Application of ARIMA Model in Portfolio Optimization

Abstract

This paper investigates the portfolio allocation strategy using the ARIMA time series model with eight companies in four industries: Technology, Healthcare, Financial Services, and New Energy. This research aims to identify the best portfolio allocation approach that maximizes the returns for investors in each industry. This paper collected daily stock prices of Apple and Microsoft in the technology industry, Johnson & Johnson and Pfizer in the healthcare industry, JPMorgan Chase and Goldman Sachs in the financial services industry, and Tesla and NextEra Energy in the new energy industry from January 2016 to December 2019. The logarithmic returns first were calculated for the first three years as the training data. Then, an appropriate ARIMA time series model would be fitted to forecast the stock return for the last year and perform the portfolio optimization based on the predicted return for 2019 following a result of weights for each asset. By comparison, the paper also applied portfolio optimization to the raw logarithmic daily return from 2016 to 2018 without the time series model fitted and got another set of weights for each asset. Finally, both cumulative returns for these two portfolios computed and compared with visualizations. There is a finding that the modified strategy of portfolio with ARIMA model forecasted data could provide a more obvious trade-off relationship between volatility and sharpe-ratio for returns than the traditional one performed with the raw data. The result of this paper provides a potential scope of an application of portfolio optimization worked with time series modeling for investors to make investment decisions that possibly yield a higher return.