Momentum Turning Points

Garg, Ashish; Goulding, Christian L.; Harvey, Campbell R.; Mazzoleni, Michele

doi:10.2139/ssrn.3489539

Cited by 7 publications

(12 citation statements)

References 75 publications

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“…, where we are using the volatility scaling framework and r t−252,t is annual return. In an attempt to react more quickly to momentum turning points, Garg et al (2021)…”

Section: Momentum Strategies Review Classical Strategiesmentioning

confidence: 99%

“…At these turning points, momentum strategies are prone to performing poorly because they are unable to adapt quickly to this abrupt change in regime. This concept is explored by Garg et al (2021) who blended a slow momentum signal based on a long lookback window (LBW), such as 12 months, with a fast momentum signal based on a short LBW, such as 1 month. This approach is a balancing act between reducing noise and being quick enough to respond to turning points.…”

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confidence: 99%

“…This approach is a balancing act between reducing noise and being quick enough to respond to turning points. Adopting the terminology from Garg et al (2021), a bull or bear market is when the two momentum signals agree on a long or short position, respectively. If slow momentum suggests a long (short) position and fast momentum a short (long) position, we term this a correction (rebound) phase.…”

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confidence: 99%

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Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection

Wood¹,

Roberts²,

Zohren³

2021

JFDS

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n Momentum strategies, including deep learning-based deep momentum networks, have underperformed in recent years owing to difficulties in adjusting to rapid changes in the market, such as when a trend reverses from an uptrend to a downtrend, or vice versa.n Inserting an online changepoint detection module into a deep momentum network pipeline leads to large performance gains, especially during periods of significant nonstationarity, as observed in recent years.n The model achieves superior risk-adjusted returns by blending a slow momentum strategy with a fast mean-reversion strategy, with the changepoint detection module helping to balance the two in a data-driven manner.

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“…, where we are using the volatility scaling framework and r t−252,t is annual return. In an attempt to react more quickly to momentum turning points, Garg et al (2021)…”

Section: Momentum Strategies Review Classical Strategiesmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection

Wood¹,

Roberts²,

Zohren³

2021

JFDS

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“…We note that even DMNs have been underperforming in recent years, such as during the SARS-CoV-2 crisis, which can be attributed to the presence of nonstationarity or momentum turning points, where a trend breaks down [22]. Whilst the LSTM is good at learning local patterns [23], this architecture is known to struggle with long term patterns and responding to significant events, such as a market crash, which we term as regime change.…”

Section: Introductionmentioning

confidence: 99%

Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture

Wood¹,

Giegerich²,

Roberts³

et al. 2021

Preprint

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Deep learning architectures, specifically Deep Momentum Networks (DMNs) [1], have been found to be an effective approach to momentum and mean-reversion trading. However, some of the key challenges in recent years involve learning long-term dependencies, degradation of performance when considering returns net of transaction costs and adapting to new market regimes, notably during the SARS-CoV-2 crisis. Attention mechanisms, or Transformer-based architectures, are a solution to such challenges because they allow the network to focus on significant time steps in the past and longer-term patterns. We introduce the Momentum Transformer, an attentionbased architecture which outperforms the benchmarks, and is inherently interpretable, providing us with greater insights into our deep learning trading strategy. Our model is an extension to the LSTM-based DMN, which directly outputs position sizing by optimising the network on a risk-adjusted performance metric, such as Sharpe ratio. We find an attention-LSTM hybrid Decoder-Only Temporal Fusion Transformer (TFT) style architecture is the best performing model. In terms of interpretability, we observe remarkable structure in the attention patterns, with significant peaks of importance at momentum turning points. The time series is thus segmented into regimes and the model tends to focus on previous time-steps in alike regimes. We find changepoint detection (CPD) [2], another technique for responding to regime change, can complement multi-headed attention, especially when we run CPD at multiple timescales. Through the addition of an interpretable variable selection network, we observe how CPD helps our model to move away from trading predominantly on daily returns data. We note that the model can intelligently switch between, and blend, classical strategies -basing its decision on patterns in the data.

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“…At these turning points, momentum strategies are prone to performing poorly because they are unable to adapt quickly to this abrupt change in regime. This concept is explored in [21] where a slow momentum signal based on a long lookback window, such as 12 months, is blended with a fast momentum signal which is based on a short lookback window, such as 1 month. This approach is a balancing act between reducing noise and being quick enough to respond to turning points.…”

Section: Introduction Time-series (Ts) Momentummentioning

confidence: 99%

Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection

Wood,

Roberts,

Zohren

2021

Preprint

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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) [1] 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 meanreversion 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.K. Wood, S. Roberts and S. Zohren are with the Department of Engineering Science and the Oxford-

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Momentum Turning Points

Cited by 7 publications

References 75 publications

Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection

Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection

Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture

Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection

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