英文标题：
《Predicting Future Shanghai Stock Market Price using ANN in the Period
  21-Sep-2016 to 11-Oct-2016》
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作者：
Barack Wamkaya Wanjawa
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最新提交年份：
2016
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英文摘要：
  Predicting the prices of stocks at any stock market remains a quest for many investors and researchers. Those who trade at the stock market tend to use technical, fundamental or time series analysis in their predictions. These methods usually guide on trends and not the exact likely prices. It is for this reason that Artificial Intelligence systems, such as Artificial Neural Network, that is feedforward multi-layer perceptron with error backpropagation, can be used for such predictions. A difficulty in neural network application is the determination of suitable network parameters. A previous research by the author already determined the network parameters as 5:21:21:1 with 80% training data or 4-year of training data as a good enough model for stock prediction. This model has been put to the test in predicting selected Shanghai Stock Exchange stocks in the future period of 21-Sep-2016 to 11-Oct-2016, about one week after the publication of these predictions. The research aims at confirming that simple neural network systems can be quite powerful in typical stock market predictions.
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中文摘要：
预测任何股票市场的股票价格仍然是许多投资者和研究人员的追求。那些在股市交易的人倾向于在预测中使用技术、基本面或时间序列分析。这些方法通常指导趋势，而不是确切的可能价格。正是由于这个原因，人工智能系统，如人工神经网络，即具有误差反向传播的前馈多层感知器，可以用于此类预测。神经网络应用中的一个难点是确定合适的网络参数。作者之前的研究已经确定网络参数为5:21:21:1，有80%的训练数据或4年的训练数据可以作为股票预测的足够好的模型。该模型已在预测2016年9月21日至2016年10月11日的未来期间，即这些预测发布后约一周，对选定的上海证券交易所股票进行了测试。这项研究旨在证实，简单的神经网络系统在典型的股市预测中可能非常强大。
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分类信息：

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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