As someone interested in applying data analytics techniques to finance and economics, I tend to look for leading indicators that contribute new insights when compared to existing ones.
It also helps if they are not obvious, and go somewhat against consensus; e.g., in interviews on media covering financial markets issues, frequently experts express the opinion that recent market gains do not influence future performance.
If we put this opinion to the test against data, we find that the opposite is what usually happens, as you can see from the graph below, which shows annualized 10-year compound changes in the S&P 500.
Dividends are not considered in this depiction. The graph is based on monthly data points from January 1910 to March 2022, where the annualized changes are calculated for each month as follows.
SP10(T) = ( SP(T) – SP(T-10y) ) ^ (1/10) – 1
In this equation we have:
SP10(T): the annualized 10-year compound change in the S&P 500 at a given point in time (T).
SP(T): the value of the S&P 500 at a given point in time (T). For this illustration, we used the average value of the S&P 500 for each month.
SP(T-10y): the value of the S&P 500 exactly 10 years prior to the time T.
This is a fairly easy calculation, which relies on a single variable. The graph seems to show that periods in which the SP10 moves quickly above 10% typically precede market crashes.
As a leading indicator (not a cause), the SP10 may not be precise enough to be used alone. Nevertheless, it can be used together with other indicators (e.g., Shiller’s PE10 ratio) in a more complete predictive model.
