What Is Look-Ahead Bias?
Look-ahead bias occurs by using information or data in a study or simulation that would not have been known or available during the period being analyzed. This will usually lead to inaccurate results in the study or simulation. Look-ahead bias can be used to sway simulation results closer into line with the desired outcome of the test.
- Look-ahead bias is when all the necessary knowledge for a simulation that is performed in the future was not available during the period of the initial test.
- This skews the results, as the new test is accounting for knowledge that was unbeknownst to the performer of the original test.
- Look-ahead bias is often discussed with the portfolio results of an investor, but it can be applied to any business or simulation.
- A backtested simulation with a look-ahead bias will not show an accurate result. Therefore, careful research is necessary to determine was knowledge was available at the time.
Understanding Look-Ahead Bias
Look-ahead bias often happens in "could have" scenarios, where an investor or other professional looks back and considers what is usually a missed opportunity. What that person fails to realize is that they know more during the period of retrospection than they did at the time they made the decision, and it is, therefore, unfair to consider their—or others—past performance, especially if key information was missing.
To avoid look-ahead bias, if an investor is backtesting the performance of a trading strategy, it is vital that they only use information that would have been available at the time of the trade. For example, if a trade is simulated based on information that was not available at the time of the trade—such as a quarterly earnings number that was released three months later—it will diminish the accuracy of the trading strategy's true performance and potentially bias the results in favor of the desired outcome.
Real World Example of Look-Ahead Bias
Look-ahead bias is one of many biases that must be accounted for when running simulations. Other common biases are sample selection bias, time period bias, and survivorship bias. All of these biases have the potential to sway simulation results closer into line with the desired outcome of the simulation, as the input parameters of the simulation can be selected in such a way as to favor the desired outcome.
For example, if a rocket company is trying to pass inspection but repeatedly fails preliminary simulations, they may take some time to find why they were unable to pass. A chief engineer is fired for the repeated failures. During their testing, they find that a part during their simulations was ordered from the wrong manufacturer and was slightly heavier than the part they need. The engineer was not aware of the switch.
The problem, however, is the new part hasn't been made yet, so the rocket company adds the weight of the new part into the simulation in order to pass a safety exam. The company will not rehire the engineer, even though they were not at fault, because they didn't have all the necessary information.