Efficacy of time series forecasting (ARIMA) in post-COVID econometric analysis

Abstract

With the COVID-19 pandemic destabilizing the global economy, statisticians all over the world are striving to predict the recovery of the same. Stock market, being a reliable indicator of the performance of major economies, is usually forecasted using time series analysis methods like the Autoregressive Moving Average Model (ARIMA). In this context, We have felt the need to question the appropriateness of ARIMA in Econometric analysis, specifically in forecasting share prices in the stock market following a colossal anomaly in the general trend such as the one caused by the COVID-19 pandemic induced market instability. In this study, the predictive efficacy of the ARIMA model relative to the variations and anomalies in the data is studied. The functional constituents of the ARIMA model are also explored and criteria such as AIC (Akaike Information Criterion) and BIC (Bayesian information criterion) are employed to select the best fit model. By analyzing the share prices of twenty random stocks listed in NYSE/NASDAQ from September 2019 to August 2020, the possible correlation between predictive efficiency of the ARIMA model and variation in the data is explored and quantified.