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Title: Prevailing macro factors of past financial bubbles : predicting the impact on the fastest growing stocks in the market
Authors: Wehbe, Patrick Elie
Advisors: Saab, Gretta 
Keywords: OLS regression, White-Hinkley, HAC method, Financial Markets, Market Sectors
Issue Date: 2022
This paper utilizes the OLS method, the White-Hinkley method, and the HAC methods of regression to investigate the how different macroeconomic indicators affect eight different sectors, namely the retail, financial, services, health, consumer discretionary, capital goods, transportation, and technology sectors. The stocks from these sectors were INTC, AAPL, FDX, LMT, BA, TM, PFE, JNJ, MSFT, JPM, AMZN, and WMT. The time period for the regressions was from January 2000 up till October 2021. Five macroeconomic indicators were selected for the regression, namely the CPI, the unemployment rate, the industrial production, M1 money supply, and the University of Michigan’s consumer sentiment. Nineteen regressions were conducted overall for the different sectors. Upon regressing the economic indicators as the independent variables, with each stock as the dependent variable, the probability of each variable was observed on the 5% and 10% level of significance in order to define how much of an impact each economic indicator has on each sector respectively.
Includes bibliographical references (p. 31-33)
Rights: This object is protected by copyright, and is made available here for research and educational purposes. Permission to reuse, publish, or reproduce the object beyond the personal and educational use exceptions must be obtained from the copyright holder
Ezproxy URL: Link to full text
Type: Project
Appears in Collections:UOB Theses and Projects

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