I am trying to rank expected returns of stocks in a universe. This is a challenge because I use expected position sizes to determine expected returns (i.e., expected returns increase as transaction costs decrease; expected returns decrease as expected market impact increases).
The ways I thought to do this was to:
a. Compare a stock’s expected return against the position with the lowest expected return.
b. Pass parameters of the Portfolio (e.g., account balance, # of positions, etc) to a Ranking System.
My end goal is to pick stocks with the highest Kelly criteria in an equal weighted portfolio. However, unlike the basic academic examples that are provided in the literature, I am introducing transaction costs and non-constant rates of returns. I am a sucker for mental torture.