摘要：Forecasting the stock price movement of a company and stock index is a classic problem. Efficient Market Hypothesis clearly asserts that it is not possible to exactly predict the stock prices of corporate entities, due to the existence of random walk behaviour in stock markets (Fama, 1970). The movements of stock prices and stock indices are influenced by many macro-economic variables such as political events, policies of the corporate enterprises, general economic conditions, commodity price index, bank rate, loan rates, foreign exchange rates, investors’ expectations, investors’ choices 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, inspired by the studies of human brain. The feed-forward networks are the most widely used (Ou & Wang, 2009). Prediction of stock market movements has become increasingly difficult due to uncertainties, involved with the probable future outcomes. At a particular point of time, there could be trends, cycles and random walk or a combination of three cases/events (Robert & David, 2011). Closing price of a 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 market (Defu et al., 2005). In data deterministic approach, a layer is employed to convert each of the technical indicator’s continuous value, from +1 to -1, indicating the probable future growth/decline movements. This layer explains the manner of stock market movements, on both upwards and downwards direction, across the time periods (Shuai & Wei, 2014). The data deterministic approach could forecast the future trend of stock market and to provide stock information signs, for taking better investment decision of buying and selling of stocks by the investors (Jigar et al., 2015a).
关键词：Behavioural Finance;Capital Market;Predictive Analytics;Stochastic;Stock Index
