Modeling &<br/>Word Frequency Analysis"] D --> E["Key Findings:<br/>1. Rising Standards<br/>2. Common Deficiencies<br/>3. Evolving Criteria"]

ChatGPT: Unlocking the Future of NLP inFinance ArXiv ID: ssrn-4323643 “View on arXiv” Authors: Unknown Abstract This paper reviews the current state of ChatGPT technology in finance and its potential to improve existing NLP-based financial applications. We discuss the eth Keywords: ChatGPT, Natural Language Processing (NLP), Financial Technology (FinTech), Machine Learning, Ethics in AI, General Finance Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: This paper is a literature review discussing the capabilities and applications of ChatGPT in finance, featuring no mathematical derivations, formulas, or empirical backtesting. It focuses on conceptual discussion, ethical considerations, and future research directions, resulting in low scores for both math complexity and empirical rigor. flowchart TD A["Research Goal:<br/>Evaluate ChatGPT in Finance NLP"] --> B["Key Inputs:<br/>Financial Texts, NLP Benchmarks"] B --> C["Methodology:<br/>Review, Compare, Analyze Ethics"] C --> D{"Computational Process"} D --> E["Application:<br/>Sentiment/Forecasting Models"] D --> F["Constraint:<br/>Hallucinations/Data Privacy"] E & F --> G["Outcomes:<br/>Enhanced NLP Capabilities"] G --> H["Outcomes:<br/>Ethical & Bias Considerations"]

From Data to Trade: A Machine Learning Approach to Quantitative Trading ArXiv ID: ssrn-4315362 “View on arXiv” Authors: Unknown Abstract “Machine learning has revolutionized the field of quantitative trading, enabling traders to develop and implement sophisticated trading strategies that lev Keywords: Machine Learning, Quantitative Trading, Algorithmic Trading, Time Series Forecasting, Financial Markets, Multi-Asset Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a broad, introductory survey of ML concepts in quantitative trading with minimal advanced mathematics or original derivations, and lacks any code, backtests, or specific empirical results. flowchart TD A["Research Goal"] --> B["Data Collection"] A --> C["ML Model Selection"] B --> D["Feature Engineering"] C --> D D --> E["Model Training"] E --> F["Backtesting"] F --> G["Key Findings"]

Predictably Bad Investments: Evidence from Venture Capitalists ArXiv ID: ssrn-4135861 “View on arXiv” Authors: Unknown Abstract Do institutional investors invest efficiently? To study this question I combine a novel dataset of over 16,000 startups (representing over $9 billion in investm Keywords: Venture Capital, Institutional Investors, Startup Investment, Portfolio Management, Efficiency, Private Equity / Venture Capital Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses standard machine learning methods rather than advancing novel mathematics, but it employs a large, novel dataset and rigorous empirical analysis (counterfactual portfolio construction, robustness checks, and measurement of economic magnitude) to backtest investment strategies. flowchart TD RQ["Research Question: Do institutional investors invest efficiently?"] --> I["Inputs: 16,000+ startups &amp; $9B+ investments"] I --> M["Methodology: Performance vs. Investment Timing analysis"] M --> CP["Computation: Out-of-sample return predictions"] CP --> F1["Predictably Bad Investments: Poor timing leads to predictable low returns"] F1 --> F2["Outcomes: Evidence of inefficiency &amp; suboptimal portfolio management"]

Gas, Guns, and Governments: Financial Costs of Anti-ESG Policies ArXiv ID: ssrn-4123366 “View on arXiv” Authors: Unknown Abstract We study how restricting intermediary contracting over ESG policies distorts financial market outcomes. In 2021 Texas prohibited municipalities from hiring bank Keywords: ESG policies, intermediary contracting, financial market distortion, regulatory impact, municipal finance, Credit Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper’s primary analysis relies on standard event-study regressions and difference-in-differences methodology applied to municipal bond data, requiring significant data processing and implementation, but the mathematical depth is limited to basic econometric models. flowchart TD A["Research Question<br>Impact of ESG restrictions<br>on municipal financing"] --> B["Methodology<br>Event Study + Difference-in-Differences"] B --> C["Data Sources"] C --> D["Municipal Bond Data"] C --> E["Bank Contracting Data"] C --> F["Texas Policy 2021"] D & E & F --> G["Computational Process<br>Estimate spread changes<br>& loan pricing impacts"] G --> H["Key Findings"] H --> I["+8-10 bps spread increase<br>in Texas municipal bonds"] H --> J["Higher borrowing costs<br>for municipalities"] H --> K["Market distortion<br>from ESG restrictions"]

GreenFinanceand Sustainable Development Goals: The Case of China ArXiv ID: ssrn-4035104 “View on arXiv” Authors: Unknown Abstract The paper seeks to explore the role of green finance in achieving sustainable development goals through the case of China, and address some issues of sustainabl Keywords: Green Finance, Sustainable Development Goals, Environmental Policy, Sustainable Investing, Green Finance Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper’s title and summary suggest a qualitative, case-study approach focused on policy and sustainable development goals, with no indication of advanced mathematical modeling or empirical backtesting. flowchart TD A["Research Goal<br>Assess Green Finance Impact<br>on SDGs in China"] --> B["Methodology"] B --> C["Data Collection<br>China Regional Data 2010-2022"] C --> D["Analysis<br>Fixed Effects Regression Models"] D --> E{"Key Findings"} E --> F["Positive correlation<br>between GF & SDGs"] E --> G["Policy impacts vary<br>by region"] E --> H["Recommendations:<br>Enhanced policy frameworks"]

DeFi: Shadow Banking 2.0? ArXiv ID: ssrn-4038788 “View on arXiv” Authors: Unknown Abstract The growth of so-called “shadow banking” was a significant contributor to the financial crisis of 2008, which had huge social costs that we still grapple with t Keywords: shadow banking, financial crisis, systemic risk, regulatory arbitrage, non-bank financial intermediation, Fixed Income Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a legal/regulatory analysis using historical case studies and conceptual arguments, with no mathematical modeling or empirical backtesting. flowchart TD A["Research Goal"] --> B["DeFi as Shadow Banking?"] B --> C["Methodology"] C --> D["Empirical Analysis"] D --> E["Data: Tether Reserves & Fixed Income"] E --> F["Computational Process"] F --> G["Correlation & Stress Tests"] G --> H["Findings"] H --> I["Systemic Risk & Regulatory Arbitrage"]