Deep academic dives into why humans choose to buy or sell reveal significant psychological biases.
A popular approach where a "trader agent" learns to maximize profits through trial and error. Papers in this field often define actions simply as -1 (sell), 0 (hold), and 1 (buy).
Research explores how external "noise" dictates trading signals. sell or buy
One of the most notable "deep" papers in the technical domain is "Deep Stock Trading: A Hierarchical Reinforcement Learning Framework for Portfolio Optimization and Order Execution", which uses complex AI policies to decide both what to trade (the high-level portfolio) and how to execute the buy/sell orders (the low-level timing). 1. AI and Deep Learning Models
Interestingly, some research suggests that having less money (leverage constraints) can actually improve outcomes because it forces investors to sell losing assets sooner to fund new opportunities, rather than holding on to them indefinitely. 3. Market Sentiment Influences Deep academic dives into why humans choose to
The HRPM Framework (mentioned above) is significant for managing the "commission fee" problem—it learns that simply knowing whether to buy or sell isn't enough; you must also minimize the cost of the trade itself. 2. Behavioral & Decision-Making Research
Research from the American Economic Association shows that investors are generally more likely to sell a security when they have a gain rather than a loss, even if holding onto the "loser" is financially detrimental. AI and Deep Learning Models Interestingly, some research
Recent research focuses on using deep learning to automate "buy/sell/hold" signals, often outperforming traditional methods.