Let's assume it is March and my illiquid private assets portfolio is only 50% marked for 12/31, but I want to get the most accurate estimate of my final return for the quarter ended on 12/31.
What is the state of the art for using known returns for that quarter to estimate the return on assets that I have not yet received a valuation for?
I'm looking to try training a neural network to do this sort of estimation, but I'm having trouble finding research along these lines to start from. What types and architectures of neural networks are most suitable to reach the highest estimation accuracy?
Any suggestions for key phrases to search for or pointers to relevant research (NN related or not) on this topic would be much appreciated.
To clarify further based on comments, within the illiquid private assets there is no real structural difference between the marked and unmarked assets. Valuations trickle in over time rather than being known in real time like publicly traded assets. So between 12/31 and, say, 3/31 the private asset portfolio will go from 0% marked for that quarter to 100% marked in dribs and drabs.
The data available to train with would be quarterly time series of returns going back a few decades. I tend to agree with the comments that there probably aren't enough sets of time series data to train a great NN since the number to work with would be in the low thousands.
The reason I was thinking of neural networks was partly just curiosity, but partly that linear regressions seem too brittle to handle fluctuations in market conditions very well. There are no known non-linear relationships, but linear relationships don't seem to tell the whole story, though maybe I just need to use fancier residual techniques and lagged returns structures.