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Stock Analysis of Apple, Google, and Meta Using Time-Series
- 1 University of Notre Dame
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Abstract
This research examines the patterns and forecasts future stock prices of three prominent tech companies—Apple, Google (Alphabet), and Meta—using two widely used time series analysis methods: ARIMA (AutoRegressive Integrated Moving Average) and ETS (Exponential Smoothing). The study explores the potential impact of layoffs on the stock prices of these companies, addressing inquiries into cyclical or seasonal autocorrelation in stock price movements. By leveraging historical stock price data and applying ARIMA and ETS models, this research uncovers trends and develops robust forecasting models. The investigation holds significance for investors, market analysts, and company stakeholders, providing valuable insights into how workforce restructuring and organizational changes may influence stock market performance. The findings suggest that although the three technology companies experienced a decline in stock prices coinciding with an increase in layoffs, the forecasts generated by the ETS model indicate a potential stabilization or even an increase in stock prices in the future. These insights equip decision-makers with valuable information for assessing potential trends and making informed decisions regarding investments and workforce management strategies.
Keywords
tech stocks, time series analysis, stock forecast
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Cite this article
Cao,X. (2023). Stock Analysis of Apple, Google, and Meta Using Time-Series. Advances in Economics, Management and Political Sciences,46,175-183.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
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