Some of the GuruModels that we feature on the site are based on the experiences of practitioners who have taken the time to articulate their approach (e.g. Jim Slater or Ben Graham). But other strategies, usually those developed by academics, have been based on what is known as “back-testing”. This involves looking at historical data and simulating what would have happened if you’d used a particular technique in the past. It is usually done without factoring in trading costs or taxes, given the complexity of factoring in these investor-specific aspects. One example of a Model derived using backtesting, which many readers will know we are huge fans of, is the Piotroski F-Score.Looking at the past like this is a powerful technique. However, it’s very important to be mindful of the pitfalls of investment simulations and “naive backtesting”. It is possible for many strategies to look great in backtests but most actually disappoint upon implementation. Simulations are usually always based on a 95% confidence interval, but in reality, investors are disappointed far more than 5% of the time (this has not been the case with Piotroski though!). To understand why, it’s important to be mindful of the following…
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