英文标题：
《Thresholded ConvNet Ensembles: Neural Networks for Technical Forecasting》
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作者：
Sid Ghoshal, Stephen J. Roberts
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最新提交年份：
2018
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英文摘要：
  Much of modern practice in financial forecasting relies on technicals, an umbrella term for several heuristics applying visual pattern recognition to price charts. Despite its ubiquity in financial media, the reliability of its signals remains a contentious and highly subjective form of \'domain knowledge\'. We investigate the predictive value of patterns in financial time series, applying machine learning and signal processing techniques to 22 years of US equity data. By reframing technical analysis as a poorly specified, arbitrarily preset feature-extractive layer in a deep neural network, we show that better convolutional filters can be learned directly from the data, and provide visual representations of the features being identified. We find that an ensemble of shallow, thresholded CNNs optimised over different resolutions achieves state-of-the-art performance on this domain, outperforming technical methods while retaining some of their interpretability.
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中文摘要：
现代财务预测的许多实践都依赖于技术，这是将视觉模式识别应用于价格图的几种启发式方法的总称。尽管其在金融媒体中无处不在，但其信号的可靠性仍然是一种有争议的、高度主观的“领域知识”形式。我们将机器学习和信号处理技术应用于22年的美国股票数据，研究金融时间序列中模式的预测价值。通过将技术分析重新定义为深度神经网络中规定性差、任意预设的特征提取层，我们表明可以直接从数据中学习更好的卷积滤波器，并提供被识别特征的视觉表示。我们发现，在不同分辨率下优化的浅层阈值CNN集合在这一领域取得了最先进的性能，优于技术方法，同时保留了它们的一些可解释性。
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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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