Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 56 , 01 December 2023
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Access | Article
Research on the Investment Portfolio Optimization Based on Efficient Frontier Model: A Portfolio of AMD, NVIDIA, TXN, LRCX, AVGO, QCOM, INTC and MRVL
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Abstract
Optimal investment portfolio is beneficial for investors to make decisions and construct investment strategy. This paper uses effective frontier methods to build an investment portfolio. This study selects 8 different companies from the USA, which are AMD, MRVL, LRCX, QCOM, INTC, AVGO, and TXN, and collects the data to optimize investment portfolio. The study finds that the optimal portfolio point is (0.1407, 0.0506), the risk is 0.1407, and the return is 0.0506. In this case, for AMD, MRVL, NVIDA, LRCX, INTC, QCOM, AVGO, and TXN, the weights for each company are 0.0502, -0.3867, 0.5289, 0.4367, -0.9987, -0.2805, 1, and 0.6501, respectively. The study allows for short selling and buy mechanisms, since a negative number is actually equivalent to a short sale. As a result, the study has an optimal portfolio that enables investors to make optimal investment decisions for the company the study choose. Through this research, it can help people to increase their life quality, because investment can help with more finance in the daily life, which can let people consume a better goods.
Keywords
portfolio investment, efficient frontier, NVIDIA
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-159-9
- ISBN (Online)
- 978-1-83558-160-5
- Published Date
- 01 December 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/56/20231057
- Copyright
- 01 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited