英文标题：
《Evaluating the Performance of ANN Prediction System at Shanghai Stock
  Market 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：
  This research evaluates the performance of an Artificial Neural Network based prediction system that was employed on the Shanghai Stock Exchange for the period 21-Sep-2016 to 11-Oct-2016. It is a follow-up to a previous paper in which the prices were predicted and published before September 21. Stock market price prediction remains an important quest for investors and researchers. This research used an Artificial Intelligence system, being an Artificial Neural Network that is feedforward multi-layer perceptron with error backpropagation for prediction, unlike other methods such as technical, fundamental or time series analysis. While these alternative methods tend to guide on trends and not the exact likely prices, neural networks on the other hand have the ability to predict the real value prices, as was done on this research. Nonetheless, determination of suitable network parameters remains a challenge in neural network design, with this research settling on a configuration of 5:21:21:1 with 80% training data or 4-year of training data as a good enough model for stock prediction, as already determined in a previous research by the author. The comparative results indicate that neural network can predict typical stock market prices with mean absolute percentage errors that are as low as 1.95% over the ten prediction instances that was studied in this research.
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中文摘要：
本研究评估了2016年9月21日至2016年10月11日期间上海证券交易所使用的基于人工神经网络的预测系统的性能。这是之前一篇论文的后续，该论文预测了价格，并在9月21日之前发表。股票市场价格预测仍然是投资者和研究人员的一项重要任务。本研究使用了一个人工智能系统，这是一个人工神经网络，它是前馈多层感知器，带有误差反向传播用于预测，与技术、基础或时间序列分析等其他方法不同。虽然这些替代方法倾向于指导趋势，而不是准确的可能价格，但另一方面，神经网络具有预测实际价值价格的能力，正如本研究所做的那样。尽管如此，确定合适的网络参数仍然是神经网络设计中的一个挑战，正如作者在之前的研究中已经确定的那样，本研究确定的配置为5:21:21:1，有80%的训练数据或4年的训练数据，作为股票预测的足够好的模型。比较结果表明，在本研究所研究的十个预测实例中，神经网络能够预测典型的股票市场价格，平均绝对百分比误差低至1.95%。
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分类信息：

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：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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