英文标题：
《On Feature Reduction using Deep Learning for Trend Prediction in Finance》
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作者：
Luigi Troiano and Elena Mejuto and Pravesh Kriplani
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最新提交年份：
2017
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英文摘要：
  One of the major advantages in using Deep Learning for Finance is to embed a large collection of information into investment decisions. A way to do that is by means of compression, that lead us to consider a smaller feature space. Several studies are proving that non-linear feature reduction performed by Deep Learning tools is effective in price trend prediction. The focus has been put mainly on Restricted Boltzmann Machines (RBM) and on output obtained by them. Few attention has been payed to Auto-Encoders (AE) as an alternative means to perform a feature reduction. In this paper we investigate the application of both RBM and AE in more general terms, attempting to outline how architectural and input space characteristics can affect the quality of prediction.
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中文摘要：
使用金融深度学习的一个主要优势是将大量信息嵌入到投资决策中。一种方法是通过压缩，这使我们考虑更小的特征空间。多项研究证明，深度学习工具进行的非线性特征约简在价格趋势预测中是有效的。重点主要放在受限玻耳兹曼机器（RBM）及其获得的输出上。很少有人关注自动编码器（AE）作为执行特征缩减的替代方法。在本文中，我们从更一般的角度研究了RBM和AE的应用，试图概述建筑和输入空间特征如何影响预测质量。
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分类信息：

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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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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