Trading Behavior

Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversity ArXiv ID: 2407.09557 “View on arXiv” Authors: Unknown Abstract Recent deep reinforcement learning (DRL) methods in finance show promising outcomes. However, there is limited research examining the behavior of these DRL algorithms. This paper aims to investigate their tendencies towards holding or trading financial assets as well as purchase diversity. By analyzing their trading behaviors, we provide insights into the decision-making processes of DRL models in finance applications. Our findings reveal that each DRL algorithm exhibits unique trading patterns and strategies, with A2C emerging as the top performer in terms of cumulative rewards. While PPO and SAC engage in significant trades with a limited number of stocks, DDPG and TD3 adopt a more balanced approach. Furthermore, SAC and PPO tend to hold positions for shorter durations, whereas DDPG, A2C, and TD3 display a propensity to remain stationary for extended periods. ...

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<br/>Test if individual investors<br/>are net buyers of<br/>attention-grabbing stocks"] --> B["Methodology<br/>Event Study & Regression Analysis"] B --> C["Data Inputs<br/>Daily Trades (TAQ) &<br/>News Data (Reuters)"] C --> D["Computation<br/>Calculate Abnormal Attention<br/>(News/High Volume)<br/>and Net Buying Imbalance"] D --> E{"Key Findings"} E --> F["Individuals: Net Buyers<br/>of high-attention stocks"] E --> G["Institutions: Net Sellers<br/>or no consistent effect"] E --> H["Outcome: Attention-driven<br/>demand creates temporary<br/>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:<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"]