  Building the future profit and loss (P&L) distribution of a portfolio
holding, among other assets, highly non-linear and path-dependent derivatives
is a challenging task. We provide a simple machinery where more and more assets
could be accounted for in a simple and semi-automatic fashion. We resort to a
variation of the Least Square Monte Carlo algorithm where interpolation of the
continuation value of the portfolio is done with a feed forward neural network.
This approach has several appealing features not all of them will be fully
discussed in the paper. Neural networks are extremely flexible regressors. We
do not need to worry about the fact that for multi assets payoff, the exercise
surface could be non connected. Neither we have to search for smart regressors.
The idea is to use, regardless of the complexity of the payoff, only the
underlying processes. Neural networks with many outputs can interpolate every
single assets in the portfolio generated by a single Monte Carlo simulation.
This is an essential feature to account for the P&L distribution of the whole
portfolio when the dependence structure between the different assets is very
strong like the case where one has contingent claims written on the same
underlying.
