A Systematic Forecasting of Bi-directional Movement of Stock Values with Gann Square: An Algorithmic Trading Tool

The focus of the study was to predict the bidirectional movements of stock values by utilizing closing stock prices. The research employed a systematic and algorithmic approach to develop a forecasting tool for stock market participants. The study commenced by gathering historical data regarding closing stock prices as the fundamental dataset for analysis. Subsequently, they conducted regression analysis within an estimated period, followed by a forecast period, aiming to establish a correlation between closing stock prices and Gann resistance and support levels. To assess the influence of Gann levels on stock price movements in both upward and downward directions, the researchers employed statistical metrics, specifically the Root Mean Square Error (RMSE) and Theil Inequality Coefficient (TIC). An innovative aspect of this research lies in its systematic use of Gann resistance and support levels as independent variables for predicting bidirectional stock price movements.