Direct answer
Review process is a method for examining past actions, outcomes, and decision-making to improve future work. The main risks are operational (how the review is run), market-related (how changing conditions affect results), counterparty-related (how providers or execution venues behave), and interpretation-related (how people and systems read the evidence).
Mechanism and definition
A review process typically takes inputs such as timestamps of decisions, recorded prices, order details, execution outcomes, and the reasoning or rules you say you followed. It then compares what happened against expectations or predefined criteria. Finally, it produces conclusions such as “what worked,” “what did not,” and “what to change.”
This workflow creates several risk surfaces:
- Operational risk: the review can fail if the data is incomplete, the criteria shift over time, or the process is not applied consistently.
- Market risk: even if your mechanics are correct, results can change because volatility, liquidity, and transaction costs vary.
- Counterparty risk: execution quality and policy enforcement depend on the platform, venue, and any intermediaries.
- Interpretation risk: the same dataset can lead to different conclusions based on assumptions and cognitive bias.
Evidence or example (with assumptions)
Assume you review a sequence of trades over two different weeks.
- Week A has tighter spreads and lower intraday swings.
- Week B has wider spreads and more rapid price movement.
If your review focuses on raw profit/loss without adjusting for costs and changing conditions, your conclusion may mainly reflect the market environment rather than the quality of your decisions. This is a market-risk and interpretation-risk overlap.
Operational risk can appear when some orders were reviewed using different fields (for example, using submitted prices rather than the actual executed prices). Then the “error rate” you calculate is not a real measurement of performance, because it measures recording choices.
Counterparty risk can appear when execution outcomes differ from what you expected from your historical dataset, such as when delays or partial fills change realized results. Even with the same strategy rules, outcomes can differ.
Limitations and risks to keep in mind
At least one material failure mode is that the review process can become a self-reinforcing loop: you notice patterns only in subsets that confirm your beliefs, and you discard contradictory evidence. This can lead to overfitting—treating random variation as a repeatable signal.
Other limitations commonly include:
- Confirmation bias: you may assign more weight to explanations that make the review “feel” consistent.
- Survivorship bias: if you only review the cases that completed normally, you may miss failures.
- Inconsistent evaluation standards: changing what counts as success between reviews reduces comparability.
- Data and cost sensitivity: results can be highly sensitive to transaction costs, execution quality, and the definitions used in tracking.
Because outcomes vary with costs, execution, and jurisdictional factors, historical relationships do not establish future results. In addition, even a well-run review cannot remove uncertainty; it can only structure what you test and how you validate it.
Verification and next questions
To verify claims about a review process, check whether the process defines inputs and outputs clearly, uses consistent criteria across time, and distinguishes realized outcomes from expectations. A useful next question is: “What exact fields and assumptions are used when comparing results across different market conditions?”
You can also ask whether the review separates operational errors (data recording, rule application, missing events) from market-driven effects (liquidity, volatility) and from execution-driven effects (fill quality, timing). This separation is key to reducing both interpretation risk and counterparty risk.