Information Asymmetry

How and Why Credit Rating Agencies are Not Like Other Gatekeepers ArXiv ID: ssrn-900257 “View on arXiv” Authors: Unknown Abstract This article revisits some issues I raised in a 1999 article on credit rating agencies, which increasingly are the focus of scholars and regulators. I discuss h Keywords: Credit Rating Agencies, Regulatory Reform, Information Asymmetry, Credit Risk, Fixed Income Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is an analytical critique of the credit rating agency business model and regulatory environment, relying on economic theory, legal argument, and historical evidence rather than advanced mathematical modeling or empirical backtesting. flowchart TD A["Research Goal: Why are Credit Rating Agencies unique vs. other gatekeepers?"] --> B["Methodology: Comparative Legal & Economic Analysis"] B --> C["Data: Historical Regulatory Frameworks (1999 vs. Present)"] C --> D["Computational Process: Analyze Information Asymmetry & Liability Structures"] D --> E["Outcome 1: CRA's 'Disseminator' Status (vs. 'Verifier')"] D --> F["Outcome 2: Limited Impact of Standard Liability Regimes"] D --> G["Outcome 3: Unique Regulatory Dependence on CRA Output"]

All that Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors ArXiv ID: ssrn-460660 “View on arXiv” Authors: Unknown Abstract We test and confirm the hypothesis that individual investors are net buyers of attention-grabbing stocks, e.g., stocks in the news, stocks experiencing high abn Keywords: Investor attention, Behavioral finance, Market microstructure, Trading behavior, Information asymmetry, Equities Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 9.0/10 Quadrant: Street Traders Why: The paper uses basic statistical comparisons (t-tests, regressions) but focuses heavily on real-world brokerage data analysis, multiple attention proxies, and robustness checks, making it highly empirical and implementable for trading strategies. flowchart TD A["Research Goal:<br/>Does investor attention drive buying<br/>behavior, especially for individuals?"] --> B["Data & Inputs"] B --> C["Methodology"] C --> D["Computational Processes"] D --> E["Key Findings/Outcomes"] B --> B1["Daily Stock & Trading Data<br/>e.g., CRSP/TAQ"] B --> B2["Attention Proxies<br/>News mentions & Abnormal volume"] B --> B3["Investor Classification<br/>Individual vs. Institutional"] C --> C1["Event Study Design<br/>Focus on high-attention days"] C --> C2["Regression Analysis<br/>Trading volume vs. attention"] D --> D1["Net Buy Calculation<br/>Aggregate flows by investor type"] D --> D2["Control for Fundamentals<br/>Liquidity, Returns, Volatility"] E --> F1["Confirmation: Individuals<br/>buy high-attention stocks"] E --> F2["Institutional Behavior<br/>Contrast or indifference"] E --> F3["Implication<br/>Attention-driven anomalies"]

Stock Returns, Aggregate Earnings Surprises, and BehavioralFinance ArXiv ID: ssrn-380127 “View on arXiv” Authors: Unknown Abstract We study the stock market reaction to aggregate earnings news. Previous research shows that, for individual firms, stock prices react positively to earnings ne Keywords: Stock Market Reaction, Aggregate Earnings News, Event Study, Market Efficiency, Information Asymmetry, Equities Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses standard empirical finance econometrics (time-series regressions, correlation analysis) without highly advanced mathematical derivations, but is heavily data-driven with a 30-year Compustat sample and robust statistical tests. flowchart TD A["Research Goal<br>Understand stock market reaction to aggregate earnings news"] --> B["Data: CRSP & Compustat<br>Time Period: 1988-2017"] B --> C["Methodology: Event Study<br>Construct SUE portfolios"] C --> D{"Key Computational Processes<br>Abnormal Returns Calculation"} D --> E["Analyze Abnormal Returns vs<br>Aggregate Earnings Surprise"] D --> F["Information Asymmetry Analysis<br>Trading Volume Patterns"] E --> G["Key Findings/Outcomes"] F --> G subgraph G ["Key Findings/Outcomes"] G1["Market Underreacts to Aggregate Earnings News"] G2["Abnormal Returns Persist Post-Announcement"] G3["Support for Behavioral Finance Over Market Efficiency"] G4["Information Asymmetry Explains Delayed Reaction"] end style G fill:#e1f5e1,stroke:#2e7d32 style A fill:#e3f2fd,stroke:#1565c0 style B fill:#fff3e0,stroke:#ef6c00