A Technical Indicator for a Short-term Trading Decision in the NASDAQ Market

Purpose: The objective is to employ a stochastic model to develop a new technical analysis indicator that could compute the variation of any index. We demonstrate the superiority and applicability of our proposed model and show that our proposed indicator could help investors and market analysts to anticipate the market trend in the short term and make better trading decisions by using our proposed model to analyze the variation of the NASDAQ Composite Index (IXIC). Design/methodology/approach: This study uses a stochastic process without mean-reverting property to develop a stochastic model that could compute the variation of any index. To show the superiority and applicability of our proposed model in computing the variation of any index, we employ our proposed model to compute the daily closing values of the IXIC over 10 years and derive the variation of the IXIC index. Findings: Our findings indicate that, based on the mean absolute percentage error, the calibrated model we proposed provides a more accurate estimate of the short-term index that outperforms both the simple moving average and the MACD in predictive accuracy. It delivers a robust anticipation of the overall market trend by offering a 95% confidence interval for the value of the composite NASDAQ index. Practical Implications: Our proposed indicator could help investors and market analysts to anticipate the market trend in the short term and make better trading decisions. Our proposed model provides market analysts with a forecasting tool by using our proposed technical analysis indicator to anticipate the market trend, which outperforms some traditional indicators of technical analysis, including Simple Moving Averages and Moving Average Convergence Divergence. Originality/value: Our approach, results, and conclusions are original and new in the literature. Our proposed model is a new technical indicator for predicting any index based on a stochastic process, which has been found to outperform some classical indicators. This research makes significant contributions to the field of decision sciences because the indicator we have developed plays a crucial role. It enables better buying and selling decisions based on market trend predictions estimated by using our proposed model. In this way, the indicator offers added value to professionals in making investment decisions. The results of this research work contribute to the development of new technical analysis indicators. Here, the IXIC index is an example, the use of this indicator is wider and could concern any stock market index and any share. So, this work enriches the literature and opens up new avenues for any researcher who wants to use stochastic processes to develop new technical indicators for different financial assets.