英文标题：
《Using Deep Learning Neural Networks and Candlestick Chart Representation
  to Predict Stock Market》
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作者：
Rosdyana Mangir Irawan Kusuma, Trang-Thi Ho, Wei-Chun Kao, Yu-Yen Ou
  and Kai-Lung Hua
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最新提交年份：
2019
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英文摘要：
  Stock market prediction is still a challenging problem because there are many factors effect to the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment and economic factors. This work explores the predictability in the stock market using Deep Convolutional Network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a Convolutional Neural Network model. This Convolutional Neural Network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of stock market. The effectiveness of our method is evaluated in stock market prediction with a promising results 92.2% and 92.1% accuracy for Taiwan and Indonesian stock market dataset respectively. The constructed model have been implemented as a web-based system freely available at http://140.138.155.216/deepcandle/ for predicting stock market using candlestick chart and deep learning neural networks.
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中文摘要：
股市预测仍然是一个具有挑战性的问题，因为影响股市价格的因素很多，如公司新闻和业绩、行业业绩、投资者情绪、社交媒体情绪和经济因素。这项工作利用深度卷积网络和烛台图探索了股票市场的可预测性。该结果用于设计决策支持框架，交易员可以使用该框架提供未来股价方向的建议指示。我们使用各种类型的神经网络，如卷积神经网络、残差网络和视觉几何群网络来完成这项工作。根据股市历史数据，我们将其转换为烛台图。最后，这些烛台图将作为训练卷积神经网络模型的输入。这种卷积神经网络模型将帮助我们分析烛台图中的模式，并预测股票市场的未来走势。我们的方法在股市预测中的有效性得到了评估，对台湾和印尼股市数据集的预测准确率分别为92.2%和92.1%。构建的模型已实现为基于web的系统，可在http://140.138.155.216/deepcandle/用于使用烛台图和深度学习神经网络预测股市。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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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：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：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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