Limitations of Buy‐and‐Hold‐like Behavior in DQN Training

Limitations of Buy-and-Hold-like Behavior in DQN Training

In some successful training runs,
the hold rate (the ratio of time steps where the agent decides to hold positions)
gradually increases and settles at a high level.

This indicates that the model is behaving similarly to a Buy and Hold strategy.

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Why This Is Problematic

• If the model simply learns Buy and Hold,
  the value of using AI and DQN becomes questionable.
• Buy and Hold is simple and does not require complex reinforcement learning techniques.
• Unless the model outperforms naive Buy and Hold significantly,
  the effort may not be justified.

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Why Monitor Hold Rate?

Tracking the hold rate allows us to:

• Detect when the agent converges to trivial strategies (like Buy and Hold).
• Identify when further innovation is necessary to find genuinely valuable trading policies.

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How to Address This Issue

• Emphasize return per unit of committed capital rather than raw total returns.
• Introduce constraints that penalize excessive holding.
• Explore alternative reward structures that prioritize active and strategic trading.
• Compare model performance directly against a Buy and Hold benchmark.

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Perspective

While discovering Buy and Hold-like behavior might seem disappointing,
it also suggests that the learning system is functioning correctly at a basic level:
recognizing that holding can be a rational action in certain markets.

Thus, it is promising, but further refinements are clearly needed.

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