{"id":12461,"date":"2019-08-01T09:27:10","date_gmt":"2019-08-01T13:27:10","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=12461"},"modified":"2024-05-15T10:30:23","modified_gmt":"2024-05-15T14:30:23","slug":"deep-neural-networks-to-enhance-time-series-momentum","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/deep-neural-networks-to-enhance-time-series-momentum\/","title":{"rendered":"Using Deep Neural Networks to Enhance Time Series Momentum"},"content":{"rendered":"\n

Authors:<\/strong> Lim, Zohren, Roberts
Title: <\/strong>Enhancing Time Series Momentum Strategies Using Deep Neural Networks
Link:<\/strong> https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3369195<\/a><\/p>\n\n\n\n

Abstract:<\/strong><\/p>\n\n\n\n

While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule. In this paper, we introduce Deep Momentum Networks — a hybrid approach which injects deep learning based trading rules into the volatility scaling framework of time series momentum. The model also simultaneously learns both trend estimation and position sizing in a data-driven manner, with networks directly trained by optimising the Sharpe ratio of the signal. Backtesting on a portfolio of 88 continuous futures contracts, we demonstrate that the Sharpe-optimised LSTM improved traditional methods by more than two times in the absence of transactions costs, and continue outperforming when considering transaction costs up to 2-3 basis points. To account for more illiquid assets, we also propose a turnover regularisation term which trains the network to factor in costs at run-time.<\/p>\n\n\n\n

Notable quotations from the academic research paper:<\/strong><\/p>\n\n\n\n

“While numerous papers have investigated the use of machine learning for financial time series prediction, they typically focus on casting the underlying prediction problem as a standard regression or classification task \u2013 with regression models forecasting expected returns, and classification models predicting the direction of future price movements. This approach, however, could lead to suboptimal performance in the context time-series momentum for several reasons.”<\/p>\n\n\n\n

“In this paper, we introduce a novel class of hybrid models that combines deep learning-based trading signals with the volatility scaling framework used in time series momentum strategies \u2013 which we refer to as the Deep Momentum Networks (DMNs). This improves existing methods from several angles.” <\/p>\n\n\n\n

“Firstly, by using deep neural networks to directly generate trading signals, we remove the need to manually specify both the trend estimator and position sizing methodology \u2013 allowing them to be learnt directly using modern time series prediction architectures.”<\/p>\n\n\n\n

\"Using
Image Source: Quantpedia – Using Deep Neural Networks to Enhance Time Series Momentum<\/figcaption><\/figure>\n\n\n\n

To learn more about this paper, view the full article on Quantpedia website:<\/em>
https:\/\/quantpedia.com\/Blog\/Details\/using-deep-neural-networks-to-enhance-time-series-momentum<\/a> <\/p>\n","protected":false},"excerpt":{"rendered":"

While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule. In this paper, we introduce Deep Momentum Networks — a hybrid approach which injects deep learning based trading rules into the volatility scaling framework of time series momentum.<\/p>\n","protected":false},"author":186,"featured_media":12470,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,338,341,351,344],"tags":[851,2105,2697,852,494,2698,2699],"contributors-categories":[13662],"class_list":{"0":"post-12461","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-ibkr-quant-news","9":"category-quant-development","10":"category-quant-europe","11":"category-quant-regions","12":"tag-algo-trading","13":"tag-deep-learning","14":"tag-deep-neural-networks","15":"tag-machine-learning","16":"tag-quant","17":"tag-time-series-analysis","18":"tag-time-series-momentum","19":"contributors-categories-quantpedia"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nUsing Deep Neural Networks to Enhance Time Series Momentum<\/title>\n<meta name=\"description\" content=\"Quantpedia discuss Deep Momentum Networks -- a hybrid approach related to deep learning based trading rules and time series momentum.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.interactivebrokers.com\/campus\/wp-json\/wp\/v2\/posts\/12461\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Using Deep Neural Networks to Enhance Time Series Momentum - 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