Comparative Analysis of Three Technology Companies

Authors

  • TEY BO WEN

DOI:

https://doi.org/10.62051/1e07w783

Keywords:

Tech; Risk; Profitability; Ratio.

Abstract

Three of the top tech companies—Apple, Tesla, and Lenovo—are carefully examined financially in this essay. These three businesses are also involved in various sectors of the technology sector. Apple is in the software and hardware business, Tesla is in the electric vehicle business, and Lenovo is in the computer business. The purpose of this study is to offer professional and insightful information on these three firms' successful investment strategies. This study includes a review of these firms' most current financial indicators, including an examination of the significance of various data points, as well as a study of the difficulties that different investor types—such as Value, Income, PEG, Index, and Momentum investors—face when choosing assets. Additionally, this research has shown that different investor types are open to purchasing different kinds of equities. Furthermore, value investors tend not to purchase the stocks of all three firms. The analysis's conclusions aid in the creation of precisely customized investment plans, which ultimately seek to maximize financial benefits in the tech sector.

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References

[1] Y. H. Chou, Y. C. Jiang, S. Y. Kuo. Portfolio optimization in both long and short selling trading using trend ratios and quantum-inspired evolutionary algorithms. IEEE Access, 9 (2021) 152115-152130.

[2] Y. H. Chou, Y. T. Lai, Y. C. Jiang, S. Y. Kuo. Using trend ratio and gnqts to assess portfolio performance in the u.s. stock market. IEEE Access. (2021).

[3] L. Ge, X. Ming, G. Ying. Application of Deep Learning in Stock Market Valuation Index Forecasting. IEEE 10th International Conference on Software Engineering and Service Science (ICSESS). (2019).

[4] P. Y. Hsu, I. W. Yeh, C. H. Tseng, S. J. Lee. A boosting regression-based method to evaluate the vital essence in semiconductor industry performance. IEEE Access. (2020).

[5] W. Yuntao, P. Yanghe, Y. Miao. A Survey on ChatGPT: AI–Generated Contents, Challenges, and Solutions. IEEE. (2023) 2644-1268.

[6] K. Sheetal, P. Shruti, C. Jyoti. AI-Based Conversational Agents: A Scoping Review from Technologies to Future Directions. IEEE. 10 (2022) 92337-92356.

[7] R. Sun, Z. Jiang, J. Su. A Deep Residual Shrinkage Neural Network-based Deep Reinforcement Learning Strategy in Financial Portfolio Management. 2021 IEEE 6th International Conference on Big Data Analytics (ICBDA). IEEE. (2021).

[8] B. Wu. Investor behavior and risk contagion in an information-based artificial stock market. IEEE Access. (2020).

[9] V. Rusu, C. Rusu. Forecasting methods and stock market analysis. Creative Math. 12 (2003) 103-110.

[10] S. Lim, M. J. Kim, C. W. Ahn. A genetic algorithm (ga) approach to the portfolio design based on market movements and asset valuations. IEEE Access. 8 (2020).

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Published

23-12-2024

How to Cite

WEN, T. B. (2024). Comparative Analysis of Three Technology Companies. Transactions on Economics, Business and Management Research, 14, 189-193. https://doi.org/10.62051/1e07w783