There has been a rapid increase in the demand for electric vehicles across the globe and so are their stock prices. The companies, like TESLA and NIO, have seen a rise and fall in their stock prices over a period of time. Various models have been used by researchers/analysts in this field for the stock price prediction using econometrics models, deep learning techniques using LSTM, RNN etc. The purpose of this paper is to predict the stock prices of these two electric vehicles (along with their stock closing prices). The econometric model, ARIMA (p, d, q) in particular, has been fitted to predict the stock prices of these companies’ electric vehicles. The ARIMA (p, d, q) model helps in forecasting by converting the non-stationary data into stationary one using differencing technique. Further, with the help of the ML algorithms, the model appropriately uses the data (training data) and then validates (testing data) in a prefixed proportion. In this paper, we will predict the stock prices of the electric vehicles by extrapolating the data to a future time period and then compare the forecasting accuracies. In terms of the managerial implications, the prediction is expected to help the case companies for better operational planning and execution.