Comparative Analysis of Three Semiconductor Technology Companies
DOI:
https://doi.org/10.54097/b84k9562Keywords:
Semiconductor, Risk, Profitability, Ratio, StrategyAbstract
This paper presents a meticulous financial analysis of Nvidia, Intel, and Texas Instruments, three leading companies in the semiconductor industry. And these three companies also operate in different section in the semiconductor industry. The study aims to provide insights into effective investment strategies of these companies. The investigation encompasses an analysis of recent financial indicators for these companies like exploring the meaning of various data, along with an exploration of asset selection challenges faced by nine distinct investor profiles including Value, Income, PEG, Index, Ratio analysis, DCF, Momentum, Insider buying and Stock buyback investors. Leveraging established financial models, the research reveals that Index, Momentum, Insider Buying, and Stock Buyback investors are inclined to invest in all three stocks. Additionally, Ratio Analysis and Discounted Cash Flow (DCF) investors are inclined towards investing Texas Instruments. The insights gained from this analysis contribute to the development of tailored investment strategies, ultimately aiming to optimize financial gains within the semiconductor sector.
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Chou, Y. H., Jiang, Y. C., & Kuo, S. Y. Portfolio optimization in both long and short selling trading using trend ratios and quantum-inspired evolutionary algorithms. IEEE Access, 2021, 9: 152115-152130.
Chou, Y. H., Lai, Y. T., Jiang, Y. C., & Kuo, S. Y. Using trend ratio and gnqts to assess portfolio performance in the u.s. stock market. IEEE Access, 2021, 9: 88348-88363.
Li, G., Xiao, M., & Guo, Y. Application of deep learning in stock market valuation index forecasting. In 2019 IEEE 10th International Conference on Software Engineering and Service Science, 2019: 551-554.
Hsu, P. Y., Yeh, I. W., Tseng, C. H., & Lee, S. J. A boosting regression-based method to evaluate the vital essence in semiconductor industry performance. IEEE Access, 2020, 8: 156208-156218.
Wang, Y., Pan, Y., Yan, M., Su, Z., & Luan, T. H. A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions, 2023. arXiv preprint arXiv:2305.18339.
Kusal, S., Patil, S., Choudrie, J., Kotecha, K., Mishra, S., & Abraham, A. AI-based conversational agents: A scoping review from technologies to future directions. IEEE Access, 2022.
Sun, R., Jiang, Z., & Su, J. A deep residual shrinkage neural network-based deep reinforcement learning strategy in financial portfolio management. In 2021 IEEE 6th International Conference on Big Data Analytics, 2021: 76-86.
Wu, B. Investor behavior and risk contagion in an information-based artificial stock market. IEEE Access, 2020, 8: 126725-126732.
Rusu, V., & Rusu, C. Forecasting methods and stock market analysis. Creative Math, 2003, 12: 103-110.
Lim, S., Kim, M. J., & Ahn, C. W. A genetic algorithm (GA) approach to the portfolio design based on market movements and asset valuations. IEEE Access, 2020, 8: 140234-140249.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.






