Momentum is considered one of the financial factors that shows consistency throughout time. Other factors come and go, but not so with momentum. This does not mean that momentum will work at all times. Additionally, momentum is subject to significant risks. It depends on the regime, with momentum profits greater during an expansion than during a recession. There is also a greater likelihood of crash risk with momentum trades. This is the premium investors are paid to hold assets showing momentum. Finally, it is found that momentum exhibits a bimodal distribution of relative returns. Past winners may be likely to persist, but there is also a greater likelihood of becoming losers. This U-shaped distribution creates specific risks when holding momentum stocks.
The paper, "Bimodality Everywhere: International Evidence of Deep Momentum,” explores this specific momentum feature by using a deep momentum technique (RET) to mitigate or exploit it. Deep momentum is a two-step process: first, neural networks generate probabilities of future return declines; second, stock performance is based on returns or the Sharpe ratio to form long-short portfolios. It is shown that using specific machine learning techniques yields significant improvements over traditional methods for forming momentum portfolios.
There is a lot going on with this paper, so the devil is in the details, but it shows that machine learning can be used to improve over a simple momentum strategy.
No comments:
Post a Comment