英文标题：
《Enhancing Time Series Momentum Strategies Using Deep Neural Networks》
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作者：
Bryan Lim, Stefan Zohren, Stephen Roberts
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最新提交年份：
2020
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英文摘要：
  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.
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中文摘要：
虽然时间序列动量在金融学中是一个研究得很好的现象，但常见的策略需要明确定义趋势估计量和头寸大小规则。在本文中，我们引入了深度动量网络——一种混合方法，它将基于深度学习的交易规则注入到时间序列动量的波动率标度框架中。该模型还以数据驱动的方式同时学习趋势估计和位置调整，通过优化信号的夏普比直接训练网络。通过对88份连续期货合约的投资组合进行回溯测试，我们证明，在没有交易成本的情况下，夏普优化的LSTM将传统方法改进了两倍以上，并且在考虑到高达2-3个基点的交易成本时，继续跑赢大盘。为了考虑更多的非流动资产，我们还提出了一个营业额调整条款，该条款培训网络在运行时考虑成本。
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分类信息：

一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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