Integrating Navier-Stokes equation and neoteric iForest-BorutaShap-Facebook’s prophet framework for stock market prediction: An application in Indian context

Abstract

The paper presents a framework for forecasting stock prices for normal time and pandemic period and tests its forecasting efficacy. It is based on the specification of dependent and independent variables drawn from the Navier-Stokes equation, which defines the relationship between velocity and kinematic viscosity, density, pressure, and external force field. We define stock market proxies for the variables used in the equation in modeling stock price behavior and extend our framework to build in aspects of behavioral finance like hindsight bias, representative bias, and anchoring bias. The predictive exercise has been carried out on stock prices of Indian companies belonging to different industrial verticals to test whether our framework can handle diversity. AI framework comprising Isolation Forest (iForest), BorutaShap, and Facebook's Prophet Algorithm has been used for automatic anomaly elimination, dedicated feature screening, and forecasting future stock price movements. The results reveal interesting insights into the varied response of stock prices of different companies to macroeconomic shocks and would be quite useful for fund managers, regulators, and policymakers. Several numerical and statistical checks indicate the efficiency of our proposed framework in yielding superior quality forecasts, both in normal and challenging times.