The Dynamics of Stock Price Change Motion Effected by Covid-19 Pandemic and the Stock Price Prediction Using Multi-layered Neural Network

Abstract

In this paper, we work on the analysis of dynamical change on stock price during Covid-19 pandemic using nonlinear deterministic motion equation. The model is given by the secondorder differential equation with constant coefficient over time with some consideration under stock market structure. This coefficient shows the rate of change of stock price throughout Covid-19. Thus, the Least Square estimator is derived to determine the constant factor. Further, we conduct the Multi-layered Neural Network algorithm to predict the future stock price. To provide accurate forecasting results, the algorithm used in this paper has to be able to recognize stock price data pattern which has dynamic characteristics. Multi-layered Neural Network solve the data with dynamic characteristics by using more than one hidden layer. The input layers of this network are not directly connected to the output layers of the network. Therefore, this algorithm is expected to provide accurate forecasting results. We use the Jakarta Composite Stock Price Index (IHSG) and Waskita Karya Company stock price's data for the subject of observation.