英文标题：
《Lagged correlation-based deep learning for directional trend change
  prediction in financial time series》
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作者：
Ben Moews, J. Michael Herrmann, Gbenga Ibikunle
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最新提交年份：
2018
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英文摘要：
  Trend change prediction in complex systems with a large number of noisy time series is a problem with many applications for real-world phenomena, with stock markets as a notoriously difficult to predict example of such systems. We approach predictions of directional trend changes via complex lagged correlations between them, excluding any information about the target series from the respective inputs to achieve predictions purely based on such correlations with other series. We propose the use of deep neural networks that employ step-wise linear regressions with exponential smoothing in the preparatory feature engineering for this task, with regression slopes as trend strength indicators for a given time interval. We apply this method to historical stock market data from 2011 to 2016 as a use case example of lagged correlations between large numbers of time series that are heavily influenced by externally arising new information as a random factor. The results demonstrate the viability of the proposed approach, with state-of-the-art accuracies and accounting for the statistical significance of the results for additional validation, as well as important implications for modern financial economics.
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中文摘要：
具有大量噪声时间序列的复杂系统中的趋势变化预测是现实世界现象的许多应用中的一个问题，股票市场是这类系统中一个众所周知的难以预测的例子。我们通过它们之间的复杂滞后相关性来预测方向趋势变化，从各自的输入中排除关于目标序列的任何信息，以实现纯粹基于与其他序列的此类相关性的预测。我们建议在该任务的预备特征工程中使用深度神经网络，该网络采用指数平滑的逐步线性回归，回归斜率作为给定时间间隔的趋势强度指标。我们将此方法应用于2011年至2016年的历史股市数据，作为大量时间序列之间滞后相关性的用例示例，这些时间序列作为随机因素受到外部产生的新信息的严重影响。结果证明了所提出方法的可行性，具有最先进的精确度，并考虑了结果的统计显著性，以供进一步验证，以及对现代金融经济学的重要影响。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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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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