35 A Volume - 6, Issue- 7, July 2018 www.eprawisdom.com Volume - 6, Issue- 7, July 2018 | SJIF Impact Factor(2018) : 8.003| EPRA International Journal of Economic and Business Review Research Paper IC Value 2016 : 61.33| e-ISSN : 2347 - 9671| p- ISSN : 2349 - 0187 KEYWORDS: ISI Impact Factor (2017):1.365 (Dubai) ABSTRACT OPTIMAL TECHNICAL TRADING RULE FOR STOCK PRICES USING PAIRED MOVING AVERAGE METHOD PREDICTED BY ARIMA AND ANN MODELS R. Sivasamy Professor, Department of Statistics, University of Botswana, Gaborone, Botswana. Peter O. Peter Phd Research Scholar, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China. Success of any trade depends on the ability to spot and profit from market swings associated with prices {x t: 1, 2, …, N} of a stock. In this paper an optimal technical trading rule (OTTR) is proposed to identify profitable positions for ‘when to buy and when to sell’ to help all traders who live and die with minute-by-minute price data. Furthermore a trading rule G SL (t) that assigns selling positions with an upper level price and buying positions with a lower level price is formulated by monitoring the ratio series R(t)=MA S (t)/MA L (t) where, S < L with MA S (t) and MA L (t) as simple moving averages (MAs) computed from the stock series {x t } under study. We denote the mean and standard deviation measures of the R SL (t) series by ‘m’ and ‘s’ respectively and the upper level positions (ULPs) are selected above the mean at time ‘t’ if (R SL (t) > m+ks, R SL (t-1) <m+ks ) and lower level positions (LLPs) below the mean are chosen at time ‘t’ if (R SL (t-1) > m-ks, R SL (t) <m-ks ), defining a trading rule G SL (t). A combination (S * , L * , h * ) that maximises the total expected profit P SL (t, h) over the positions determined by the OTTR is selected as the ‘Optimal technical trading rule (OTTR(S * ,L * , h * ))’ for this investigation. To implement the proposed methodology pertaining to this rule, a training data set and testing data set are simulated and an appropriate model is fitted by hybrid-Auto Regressive Integrated Moving Average (hybrid-ARIMA) and Artificial Neural Network (ANN) methods. Using the estimated values of the parameters by hybrid-ARIMA and NN methods, predictions are made for testing data set. From these predicted values, OTTR(S * , L * ,h * ) for both hybrid-ARIMA and ANN approaches are obtained and the corresponding maximum profits are compared. ARIMA model; ANN model; MA values; predicted prices; OTTR R(t) ratio; Positional profit. INTRODUCTION Traders participate in financial markets for buying and selling stocks, futures, forex and other securities through positions, each with an opening and closing out days with the intention of making frequent gains or returns. Technical traders often believe that they have all of the information necessary to make an informed trade by viewing the past trade and price history of a stock. Often they rely on stock charts that are constructed based on trading information such as previous prices and trading volume, plus mathematical indicators. Several studies on exploring technical analysis have been published in the last five decades. Rodolfo et al. (2017) have well accounted the existing literature on technical analysis by presenting an overview of characteristics of the literature, potential knowledge gaps and focusing on the analysis of stocks and future research in this area. A motivating factor comes from the technical trading method developed by Netfci (1991) showing that most patterns used by technical analysts need to be characterized by appropriate sequences of local minima and/or maxima called signals of market turning points and more often results in nonlinear prediction problems. Similar studies, using technical indices such as moving averages (MA) and relative strength indices (RSI), have been extensively investigated by Asadi et al. (2012 ) and Chang and Fan (2008). The applications of text mining techniques are discussed in Lo et al. (2000) to find the important information from news articles. Zhu and Zhou (2009) discuss the usefulness of technical analysis, specifically the widely employed moving average trading rule from an asset allocation perspective. The authors’