JOURNAL OF CRITICAL REVIEWS ISSN- 2394-5125 VOL 7, ISSUE 16, 2020 LONG SHORT-TERM MEMORY DEEP NETWORK AND MACHINE LEARNING APPROACH IN ONE-DAY AHEAD STOCK MARKET PREDICTION 1P.V.CHANDRIKA, 2K. SAKTHI SRINIVASAN 1Research Scholar, VIT Business School, Vellore Institute of Technology, Vellore. 2Professor, VIT Business School, Vellore Institute of Technology, Vellore. Abstract The emergence of industry 4.0 with machine learning, deep learning and artificial intelligence have motivated the interest of many of the researchers in applying these techniques in various domains. There are lot of studies which has proven the strong evidences in predicting the stock market prices using traditional techniques of regression, ARIMA, ARCH and GARCH, The present research paper Deals with Machine Learning (ML) techniques using ARIMA, and Support Vector Machine (SVM) algorithms. Also, ANN, RNN and LSTM are used in Deep Learning Techniques to predict the price movement. To predict, stock indices of DJIA, NIFTY50, S&P 500, KOSPI, SSE are used. Selected technical indicators are used in the model to forecast the price movement for the next day. Appropriate performance metrics are applied and the results compared with traditional approach ARIMA. The results prove that SVM outperforms ARIMA in ML techniques and RNN-LSTM is more accurate than ANN. Various technical indicators are been use to predict the direction of the stock indices and to find the closing price of the stock index for next one day time period. The models are been evaluated based on the various performance metrics of the algorithms and are been compared with the traditional techniques of ARIMA. The results show that for machine learning algorithms Support vector machines outperformed ARIMA and for deep learning models RNN-LSTM outperformed ANN. Keywords: Time Series Forecasting, Machine Learning, Deep Learning, ARIMA, SVM, ANN, RNN, LSTM 1. Introduction Stock Market is found to be volatile and dynamic in nature. Applying the techniques of machine learning, deep learning to such an unstable, volatile market is quite challenging. To evaluate whether the techniques of machine learning and deep learning will help in predicting the stock market movements have enhanced the interest of researchers. In fact there are many traditional theories which tried to explain the stock market movements but there is no single theory that supports the volatility and reason pertaining to changes in the stock prices. Usually there are three methods in which the stock price movements can be studied, those are 1. Fundamental Analysis 2. Technical Analysis and 3. Times series Forecasting. 1.1 Fundamental Analysis: Fundamental analysis makes use of Market value of companies financial and competitive strength in order to predict future movements of an asset. It is a method of measuring security’s intrinsic value by examining related economic and financial factors. It helps an investor to compare with security’s current price and to check whether a security is undervalued or overvalued. Hence it is a way of determining fair market value of a stock. 1.2 Technical Analysis: Technical Analysis is a way of representing the price and volume movements of stocks using charts and graphs. It helps in finding out the trends and patterns of the stocks and helps in forecasting the price movements. The charts are built based on certain statistical analysis. The theory which is not in agreement with fundamental and technical analysis is Efficient Market Hypothesis (EMH). EMH proposes that it is impossible to beat the market through fundamental and technical analysis, because the market efficiently prices all the stocks on ongoing basis and any opportunity with excess returns are grabbed by market participants, so there is no chance of outperformance in the market during the long run. 2501 JOURNAL OF CRITICAL REVIEWS ISSN- 2394-5125 VOL 7, ISSUE 16, 2020 1.3 Time Series Forecasting: Time Series Forecasting techniques Are used on historical time period data and takes into consideration different forecasting techniques. Techniques which are popular in this method include: 1. Moving Average 2. Exponential Smoothing 3. Auto Regressive Moving Average (ARMA) 4. Auto Regressive Integrated Moving Average (ARIMA) Prediction accuracy for stock index direction however differs in each model. When the techniques of machine learning and deep learning are used for prediction, many studies propose that neural networks with hybrid models perform better in predicting the stock market direction[ ]. Studies[ ] also propose hybrid models integrating ML and DNN are much superior when technical indicators are taken as input features. This study attempts to predict the direction of stock indices using ML and DLN techniques. In ML, Support Vector Machines (SVM), Logistic Regression(LR) and Auto Regressive Integrated Moving Average (ARIMA) are used as the preferred algorithms and in DNN, Multi-Layer Perceptron (MLP), Artificial Neural Network (ANN), Deep Neural Network (DNN), Recurrent Neural Network (RNN) are preferred as input algorithms. In addition, this research examines performance of RNN-LSTM as a hybrid tool in determining the index movement. Finally a comparison on accuracy is made on hybrid model of RNN-LSTM with ML models. The remainder of the paper is organized as follows. Past related works are described in Section 2, objectives of the study and limitations are presented in Section 3. Data description, feature engineering and pre-processing methods and modelling are discussed in Section 4, a brief on different algorithms are also detailed. In Section 5 results of the models used are summarized and in section 6 we present the concluding remarks 2. Related Works 2.1 Econometric Model Earlier approach adopted in stock market forecasting is confined to basic statistical application and econometric modelling. Later, econometric method are adopted as the basic algorithms in ML techniques to improve the accuracy level. Still, the effectiveness of this method proven short of prediction power, which is highlighted in this section as summarized from survey of available literature. The ARIMA models work well on stationary time series data but can be converted back to original series by using some transformations [1]. Adebiyi, Adewumi, and Ayo[2014] used ARIMA for predicting short and long term movement in New York Stock Exchange and Nigerian stock Exchange. Performance of model in predicting short term movement are better than long term. In another work, Ali et al [2014], finds neural network outperforming ARIMA. When time series data is modelled using ARIMA and GARCH, Indian stock market is found to be outperformed on hybrid models of ARIMA and GARCH when compared with traditional technique of ARIMA [4]. Decision on buy and sell strategy proved profitable when ANN is used as predicting model [Bannerjee, 2014]. Steel [2014], finds hybrid model of ARIMA is a better predictor than traditional ARIMA in finding the stock index movement of Germany, UK, France US, Japan and Australian stock exchanges. His study proved to be good in short term prediction. 2.2 Support Vector Machine Power of prediction is better when SVM is used in finding the direction of stock price and index [ Patel, Shah, Thakkar and, Kotecha,2015], adoption of ANN, SVM, RF and Naïve Bayes with technical indicators pronounce the superior performance of SVM in predicting the direction of SNX Nifty and S&P500. An error below 1 per cent is evident in SVR application for long term prediction for ten year period in NSE listed companies and NIFTY, Hore, Vipani, Das and Dutta [2018]. Swell[2017], finds SVM a better predictor compared to other traditional algorithms for forecasting DJIA daily returns. 2502 JOURNAL OF CRITICAL REVIEWS ISSN- 2394-5125 VOL 7, ISSUE 16, 2020 Buy and sell decision is easy when MLP and LR classification is exercised, and proves its worth with high accuracy rate where SVM outperforms [Dingali and Fourerir, 2017]. 2.3 Neural Network: Neural Network applied with back propagation show an improved accuracy, but it is much effective in a hybrid model embedded with Genetic Algorithm. GA-ANN outperforms compared to ANN in predicting the movement of Nikkei 225 index for monthly observation [Qiu, 2014]. MLP is similar to ANN in forward propagation and DNN, wherein it is termed as Convolution Neural Network(CNN), the results of MLP shows better accuracy when used along with LR, and Naïve Bayes, but a shade underperformer when compared to DNN[Gilberto, 2015]. Krollner, Vanstone and, Finnie [2010] finds ANN as a dominating algorithm in ML for stock market forecasting. Accuracy of ANN is 89.65 per cent when used in Indian stock market index, S&P CNX and Nifty 50 to understand the direction, but it shows a lesser accuracy of 69.72 per cent if a long period is taken for forecasting [Majumder and Hussain, 2015] Kumar and Sharma[2016] finds a high accuracy of 99 per cent using ANN in Nifty 50. Alotaibi et al.[2018] finds ANN with back propagation capable of predicting Saudi Stock market and Oil prices. Jerzy Korczak [2017] developed a hybrid model of Artificial Neural Network with Principle Component Analysis which is called as Agent trader (A-trader) System to forecast the prices of Talit and NASDAQ stock index. Naeini[2010] used MLP and Elman Recurrent Network and finds MLP underperforming. Taking technical indicators as input feature and training the ANN gives an accuracy of 60.87 percent in predicting the Nikkei 225 index [Qui and Song, 2016]. Panda and Narashiman[2006] reveal the superior performance of ANN over linear auto regressive models. This is observed in their findings when applied in BSE stocks. In one study, Ticardo, and Murillo[2016] used DNN to get prediction accuracy of 65 percent, a similar less error metrics is observed when Convolution Neural Network used along with LR and SVM [Dingle and Fournier,2017] and turns out to be more superior in high accuracy.