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Financial instability analysis using ANN and feature selection technique: Application to stock market price prediction

机译:基于神经网络和特征选择技术的金融不稳定性分析:在股票价格预测中的应用

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摘要

Nowadays, Demand of forecasting stock market price is increasing at a higher rate than the ever before as more people are getting connected to the stock business. Many criteria play more or less strong inductive role over the stock market, the trend and price always keep changing here. So, it is challenging to predict exact price value. But some Data mining and Machine learning techniques can be implemented to do this challenging task to predict stock market price and trend. In this study, Artificial Neural Network (ANN) is used along with windowing operator; which is highly efficient for working with time series data for predicting stock market price and trend. This study is done on Wal-Mart Stores Inc. (WMT) a listed company of New York Stock Exchange. Five years historical dataset (2010-2015) is used to undertake the experiments of this study. According to the result of this study Artificial Neural Network (ANN) can produce a rational result with a small error.
机译:如今,随着越来越多的人与股票业务建立联系,预测股票市场价格的需求正以前所未有的速度增长。许多标准或多或少在股票市场上起着重要的归纳作用,趋势和价格始终在这里变化。因此,预测准确的价格值具有挑战性。但是可以实施一些数据挖掘和机器学习技术来完成这项具有挑战性的任务,以预测股市价格和趋势。在这项研究中,人工神经网络(ANN)与开窗算子一起使用。这对于处理时间序列数据以预测股市价格和趋势非常有效。这项研究是在纽约证券交易所的上市公司沃尔玛(WMT)上进行的。使用五年历史数据集(2010-2015)进行这项研究的实验。根据这项研究的结果,人工神经网络(ANN)可以产生合理的结果且误差很小。

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