Big Data Analytics-Application of Artificial Neural Network in Forecasting Stock Price Trends in India

By Sigo, Marxia Oli; Selvam, Murugesan et al. | Academy of Accounting and Financial Studies Journal, July 1, 2018 | Go to article overview

Big Data Analytics-Application of Artificial Neural Network in Forecasting Stock Price Trends in India


Sigo, Marxia Oli, Selvam, Murugesan, Maniam, Balasundram, Kannaiah, Desti, Kathiravan, Chinnadurai, Vadivel, Thanikachalam, Academy of Accounting and Financial Studies Journal


INTRODUCTION

Big Data becomes the buzzword in the field of technology for some time now (Tayal et al., 2018). Siegel (2016) emphasized that a little prediction goes a long way. Forecasting the movement of stock price of a company and stock index is a classic problem, to all those who are connected to the stock markets. The Efficient Market Hypothesis (EMH) clearly asserts that it is not possible to exactly forecast the stock prices of companies, due to the existence of random walk behaviour, in the stock markets (Fama, 1970). The movements of stock prices and stock indices are influenced by many macro-economic variables, such as political events, business policies of the corporate enterprises, general economic conditions, commodity price index, bank rate and loan rates and changes in foreign exchange rates, investors' expectations, investors' choices, investors' perception and the human psychology of stock market investors (Miao et al., 2007). Neural networks are a class of generalized, non-linear and non-parametric models, developed from the studies of human brain. It is one of the data mining tools, which performs better than the conventional statistical tools of financial forecasting. The construction of an intelligent data mining system, mainly involves a selection of better forecasting models and trading strategies. The feed-forward networks are the most widely used architecture since such networks offer good generalization abilities, for forecasting the future movements (Ou and Wang, 2009). The stock market transactions, across the globe, are voluminous and volatile. Prediction of stock price movements, being big data, is increasingly difficult due to the prevalence of an element of uncertainties involved with the probable future outcomes (Siegel, 2016). To get accurate response, we use big data analytic concept (Mishra et al., 2018). If and only if the information obtained relating to the stock prices is pre-processed efficiently, the forecasting would become more accurate and reliable. Since the stock price movement is stochastic, non-stationary and non-linear in nature, the volatility widely persists in the stock prices and index movements. Big data tools are used to process unstructured data sets to get the meaningful visualizations (Sankaranarayanan and Thind, 2017). Every industry and business is digitizing their data ushering in the dawn of an era of big data in India (Panicker and Srivastava, 2017). Big Data processes huge volumes of transactional information in real time (Gupta and Tripathi, 2016). At a particular point of time, there could be trends, cycles and random walk or a combination of these three cases/events, in respect of stock market movements (Snigaroff and Wroblewski, 2011). The closing value of the stock index has been used, as one of the important statistical data, to derive useful information about the current and probable future movement pattern of stock markets (Zhang et al., 2005). Based on the neural network forecasting model, an intelligent mining system has been developed. Artificial Neural Network (ANN) approach could forecast the future trend of stock market and it provides stock information signs, for taking better investment decision of buying and selling of stocks, by the investors (Patel et al., 2015). High frequency data has great potential for new insights (Balaji, 2017). ANN, one of the applications of neural network (machine learning) method, is used in this study, to analyze current price trends and probable future prices of company stocks (Maas, 2017).

REVIEW OF LITERATURE

An extensive review of literature, in the area of forecasting of stock prices, has been done to find the research gap and to get an idea of predictive analytics of financial markets. Etzioni (1976) forecasted the movements of stock indices and individual stock prices and explained the difficulties in making specific forecasting of financial markets. It was emphasized that buying a stock, exactly when the price was at the lowest ebb and making a sale when the market price of the share was at the highest ebb, would help the investors to make more profitable choices. …

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