S2000magician wrote:
allalongthewatchtower wrote: I am reading that Statistical factors have no basis in finance theory for return-generating models. I am not quite what is meant by Statistical factors. I believe it means historical average mean return and historical annual sd. Am I correct? I read and re-read Schweser and Curriculum, but I wasn’t too sure what Statistical factors mean. I’d appreciate any thoughts.
I believe that you’re misinterpreting what you’re reading, or else Schweser’s interpretation of the curriculum is wrong.
Statistical factors can be anything:
- The GDP of Sri Lanka
- How fast (in lbs. / week) our puppy’s weight increases
- The 6-month GBP LIBOR rate
- The nationality of the driver who wins the Monaco Grand Prix
- The trailing 12-month moving average unemployment rate in Argentina
- Detroit’s per capita annual income divided by Hong Kong’s per capita annual credit card purchases
- Whatever
You build a mathematical model for the returns on McDonald’s common stock with all of these (and more) as independent variables and do a lot of fancy statistical analysis to determine which inputs have statistically significant coefficients and which ones don’t, then use that information to decide which inputs you’ll use in your predictive model.
The point that the curriculum makes (or tries to make) is that you may get many input variables that have statistical significance, but no economic basis for that significance. It’s unlikely, for example, that Geordie’s growth rate has any economically valid relation to the return on McDonald’s common stock, even though you may get a very high positive correlation between the two. On the other hand, statistical factors such as population growth, GDP growth, interest rates, unemployment rates, and so on are like to have both statistical significance and an economic basis for inclusion in such a model.