The stock price forecast under the failure of silicon valley bank based on the ARIMA model

Abstract

The collapse of Silicon Valley Bank on March 10, 2023, had a profound impact on the stock prices of many companies in the United States. This study aims to examine the response of other banks in the US to this event by utilizing the Autoregressive Integrated Moving Average (ARIMA) model to forecast their stock prices. The research demonstrates that the ARIMA model effectively predicts the general trend of these banks' stock prices, with Root Mean Squared Error (RMSE) values below 1 for four out of six major US banks. These findings indicate that the proposed method is a promising tool for managing sudden fluctuations in stock prices, outperforming traditional linear regression models. Consequently, this research provides valuable insights for investors and financial institutions in managing and mitigating risks associated with abrupt market changes. Additionally, the study contributes to a greater understanding of the effects of bank collapses on the stock market. Overall, the research highlights the significance of incorporating advanced forecasting methods, such as ARIMA, in analyzing and predicting stock price movements in volatile market conditions.