Predicting stock price movements is difficult due to the speculative nature of the stock market. Accurate predictions of stock prices allow traders to increase their profits. Stock prices react when receiving new information. During the trading day, it is difficult to understand the up and down movements signaled by stock prices. This paper addresses the problem of fluctuations in stock prices. We proposed the method to identify stock movement trend in data, and this method considered the combination of candlestick data and technical indicator values. The outcome of this method is given as inputs to a deep neural network (DNN) to classify a stock price’s up and down movements. National Stock Exchange, India, datasets are considered for an experiment from the years 2008 to 2018. The work is carried out using H2O deep learning on an RStudio platform. Experimental results are compared with a three-layer artificial neural network (ANN) model. The proposed five-layer DNN model outperforms state-of-the-art methods by 8–11% in predicting up and down movements of a given stock.

中文翻译：

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基于深度神经网络的日内库存预测

由于股票市场的投机性，很难预测股票价格的走势。准确的股价预测可以使交易者增加利润。收到新信息时，股票价格会做出反应。在交易日中，很难理解股价所暗示的上下运动。本文解决了股价波动的问题。我们提出了一种识别数据中股票走势的方法，该方法考虑了烛台数据和技术指标值的组合。该方法的结果被作为对深度神经网络（DNN）的输入，以对股价的上下波动进行分类。考虑使用印度国家证券交易所的数据集进行2008年至2018年的实验。这项工作是在RStudio平台上使用H2O深度学习进行的。将实验结果与三层人工神经网络（ANN）模型进行了比较。建议的五层DNN模型在预测给定股票的上下运动方面比最新方法高出8-11％。