Portfolio Optimization for Cloud Computing, Biotechnology, Artificial Intelligence, Green Energy and Pharmaceutical Industries

Abstract

Making reasonable portfolio allocation is a common problem nowadays. This paper analyzes asset allocation in cloud computing, biotechnology, medical engineering, green energy and artificial intelligence. These five assets are all fast-growing and suitable investments this year. This paper uses a time series approach to analyze these assets. Assets are analyzed using the mean-variance approach, Autoregressive Integrated Moving Average model (ARIMA), and FAME-French model. The mean variance model can better optimize the portfolio, while the ARIMA model is used to predict the selected ETF and judge its trend in the future period of time, and finally the Fama-French model is used to explain the portfolio. Finally, this paper evaluates the performance of the portfolio of these assets, including the specific proportion and the related return rate. The results of the analysis show that green energy and pharmaceutical industries account for the proportion of investment sent, the study is useful for potential investors in cloud computing, artificial intelligence, green energy, pharmaceutical industries, and biotechnology.