A scholar in Moscow, at National Research University Higher School of Economics, has proposed a trading strategy that combines ‘momentum’ and “contrarian’ strategies in a way that, this scholar believes, could reap the upsides of both.
The scholar, Victoria Dobrynskaya, assistant professor of finance, says that momentum strategies do produce alpha, or as she puts it produces high “returns [that] cannot be explained by risk factors,” but they are subject to “occasional severe crashes” when momentum suddenly reverses.
That’s a common enough observation. Her work exploring this situation continues that of Kent Daniel, Ravi Jagannathan, and Soohun Kim, three U.S. based scholars who have written on “tail risk in momentum strategy returns.” Daniel et al developed a hidden Markov model of the unobserved turbulent state impacting returns on the momentum strategy.
Markov Models
By way of review, a “Markov model” is one that assumes that the probability of future states depend only upon the present state, regardless of what has come before, just as the outcome of a particular coin flip owes nothing to the string of coin flips that may have preceded it. A “hidden Markov model” is one that postulates a present state that is only partially observable.
As Dobrynskaya observes, Daniel’s model postulates both calm and turbulent states in its Markov chain, and forecasts large momentum losses in the turbulent states. She also says that Daniel’s model does a better job forecasting these losses “than alternative forecasting models suggested by the literature.”
Based on such prior work, Dobrynskaya postulates a simple strategy: a portfolio manager might switch to a contrarian strategy one month after a significant market loss, hold that position for three months, then switch back to the momentum position if no new market crash has occurred in that period.
