Look-Ahead Bias
What Is the Look-Ahead Bias?
Look-ahead bias happens by involving data or data in a study or simulation that could not have possibly been known or available during the period being analyzed. This can lead to inaccurate results in the study or simulation. More importantly, a look-ahead bias can unintentionally influence simulation results closer into line with the desired outcome of the test. This leads to economists and analysts putting too much confidence in their models and the ability of the model to predict and mitigate future events. Investors additionally need to be aware of the potential for look-ahead bias when evaluating particular trading strategies utilizing past data.
Understanding Look-Ahead Bias
Look-ahead bias often happens in "could have" scenarios, where an investor or other professional considers what is a missed opportunity in hindsight. What that person neglects to realize is that they know more currently looking back than they did at the time they made the decision. Therefore, it very well might be unwise to judge their — or others — past performance too cruelly in retrospect, especially assuming that key data was missing.
Assuming that an investor is backtesting the performance of a trading strategy, they genuinely should just use data that would have been available at the time of the trade to keep away from a look-ahead bias. For example, assuming a trade is simulated based on data that was not available at the time of the trade —, for example, a quarterly earnings number that was released a month later — it will lessen the precision of the trading strategy's true performance and potentially bias the results for the desired outcome.
The Look-Ahead Bias and Other Biases in Investing
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. These biases have the potential to influence simulation results closer into line with the desired outcome of the simulation, as the input parameters of the simulation can be selected so as to incline toward the desired outcome.
As mentioned, these biases are most clearly seen when investors look back upon the year. Stocks that have performed well all through the year may now be overbought on the assumption that they will do the same thing the next year. While past performance does influence future performance, investors really must look at the fundamentals of the company carefully as there is dependably the risk of overvaluation.
Assuming you took the top performing stocks toward the end of the year and afterward tried to choose common data points they had toward the beginning of the year, like the trailing P/E ratio range, you'd be falling prey to a look-ahead bias because you'd just be looking at stocks you know enjoyed huge growth rather than at all stocks with a comparative trailing P/E ratio range at that time. By excluding the full range of stocks, you would end up with overconfidence in trailing P/E ratio as the key measure to predict future appreciation. This look-ahead bias can be corrected by widening the sample to all stocks that fit your particular criteria toward the beginning of the year and tracking their outcomes too.
Features
- A backtested simulation with a look-ahead bias won't show an accurate result. Therefore, careful research is necessary to determine what data was available at that point.
- A look-ahead skews the results and leads to overconfidence in models and other frameworks worked out of the skewed results.
- Look-ahead bias is when data that was not readily available at the time is used in a simulation of that time period.
