Advances in Economics, Management and Political Sciences

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Proceedings of the 2nd International Conference on Financial Technology and Business Analysis

Series Vol. 56 , 01 December 2023


Open 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

Ye Chen * 1
1 Department of Economic, University College London, London, United Kingdom

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 56, 49-55
Published 01 December 2023. © 01 December 2023 The Author(s). Published by EWA Publishing
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.
Citation Ye Chen. Research on the Investment Portfolio Optimization Based on Efficient Frontier Model: A Portfolio of AMD, NVIDIA, TXN, LRCX, AVGO, QCOM, INTC and MRVL. AEMPS (2023) Vol. 56: 49-55. DOI: 10.54254/2754-1169/56/20231057.

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

1. De Bondt, W. F., & Thaler, R. (1985). Does the stock market overreact. The Journal of finance, 40(3), 793-805.

2. McGrattan, E. R., & Prescott, E. C. (2001). Is the stock market overvalued? (No. w8077). National Bureau of Economic Research. Retrieved from https://www.nber.org/papers/w8077

3. Guiso, L., Sapienza, P., & Zingales, L. (2008). Trusting the stock market. the Journal of Finance, 63(6), 2557-2600.

4. Mazur, M., Dang, M., & Vega, M. (2021). COVID-19 and the march 2020 stock market crash. Evidence from S&P1500. Finance research letters, 38, 101690.

5. Jung, J., & Shiller, R. J. (2005). Samuelson’s dictum and the stock market. Economic Inquiry, 43(2), 221-228.

6. Iqbal, J. (2012). Stock market in Pakistan: An overview. Journal of Emerging Market Finance, 11(1), 61-91.

7. Blanchard, O., Rhee, C., & Summers, L. (1993). The stock market, profit, and investment. The Quarterly Journal of Economics, 108(1), 115-136.

8. Bustos, O., & Pomares-Quimbaya, A. (2020). Stock market movement forecast: A systematic review. Expert Systems with Applications, 156, 113464.

9. Wattenberg, M. (1999, May). Visualizing the stock market. In CHI’99 extended abstracts on Human factors in computing systems (pp. 188-189).

10. Zuckerman, E. W. (2004). Structural incoherence and stock market activity. American Sociological Review, 69(3), 405-432.

11. Akhilesh G. (2023). Efficient Frontier: What It Is and How Investors Use It. Retrieved from June 28, 2023. Available at: https://www.investopedia.com/terms/e/efficientfrontier.asp

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

Copyright © 2023 EWA Publishing. Unless Otherwise Stated