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).
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 sell or buy
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. A popular approach where a "trader agent" learns
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 often outperforming traditional methods.
Recent research focuses on using deep learning to automate "buy/sell/hold" signals, often outperforming traditional methods.