英文标题：
《High-performance stock index trading: making effective use of a deep
  LSTM neural network》
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作者：
Chariton Chalvatzis, Dimitrios Hristu-Varsakelis
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最新提交年份：
2019
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英文摘要：
  We present a deep long short-term memory (LSTM)-based neural network for predicting asset prices, together with a successful trading strategy for generating profits based on the model\'s predictions. Our work is motivated by the fact that the effectiveness of any prediction model is inherently coupled to the trading strategy it is used with, and vise versa. This highlights the difficulty in developing models and strategies which are jointly optimal, but also points to avenues of investigation which are broader than prevailing approaches. Our LSTM model is structurally simple and generates predictions based on price observations over a modest number of past trading days. The model\'s architecture is tuned to promote profitability, as opposed to accuracy, under a strategy that does not trade simply based on whether the price is predicted to rise or fall, but rather takes advantage of the distribution of predicted returns, and the fact that a prediction\'s position within that distribution carries useful information about the expected profitability of a trade. The proposed model and trading strategy were tested on the S&P 500, Dow Jones Industrial Average (DJIA), NASDAQ and Russel 2000 stock indices, and achieved cumulative returns of 340%, 185%, 371% and 360%, respectively, over 2010-2018, far outperforming the benchmark buy-and-hold strategy as well as other recent efforts.
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中文摘要：
我们提出了一种基于深长短期记忆（LSTM）的神经网络来预测资产价格，以及一种基于模型预测产生利润的成功交易策略。我们的工作是基于这样一个事实，即任何预测模型的有效性都与它所使用的交易策略内在地耦合在一起，反之亦然。这突出了开发联合优化的模型和策略的困难，但也指出了比主流方法更广泛的调查途径。我们的LSTM模型结构简单，根据过去几个交易日的价格观察结果生成预测。该模型的体系结构经过调整，以提高盈利能力，而不是准确性，其策略不是简单地根据预测价格是上涨还是下跌进行交易，而是利用预测收益的分布，以及预测在该分布中的位置包含有关交易预期盈利能力的有用信息。拟议的模型和交易策略在标准普尔500指数、道琼斯工业平均指数（DJIA）、纳斯达克指数和罗素2000指数上进行了测试，在2010-2018年期间分别实现了340%、185%、371%和360%的累积回报，远超基准买入持有策略和其他近期努力。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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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：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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