Investor Attention

All that Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors ArXiv ID: ssrn-1151595 “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: 8.0/10 Quadrant: Street Traders Why: The paper focuses on empirical testing of a behavioral hypothesis using event studies and regressions on large-scale trading datasets, requiring significant data processing and backtesting but relying on relatively straightforward statistical models. flowchart TD A["Research Goal Test if individual investors are net buyers of attention-grabbing stocks"] --> B["Methodology Event Study & Regression Analysis"] B --> C["Data Inputs Daily Trades (TAQ) & News Data (Reuters)"] C --> D["Computation Calculate Abnormal Attention (News/High Volume) and Net Buying Imbalance"] D --> E{"Key Findings"} E --> F["Individuals: Net Buyers of high-attention stocks"] E --> G["Institutions: Net Sellers or no consistent effect"] E --> H["Outcome: Attention-driven demand creates temporary price pressure"]

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: Does investor attention drive buying 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 e.g., CRSP/TAQ"] B --> B2["Attention Proxies News mentions & Abnormal volume"] B --> B3["Investor Classification Individual vs. Institutional"] C --> C1["Event Study Design Focus on high-attention days"] C --> C2["Regression Analysis Trading volume vs. attention"] D --> D1["Net Buy Calculation Aggregate flows by investor type"] D --> D2["Control for Fundamentals Liquidity, Returns, Volatility"] E --> F1["Confirmation: Individuals buy high-attention stocks"] E --> F2["Institutional Behavior Contrast or indifference"] E --> F3["Implication Attention-driven anomalies"]