  In this paper, we revisit the parameter learning problem, namely the
estimation of model parameters for Dynamic Bayesian Networks (DBNs). DBNs are
directed graphical models of stochastic processes that encompasses and
generalize Hidden Markov models (HMMs) and Linear Dynamical Systems (LDSs).
Whenever we apply these models to economics and finance, we are forced to make
some modeling assumptions about the state dynamics and the graph topology (the
DBN structure). These assumptions may be incorrectly specified and contain some
additional noise compared to reality. Trying to use a best fit approach through
maximum likelihood estimation may miss this point and try to fit at any price
these models on data. We present here a new methodology that takes a radical
point of view and instead focus on the final efficiency of our model.
Parameters are hence estimated in terms of their efficiency rather than their
distributional fit to the data. The resulting optimization problem that
consists in finding the optimal parameters is a hard problem. We rely on
Covariance Matrix Adaptation Evolution Strategy (CMA-ES) method to tackle this
issue. We apply this method to the seminal problem of trend detection in
financial markets. We see on numerical results that the resulting parameters
seem less error prone to over fitting than traditional moving average cross
over trend detection and perform better. The method developed here for
algorithmic trading is general. It can be applied to other real case
applications whenever there is no physical law underlying our DBNs.
