At Timeline, we believe that historical data over 118 years is the most objective way to illustrate asset class behaviour. However, we accept that some users may want to test the withdrawal strategies based on their return expectation.
Timeline enables users to test withdrawal strategies based on extensive historical data as well as Monte Carlo simulations. Extensive historical data takes financial planning out of a ‘theoretical’ realm into the ‘empirical’world because it’s based on observed behaviour of asset classes. This frees a financial planner from having tosecond guess how asset classes are going to behave in the future.
The main criticism of the historical model is that there aren’t enough scenarios in history to account for the widerange of possible outcomes. Some periods overlap and aren’t entirely independent of each other. Also, global markets are more complicated today than they’ve ever been and returns could be worse in the future.
Monte Carlo attempts to overcome the limitations of historical data by generating scenarios that we’ve never seenbefore. The practical relevance for Monte Carlo is that it enables us to explore scenarios that have never happenedbefore. But Monte Carlo simulations have their weaknesses. The chief one is that returns in any one yearare entirely independent of previous years.
The implication is that Monte Carlo analysis tends to overstate tail risk,compared to the actual historical worst case. This is because Monte Carlo simulations don’t account for meanreversion, which is a key characteristic of most asset classes.
As Dr. Derek Tharp CFP notes ‘whether the prior year was flat, saw a slight increase, or a raging bull market, Monte Carlo analysis assumesthat the odds of a bear market decline the following year are the same. And the odds of a subsequent declinein the following years also remains the same, regardless of whether it would be the first or eighth consecutiveyear of a decline! Yet, a look at real-world market data reveals that this isn’t really the case. Instead, marketreturns seem to exhibit at least two different trends. In the short-run, returns seem to exhibit “positive serialcorrelation” (i.e., momentum – whereby short-term positive returns are more likely to be followed by positivereturns, and vice-versa), and, in the long-run, returns seem to exhibit “negative serial correlation” (i.e., mean reversion – whereby longer-term periods of low performance are followed by periods of higher performance,and vice-versa).’
For a detailed discussion of the strengths and weaknesses of historical and Monte Carlo models, please see: