  This work extends a previous work in regime detection, which allowed trading
positions to be profitably adjusted when a new regime was detected, to ex ante
prediction of regimes, leading to substantial performance improvements over the
earlier model, over all three asset classes considered (equities, commodities,
and foreign exchange), over a test period of four years. The proposed new model
is also benchmarked over this same period against a hidden Markov model, the
most popular current model for financial regime prediction, and against an
appropriate index benchmark for each asset class, in the case of the
commodities model having a test period cost-adjusted cumulative return over
four times higher than that expected from the index. Notably, the proposed
model makes use of a contrarian trading strategy, not uncommon in the financial
industry but relatively unexplored in machine learning models. The model also
makes use of frequent short positions, something not always desirable to
investors due to issues of both financial risk and ethics; however, it is
discussed how further work could remove this reliance on shorting and allow the
construction of a long-only version of the model.
