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

Wood, Kieran; Roberts, Stephen; Zohren, Stefan

doi:10.3905/jfds.2021.1.081

Cited by 13 publications

(9 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the figure, it can be observed that there are some crucial times when the negative correlation between all three models and the US MSCI Total Return Index coincides with periods of sustained negative equity performance. For example, we observe a dip in the 252-trading days rolling correlation, which becomes highly negative during periods of financial market stress, such as the Global Financial Crisis of -2009(Wood et al, 2022 and, more recently, the Coronavirus Crash of 2020 (Mazur et al, 2021). The longest bull market on record post-global financial crisis began in March 2009 and lasted nearly 11 years until the COVID-19 pandemic brought it to a close.…”

Section: Long-term Correlation With Equities Indexmentioning

confidence: 85%

Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning

Ong

Herremans

2022

SSRN Journal

View full text Add to dashboard Cite

Section: Long-term Correlation With Equities Indexmentioning

confidence: 85%

Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning

Ong

Herremans

2022

SSRN Journal

View full text Add to dashboard Cite

“…Thereafter, the Sharpe ratio is optimized through a long short-term memory (LSTM). Kieran et al take advantage of both Garg and Lim by introducing an online turning points detector to the DMN and achieves 33% higher than the standard time-series strategy on Sharpe ratio [11].…”

Section: Literature Surveymentioning

confidence: 99%

Exploit momentum in Cryptocurrency Market

Zhang¹

2023

BCPBM

View full text Add to dashboard Cite

Researchers put efforts into explanations of the momentum phenomenon and improvements of the momentum strategy since the emergence of momentum in 1993. Interested in anomalies appearing as exhibited in traditional asset markets, adequate studies are launched on the nascent phenomenon emergers in the last decade, the cryptocurrency market. Recent studies have shown that there is hardly any cross-sectional momentum in the cryptocurrency market. To explore the momentum anomaly additionally in the cryptocurrency market, this paper implemented a time-series momentum on cross-sectional winners for improvement. Previous studies have introduced detecting the turning point between long-term slow time-series factor and short-term fast time-series factor contributes to predicting the trend well. Furthermore, a threshold decided by a certain machine learning model suggests better performance. In this paper. A multilayer perceptron (MLP) is utilized to learn the weights of time-series factors. The combination of cross-sectional momentum and time-series momentum shows advantages and the MLP learned weighted strategy is preferable.

show abstract

“…Additionally, there are a lot of applications for regime changing point detection in time series in different areas, such as financial data analysis, etc. [28][29][30][31][32][33][34][35][36][37][38]. Detection of change in time series is a very widely investigated problem.…”

Section: State Of the Artmentioning

confidence: 99%

Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation

et al. 2022

View full text Add to dashboard Cite

In this paper, a novel method for long-term data segmentation in the context of machine health prognosis is presented. The purpose of the method is to find borders between three data segments. It is assumed that each segment contains the data that represent different statistical properties, that is, a different model. It is proposed to use a moving window approach, statistical parametrization of the data in the window, and simple clustering techniques. Moreover, it is found that features are highly correlated, so principal component analysis is exploited. We find that the probability density function of the first principal component may be sufficient to find borders between classes. We consider two cases of data distributions, Gaussian and α-stable, belonging to the class of non-Gaussian heavy-tailed distributions. It is shown that for random components with Gaussian distribution, the proposed methodology is very effective, while for the non-Gaussian case, both features and the concept of moving window should be re-considered. Finally, the procedure is tested for real data sets. The results provided here may be helpful in understanding some specific cases of machine health prognosis in the presence of non-Gaussian noise. The proposed approach is model free, and thus it is universal. The methodology can be applied for any long-term data where segmentation is crucial for the data processing.

show abstract

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

Cited by 13 publications

References 43 publications

Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning

Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning

Exploit momentum in Cryptocurrency Market

Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation

Contact Info

Product

Resources

About