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Event Type
Research Presentation
Academic Department
Mathematics and Statistics
Location
Dana Science Building, 2nd floor
Start Date
14-4-2023 1:30 PM
End Date
14-4-2023 3:00 PM
Description
Under the direction of Dr. Giancarlo Schrementi
The stock market, a volatile marketplace, has its stock prices determined by the ever-changing demand and supply of stocks. The Efficient Market Hypothesis (EMH) postulates that the prevailing prices of securities are adequately priced and encompass all accessible information in the market, resulting in difficulty to generate returns even with technical and fundamental analyses. However, some investors claim that they can surpass the market in the short run through forecasting methods, such as the Autoregressive Integrated Moving Average (ARIMA) model, which uses historical data to predict future trends based on the assumption that future trends will imitate prior trends. The model has been extensively utilized in predicting demand, particularly in forecasting future demand in the food manufacturing industry, as it provides managers with trustworthy guidance for making supply chain-related decisions. This research intends to employ the ARIMA model to scrutinize and forecast the stock prices of Apple and GameStop, particularly concentrating on discerning the comparative effectiveness of the model for the two companies. Apple's history of stable stock prices may render it a more suitable candidate for the ARIMA model than GameStop. This study has the potential to provide valuable insights for investors seeking to invest in Apple or GameStop.
Predicting the Stock Prices using ARIMA Model
Dana Science Building, 2nd floor
Under the direction of Dr. Giancarlo Schrementi
The stock market, a volatile marketplace, has its stock prices determined by the ever-changing demand and supply of stocks. The Efficient Market Hypothesis (EMH) postulates that the prevailing prices of securities are adequately priced and encompass all accessible information in the market, resulting in difficulty to generate returns even with technical and fundamental analyses. However, some investors claim that they can surpass the market in the short run through forecasting methods, such as the Autoregressive Integrated Moving Average (ARIMA) model, which uses historical data to predict future trends based on the assumption that future trends will imitate prior trends. The model has been extensively utilized in predicting demand, particularly in forecasting future demand in the food manufacturing industry, as it provides managers with trustworthy guidance for making supply chain-related decisions. This research intends to employ the ARIMA model to scrutinize and forecast the stock prices of Apple and GameStop, particularly concentrating on discerning the comparative effectiveness of the model for the two companies. Apple's history of stable stock prices may render it a more suitable candidate for the ARIMA model than GameStop. This study has the potential to provide valuable insights for investors seeking to invest in Apple or GameStop.