Industry Diversification and Optimal Industry Combination of Portfolios
There authors contributed equally
- DOI
- 10.2991/aebmr.k.220307.196How to use a DOI?
- Keywords
- industry diversification; minimum variance portfolio; industry combination; maximum Sharpe ratio portfolio; maximum drawdown portfolio
- Abstract
The importance of asset diversification has been tested and proven many times in the past few decades. Investors can allocate their money into different asset classifications, such as stocks, golds, and funds. Undoubtedly, returns and risks vary across different types of assets. A huge number of risk lovers are keen to invest in stocks and equity funds due to their fascinating revenues. However, the equity market covers a wide variety of industries. How could one reasonably allocate assets to obtain a fair gain with low risks? We aimed at figure out the optimal combination of 20 industries that construct minimum variance, maximum Sharpe ratio, and maximum drawdown portfolios separately among 64 industries in China. Basically, we gathered industries index data in 2014 and 2015 and utilized Excel functions to do our experiment. Eventually, 7 industries showed up in both years’ minimum variance portfolios. If certain industries appeared in minimum variance portfolios over and over again, these industries usually had relatively low risks and could offer suggestions for investors. Some implications could also be found from maximum Sharpe ratio and maximum drawdown experiments.
- Copyright
- © 2022 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Mingyu Lyu AU - Xiaolong Qiu AU - Zijian Liu AU - Xu Yang PY - 2022 DA - 2022/03/26 TI - Industry Diversification and Optimal Industry Combination of Portfolios BT - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022) PB - Atlantis Press SP - 1183 EP - 1191 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.220307.196 DO - 10.2991/aebmr.k.220307.196 ID - Lyu2022 ER -