Slow Momentum with Fast Reversion: A Trading Strategy Using Deep
Learning and Changepoint Detection
- URL: http://arxiv.org/abs/2105.13727v1
- Date: Fri, 28 May 2021 10:46:53 GMT
- Title: Slow Momentum with Fast Reversion: A Trading Strategy Using Deep
Learning and Changepoint Detection
- Authors: Kieran Wood, Stephen Roberts, Stefan Zohren
- Abstract summary: We introduce an online change-point detection (CPD) module into a Deep Momentum Network (DMN) pipeline.
Our CPD module outputs a changepoint location and severity score, allowing our model to learn to respond to degrees of disequilibrium.
Using a portfolio of 50, liquid, continuous futures contracts over the period 1990-2020, the addition of the CPD module leads to an improvement in Sharpe ratio of $33%$.
- Score: 2.9005223064604078
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Momentum strategies are an important part of alternative investments and are
at the heart of commodity trading advisors (CTAs). These strategies have
however been found to have difficulties adjusting to rapid changes in market
conditions, such as during the 2020 market crash. In particular, immediately
after momentum turning points, where a trend reverses from an uptrend
(downtrend) to a downtrend (uptrend), time-series momentum (TSMOM) strategies
are prone to making bad bets. To improve the response to regime change, we
introduce a novel approach, where we insert an online change-point detection
(CPD) module into a Deep Momentum Network (DMN) [1904.04912] pipeline, which
uses an LSTM deep-learning architecture to simultaneously learn both trend
estimation and position sizing. Furthermore, our model is able to optimise the
way in which it balances 1) a slow momentum strategy which exploits persisting
trends, but does not overreact to localised price moves, and 2) a fast
mean-reversion strategy regime by quickly flipping its position, then swapping
it back again to exploit localised price moves. Our CPD module outputs a
changepoint location and severity score, allowing our model to learn to respond
to varying degrees of disequilibrium, or smaller and more localised
changepoints, in a data driven manner. Using a portfolio of 50, liquid,
continuous futures contracts over the period 1990-2020, the addition of the CPD
module leads to an improvement in Sharpe ratio of $33\%$. Even more notably,
this module is especially beneficial in periods of significant nonstationarity,
and in particular, over the most recent years tested (2015-2020) the
performance boost is approximately $400\%$. This is especially interesting as
traditional momentum strategies have been underperforming in this period.
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