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A common problem when evaluating a portfolio manager is that the history of returns is often so short that estimates of risk and performance measures can be highly unreliable. A similar problem occurs when testing a new trading strategy. Even if you have a fairly long history for the strategy’s performance, often you only have observations over a single market cycle which can make it difficult to evaluate how your strategy would have held up in other markets. If you trade stocks you have probably heard the refrain: “I’ve never seen a bad back test”.
One method to address this deficiency is through Factor Model Monte Carlo (FMMC). FMMC can be used to estimate a factor model based on a set of financial and economic factors that reliably explain the returns of the fund manager. We can then simulate returns to determine how the manager would have performed in a wide variety of market environments. The end result is a model that produces considerably better estimates for risk and performance than if we simply used the return series available to us.
The Task and Set Up
For this case study, we will be analyzing the returns for the new hedge fund Aric’s Hedge Fund; hereafter known as AHF. The hedge fund case is particularly interesting because hedge funds can use leverage, invest in any asset class, go long or short, and use many different instruments. Hedge funds are often very secretive about their strategy and holdings. Thus, having a reliable risk model to explain the source of their returns is essential.
Keep in mind that Aric’s Hedge Fund is not a real hedge fund (I’m Aric, I don’t have a hedge fund), but this is a _real _series of returns. I obtained the returns for a hedge fund in operation that we invest in where I work so the results of this study are applicable to a real-world scenario.
We have data for Aric’s Hedge Fund from January 2010 to March 2020. For the purpose of this post and evaluating the accuracy of our model we will pretend as though AHF is pretty new to the scene and that we only have data from January 2017 through March 2020. To overcome the data deficiency, we will build a factor model on the basis of this “observed data” and then utilize the entire data series to evaluate the accuracy of our simulation for assessing the risk and performance statistics.
The below graph shows the cumulative return of AHF since January 2010. The data to the right of the red line represents the “observed period”.
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