A type of bias that occurs when a study or simulation relies on data or information that is not yet available or known during the study
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Look-ahead bias is a type of bias that occurs when a study or simulation relies on data or information that was not yet available or known during the time period being studied. It generally leads to inaccurate results from a study or simulation. The incorporation of fundamental data that wasn’t available at the time of the study delivers biased results that are often close to the desired outcome but not to the real outcome.
In finance, look-ahead bias is commonly encountered in trading strategies and various financial models. If an analyst commits a look-ahead bias in a trading strategy, testing the strategy will likely return unreasonably positive results. However, the real application of the strategy is likely to deliver dramatically different results from those obtained during the testing process.
One of the issues with look-ahead bias is that it is reasonably difficult to detect during backtesting. Backtesting is a process of applying a model or a simulation to historical data to assess the accuracy of a model or a simulation.
In some cases, backtesting cannot signal that the model is biased. However, if during backtesting the model returns an exceptional result, then that can be a red flag that there is something wrong with the model. Many finance professionals involved in the development of trading strategies carefully review strategies that indicate returns above a certain level – for example, 20%.
The best solution to avoid the look-ahead bias is a thorough assessment of the validity of developed models and strategies.
Example of Look-ahead Bias
Let’s consider that you work as a quantitative analyst at a hedge fund. You’re working on the development of a new trading strategy for equities. Your model assesses the relationship between the release of quarterly earnings reports and the stock price.
The main assumption behind your model is that the stock price reacts to earnings reports. However, during the backtesting of the model, you assume that the company’s earnings reports are released on the same date as when the fiscal quarter closes.
This situation is a classic example of look-ahead bias, given that the quarterly earnings reports become available only one month after the end of the quarter. Therefore, your backtesting incorporates information that was not available during the time being tested. Thus, the results of the backtesting are likely to be inaccurate.
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