Investment Decision Logging
Investment decision logging is the practice of recording why an investor entered, held, sold, or waited on a position, including the thesis, evidence, risk, expected catalyst, and conditions that would change the decision. In EP69 AI时代来临，投资不再是单机模式, Tang Haocheng contrasts professional investors, who often must write down industry and company reasoning, with ordinary investors who may forget why they made a trade months later.
The episode treats logging as both an Investment Risk Management habit and a product opportunity for Financial AI Agents. AI can help collect evidence, summarize changes, remind the user what they were waiting for, and make a hold-or-wait decision more explicit without taking over the final judgment.
Key Claims
- A written thesis makes later review possible; memory alone tends to rationalize the outcome.
- Logging should include not only “buy” or “sell” but also the reason for waiting and the data or event that would matter next.
- Decision records make Behavioral Investing Biases easier to notice because the original reason can be compared with later emotion.
- AI assistants can improve the workflow by linking records to watchlists, natural-language alerts, and updated market information.
Connections
- Tang Haocheng — guest who emphasizes the professional-investor written-thesis habit.
- Investment Risk Management — risk-control reason for maintaining records.
- AI Investment Research and Financial AI Agents — AI-supported research and follow-up workflow.
- Behavioral Investing Biases — biases the practice helps expose.
- Investor Education — practical skill ordinary investors can learn before relying on product recommendations.