The Application of Modern Portfolio Theory in US Stock Market with the Use of Python Programming
- DOI
- 10.2991/978-94-6463-030-5_17How to use a DOI?
- Keywords
- Modern Portfolio Theory; Investment; Finance; Stock Market; Python Programming
- Abstract
The purpose of our study is to investigate the application of Modern portfolio Theory in the stock markets and design an investment strategy for risk-averse investors. The principle of the Modern portfolio theory is mean-variance analysis, that is we use mean and variance to measure the expected return and risk of portfolios respectively, and select an optimal portfolio based on it. The core idea is diversification of investments, which means to divide the investment into a combination of different financial assets or shares of different companies, such as stocks and securities. We investigate the adjusted closing stock price of five companies: Facebook, Amazon, Apple, Netflix and Google, from 2011 to 2021. Our results show that the minimum risk (annualized volatility at 23%) can be obtained when the expected annualized return is at level of 28%. A higher expected annualized return (at the level of 33%) might be achieved if the investors are willing to take more risk (annualized volatility at 25%).
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Tianjia Xia PY - 2022 DA - 2022/12/20 TI - The Application of Modern Portfolio Theory in US Stock Market with the Use of Python Programming BT - Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022) PB - Atlantis Press SP - 152 EP - 160 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-030-5_17 DO - 10.2991/978-94-6463-030-5_17 ID - Xia2022 ER -