Portfolio Design for Chinese Pension Investment
- DOI
- 10.2991/978-94-6463-052-7_127How to use a DOI?
- Keywords
- Portfolio; pension; Sharpe ratio; China
- Abstract
The article mainly discusses the investment of pension savings in China, which is an essential topic due to the aging problem in China. The assets in the portfolio are INTC, TXN, XLP and BRSVX, determined by the asset categories and risk diversification. To simulate the change in pension savings, exchange rate of CNY to USD is applied. The expected returns of the assets are calculated by the FF3F model and portfolio weights are determined by maximizing the Sharpe ratio. The final portfolios are one investing all of the pension savings and one investing only 30% of the pension savings. The maximum Sharpe ratio of the latter portfolio is larger than the former one. They both have higher expected return than most of the assets included in the portfolio and have lower variance than the stocks. The result also shows that single assets instead of funds have greater impact in forming the weight of portfolio. The results in this paper benefit the related investors in financial markets.
- Copyright
- © 2022 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 - Muyang Zhou PY - 2022 DA - 2022/12/27 TI - Portfolio Design for Chinese Pension Investment BT - Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022) PB - Atlantis Press SP - 1131 EP - 1139 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-052-7_127 DO - 10.2991/978-94-6463-052-7_127 ID - Zhou2022 ER -