Abstract
Stock price prediction plays a main role and serve as a key for quantitative analysis and investment in financial field. With the boosted of various state-of-art statistical approaches as well as computing strategies, various forecasting scenarios have been proposed. Among numerous prediction schemes, ARIMA is one of widely implemented and explored models. On this basis, this study presents extensive process of predicting stock price based on the ARIMA model. The stock prices of Tesla, NIO and BAIC BluePark in new energy vehicles industry are collected from investing.com and money.com and used for prediction. The parameters of ARIMA model which is p, q and d ranging from 0 to 4. According to the analysis, it is feasible for short-term forecasting in new energy vehicles industry and is competitive with other forecasting models in helping both institutions and individuals’ investment. To be specific, the predicted stock price is close to the actual data and institutions and individuals can make profitable decisions based on the results. Overall, these results shed light on guiding further exploration of price forecasting of underlying assets.
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