Forecasts on Best Investment Portfolio for Healthcare Companies Based on ARIMA and GARCH Models

He Gong (), Rei Hong and Zhuoran Li
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He Gong: University of Toronto, Faculty of Arts and Science
Rei Hong: Guangdong University of Finance and Economics, School of Business Administration
Zhuoran Li: University of California, School of Physical Sciences and Social Science

A chapter in Proceedings of the 2022 2nd International Conference on Financial Management and Economic Transition (FMET 2022), 2023, pp 399-414 from Springer

Abstract: Abstract Healthcare stocks have increased due to the testing and treatment costs and many other factors caused by the COVID-19 pandemic since 2020. This paper constructs portfolios to minimize the risk and maximize the returns of healthcare stocks. Moreover, to enhance the performance of the initial portfolio, this paper uses time series analysis to forecast the stock price and verify the forecasting outcomes. The paper investigates the stock price of top healthcare companies in the United States using time series analysis to predict their performance and organize an optimal portfolio. Specifically, this research paper first employs the Auto Regressive Integrated Moving Average (ARIMA) and Auto Regressive Conditional Heteroskedasticity (ARCH) models to determine the 30 days stock price forecasting. After that, according to the historical data and forecasts, it evaluates the portfolio's efficient frontier based on Monte Carlo simulations (MCOS), which determines the minimum volatility, maximum Sharpe ratio, and the most suitable portfolio. The results show that the return of the optimal portfolio performs a more significant expected return and has less volatility than the portfolio with equal weights, which proves the validity of our model.

Keywords: ARIMA model; GARCH model; Portfolio optimization; Time series; Healthcare (search for similar items in EconPapers)
Date: 2023
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DOI: 10.2991/978-94-6463-054-1_44

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