SHORT COMMUNICATION Intraday Stock Prediction Based on Deep Neural Network Nagaraj Naik 1 • Biju R. Mohan 1 Received: 8 October 2018 / Revised: 8 November 2019 / Accepted: 22 November 2019 Ó The National Academy of Sciences, India 2019 Abstract 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 can- dlestick 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 plat- form. 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. Keywords Technical indicators Á DNN Á Classification Á Candlestick pattern Introduction Due to the volatile nature of the stock market, predicting stock price movements for intraday trade is a challenging task. Interpretation of stock-related critical information makes trading more profitable [1]. The efficient market hypothesis (EMH) states that the prices of trade assets such as stocks already reflect all publicly available information, and thus, if your investments are based on publicly avail- able information, you will not systematically outperform the market over time. Investors are making money by either taking a chance or making riskier investments [13]. However, most of the stock-related studies [4, 5] stated that the stock market can be predicted. Stock analysis can be carried out in two ways. The first is fundamental analysis, and the second is technical analysis. The fundamental analysis involves an examination of financial data, man- agement, business track record, competition, earnings, macroeconomic factors, and overall economic conditions. The technical analysis is a method of making stock mea- surements based on historical price. This method involves various statistical analyses of stock data based on stock prices. The extant literature suggests that the technical indicator is an essential parameter for evaluating stock prices [4, 7]. The first contribution of this paper is the identification of a trend in data by using a combination of candlestick data and technical indicators. The existing research has not considered these combinations. The second contribution of this paper is using DNN to classify and accurately predict a stock price’s up and down movements. The existing research is based on a three-layer artificial neural network (ANN), which is unable to classify the up and down movements of stock prices. & Nagaraj Naik nagaraj21.naik@gmail.com Biju R. Mohan biju@nitk.edu.in 1 Department of Information Technology, National Institute of Technology, Surathkal, Karnataka, India 123 Natl. Acad. Sci. Lett. https://doi.org/10.1007/s40009-019-00859-1