Statistical Modeling of S&P 500 Data Based on Time Lags of Apple Corporation

Ranju Karki, Doo Young Kim, Christ P. Tsokos

Abstract: Our objective is to select a company, AAPL, of the S&P 500 to be our leading company and proceed to predict the closing price of AAPL in conjunction with the other companies. We utilized the weighted five-day moving arc length as a measure of volatility and Self-Organized Maps to identify the appropriate cluster of companies that followed similar patterns with AAPL. We also developed predictive statistical models for the closing prices of the AAPL with Meta Platforms, Inc. (FB) and Microsoft Corporation (MSFT). One can select any company within the identified cluster to develop a predictive model using our procedures and methodologies.

Keywords: Clustering, Statistical Modeling, SOMs, RVAR, Stock Price

How to Cite?: Ranju Karki, Doo Young Kim, Christ P. Tsokos, "Statistical Modeling of S&P 500 Data Based on Time Lags of Apple Corporation", Volume 11 Issue 8, August 2022, International Journal of Science and Research (IJSR), Pages: 1397-1409, https://www.ijsr.net/getabstract.php?paperid=SR22826023951, DOI: https://dx.doi.org/10.21275/SR22826023951