Using historical data to test the performance of retirement plans is called backtesting. Backtesting is a strategy used in different industries including finance, meteorology, and oceanography.

Backtesting is a one tool, but not the only tool, that you can use to understand and estimate the performance of a particular withdrawal strategy.

Limitations of Backtesting

Backtesting is only effective when you have high-quality data to run calculations with. The data set used in FI Calc was compiled by a Nobel Prize-winning economist, Robert Shiller (you can read more about the data set used in FI Calc here).

Another problem with backtesting is called overfitting. Overfitting is when you design an algorithm that follows too closely to the current data set, but is not effective with new data.

One of the ways to avoid overfitting is to test your models in other scenarios not present in the historical data, such as by running what are called Monte Carlo simulations. FI Calc does not currently support Monte Carlo simulations, but it is a feature that we plan to introduce in the future.