In the prior post we showed some skill in estimating the end-of-month factor returns for the Dynamic Trading Risk Factor based on early measurements of partial universe returns and a bias correction model.

However, we've not indicated why that would be interesting. The principal reason is as follows: we have good evidence that the Dynamic Trading Risk Factor trends — specifically that an AR(1) model can be fitted to it. This means that we can predict the next month's returns of the factor based on this months inferred value. But, and this is the problem, we typically only fully know a month's factor return at the end of the next month — and that's the month we're trying to predict.

If we could take an early estimate of the value we would record at the end of the current month, then we can make an estimte of the forecast for the current month with sufficient time to act upon that information. For example, we could time trades in one of the public listed securities that we have demonstrated have a high R² onto the factor.

Of course, for our market timing to work, it is necessary that the asset returns that are associated with the factor be delivered, to some extent, over the remaining part of the month. In the next post, we will seek to establish the validity of this proposition.