To be clear, my model is exactly the same as your model, I just changed one of the parameters—I changed the AI portfolio’s overall expected return from 4.7% to 1.3%.
It’s not intuitively obvious to me whether, given the 1.3%-return assumption, the optimal portfolio contains more AI than the global market portfolio. I know how I’d write a program to find the answer, but it’s complicated enough that I don’t want to do it right now.
(The way you’d do it is to model the correlation between the AI portfolio and the market, and set your assumptions such that the optimal value-neutral portfolio (given the two investments of “AI stocks” and “all other stocks”) equals the global market portfolio. Then write a utility function that assigns more utility to money in the short-timelines world and maximize that function where the independent variable is % allocation to each portfolio. You can do this with Python’s scipy.optimize, or any other similar library.)
EDIT: I wrote a spreadsheet to do this, see this comment
To be clear, my model is exactly the same as your model, I just changed one of the parameters—I changed the AI portfolio’s overall expected return from 4.7% to 1.3%.
It’s not intuitively obvious to me whether, given the 1.3%-return assumption, the optimal portfolio contains more AI than the global market portfolio. I know how I’d write a program to find the answer, but it’s complicated enough that I don’t want to do it right now.
(The way you’d do it is to model the correlation between the AI portfolio and the market, and set your assumptions such that the optimal value-neutral portfolio (given the two investments of “AI stocks” and “all other stocks”) equals the global market portfolio. Then write a utility function that assigns more utility to money in the short-timelines world and maximize that function where the independent variable is % allocation to each portfolio. You can do this with Python’s scipy.optimize, or any other similar library.)
EDIT: I wrote a spreadsheet to do this, see this comment