Investment professionals are familiar with this problem. A backtest may show a pattern. A factor may appear significant. A model may identify structure in returns. Yet once turnover, trading costs, slippage, market impact, regime shifts and operational constraints are included, the apparent opportunity may disappear.
AI does not eliminate this problem. In some cases, it can intensify it.
A reinforcement learning agent is trained to act. If it detects weak signals, it may try to exploit them through frequent position changes. But when those signals are close to noise, trading activity can become costly. The agent may overfit short-lived fluctuations, engage in excessive trading and erode returns through costs.
Alternatively, the agent may learn the opposite lesson. If the environment is noisy and transaction costs are meaningful, the most rational policy may be to do nothing. In this case, the AI system does not become a superior trader. It becomes inactive.
Both outcomes are important. They show that failure is not random. It is informative.
If an AI agent cannot transform a detectable pattern into positive net performance, that may tell us something about the market environment. The signal may be too weak. The cost structure may be too high. The observation space may be insufficient. The model may be too constrained. Or the pattern may be statistically real but economically untradable.
This is the essence of the Learnability Threshold.
