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首页> 外文期刊>Decision Science Letters >Resilient back-propagation algorithm, technical analysis and the predictability of time series in the financial industry
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Resilient back-propagation algorithm, technical analysis and the predictability of time series in the financial industry

机译：金融行业的弹性反向传播算法，技术分析和时间序列的可预测性

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摘要

In financial industry, the accurate forecasting of the stock market is a major challenge to optimize and update portfolios and also to evaluate several financial derivatives. Artificial neural networks and technical analysis are becoming widely used by industry experts to predict stock market moves. In this paper, different technical analysis measures and resilient back-propagation neural networks are used to predict the price level of five major developed international stock markets, namely the US S & P500, Japanese Nikkei, UK FTSE100, German DAX, and the French CAC40. Four categories of technical analysis measures are compared. They are indicators, oscillators, stochastics, and indexes. The out-of-sample simulation results show a strong evidence of the effectiveness of the indicators category over the oscillators, stochastics, and indexes. In addition, it is found that combining all these measures lead to an increase of the prediction error. In sum, technical analysis indicators provide valuable information to predict the S & P500, Nikkei, FTSE100, DAX, and the CAC40 price level.
机译：在金融行业，准确预测股票市场是优化和更新投资组合以及评估几种金融衍生产品的主要挑战。人工神经网络和技术分析已被行业专家广泛用来预测股市走势。在本文中，使用不同的技术分析方法和弹性反向传播神经网络来预测五个主要发达国际股票市场的价格水平，即美国S＆P500，日本日经指数，英国FTSE100，德国DAX和法国CAC40 。比较了四类技术分析措施。它们是指标，震荡指标，随机指标和指标。样本外仿真结果显示了指标类别在震荡指标，随机指标和指标上的有效性的有力证据。另外，发现结合所有这些措施导致预测误差的增加。总之，技术分析指标可提供有价值的信息，以预测S＆P500，日经指数，FTSE100，DAX和CAC40的价格水平。

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