Financial Diagnosis and Value Recovery of Meta Platform Inc.: A Study to Meta with Targeted Remedies
DOI:
https://doi.org/10.54097/fs80pq26Keywords:
profitability, solvency, Meta's liquidity, Project objectivesAbstract
During last few years, the world has witnessed the explosion in science and technological industry and the fierce competition in this industry. Also, the technology has suffered from the economic policy of the home government. In this essay, the financial analysis is used at first in order to measure the liquidity profitability solvency and share price changes of Meta. The total business operation of Meta is good; however, some problems are discovered. Then, the comprehensive and deep investigation is carried out at the propose of explaining the causes. Finally, three helpful suggestions are introduced and explained. As a conclusion, the major problems of Meta consist of worsen solvency ability, reliance on advertisement income, and the productivity paradox of artificial intelligence program. And these issues can be solved by clarifying project goals and strengthening responsibility-based management, expanding business on online shopping to reduce reliance on advertisement revenue, utilizing reference shares enhance solvency ability and using asset-liability hedging method. This essay can be used to help investors realize the business operation situation of Meta and its competitors, what’s more, the article provides the ways for the companies which meet the similar predicament.
Downloads
References
[1] T. Davenport, A. Guha, D. Grewal, et al., How artificial intelligence will change the future of marketing. J. Acad. Mark. Sci. 48, 24–42 (2019).
[2] S. O’Connor, Open artificial intelligence platforms in nursing education: tools for academic progress or abuse? Nurse Educ. Pract. 66, 103537 (2023).
[3] I. Breskienė, Employee surveillance using algorithmic management based on artificial intelligence systems. Teisė 133, 103–117 (2025).
[4] M. Sokolova, Risks of uncontrollability for GenAI models of American technology companies. Russ. Am. 21st Century 11 (2024).
[5] A. Lastauskaite, R. Krušinskas, The impact of production digitalization investments on European companies’ financial performance. Economies 12, 138 (2024).
[6] P. Helo, Y. Hao, Artificial intelligence in operations management and supply chain management: an exploratory case study. Prod. Plan. Control 33, 1573–1590 (2021).
[7] S. Wamba-Taguimdje, S. Wamba, J. Kamdjoug, et al., Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Bus. Process Manag. J. 26, 1893–1924 (2020).
[8] Ç. Hazir, Working capital management and global economic policy uncertainty: evidence from Turkey. Finans Ekon. Sos. Araştırmalar Derg. 10, 106–118 (2025).
[9] Y. Feng, The influence of the Federal Reserve System interest rate hike on Tesla: empirical research based on ARIMA model. Adv. Econ. Manag. Polit. Sci. 61, 228–235 (2023).
[10] B. Laliberte, R. Sengupta, Why do net interest margins behave differently across banks as interest rates rise? Fed. Reserv. Bank Kans. City Econ. Rev. (2024).
[11] Meta Platforms, Inc., Form 10-K for the fiscal year ended December 31, 2024. U.S. Secur. Exch. Comm. (2025).
[12] Meta Platforms, Inc., Form 10-K for the fiscal year ended December 31, 2023. U.S. Secur. Exch. Comm. (2024).
[13] Meta Platforms, Inc., Form 10-K for the fiscal year ended December 31, 2022. U.S. Secur. Exch. Comm. (2023).
[14] T. Iankovets, D. Nikolaiev, Meta Ads digital advertising in social networks. Sci. Fruct. 5, 76–88 (2024).
[15] M. Klein, A. Kutlar, A comparative experimental study on artificial intelligence- and human-driven social media marketing campaigns. Pressacademia (2024).
[16] M. Masud, S. Paul, A. Azeem, Optimisation of a production inventory model with reliability considerations. Int. J. Logist. Syst. Manag. 17, 22 (2013).
[17] Yahoo Finance, Meta Platforms, Inc. (META) historical prices – 2025 annual data. Yahoo Finance (2025). https://finance.yahoo.com/quote/META/history?p=META
[18] T. A. Kitaw, B. D. Tilahun, A. B. Zemariam, et al., The financial toxicity of cancer: unveiling global burden and risk factors—a systematic review and meta-analysis. BMJ Glob. Health 10, e017133 (2025).
[19] B. Glasson, Reality offsets: climate meets capitalism at the Olympic Games. Eur. J. Cult. Stud. (2024).
[20] J. Lu, W. Lin, Q. Wang, Does a more diversified revenue structure lead to greater financial capacity and less vulnerability in nonprofit organizations? A bibliometric and meta-analysis. VOLUNTAS Int. J. Volunt. Nonprofit Organ. 30, 593–609 (2019).
[21] Alphabet Inc., Form 10-K for the fiscal year ended December 31, 2024. U.S. Secur. Exch. Comm. (2025).
[22] M. Dirse, G. Japee, The effect of capital structure on the financial sustainability of microfinance institutions: a meta-analysis. Rev. Dev. Econ. 28, 958–983 (2024).
[23] B. Irfan, R. Sirvent, Large language models and the future of gastroenterology: dissecting the biopolitics of data in a global health ecosystem. Front. Med. 12, 1668365 (2025).
[24] S. Wang, Research on the investment value of META based on financial analysis, risk and return analysis, valuation method. Adv. Econ. Manag. Polit. Sci. 152, 188–197 (2025).
[25] M. Regona, T. Yiğitcanlar, C. Hon, M. Teo, Artificial intelligence and sustainable development goals: systematic literature review of the construction industry. Sustain. Cities Soc. 108, 105499 (2024).
[26] S. Bouhouita-Guermech, P. Gogognon, J. Bélisle-Pipon, Specific challenges posed by artificial intelligence in research ethics. Front. Artif. Intell. 6, 1149082 (2023).
[27] G. Milano, Beyond EVA. J. Appl. Corp. Finance (2019).
[28] C. B. Aranda-Jan, N. Mohutsiwa-Dibe, S. Loukanova, Systematic review on what works, what does not work and why of implementation of mobile health (mHealth) projects in Africa. BMC Public Health 14, 188 (2014).
[29] H. Kautz, The third AI summer: AAAI Robert S. Engelmore Memorial Lecture. AI Mag. 43, 105–125 (2022).
[30] M. Jensen, K. Murphy, CEO incentives—it's not how much you pay, but how. J. Appl. Corp. Finance 36, 80–91 (2024).
[31] B. Yuceer, Ö. Şarkbay, M. Başal, The effect of consumer attitude on the effect of social media marketing on hospital promotion activities through content marketing. Open J. Soc. Sci. 12, 375–391 (2024).
[32] J. Ren, Behavioural economic analysis of Generation Z consumption decision-making in social media environments. Highlights. Bus. Econ. Manag. 58, 72–78 (2025).
[33] K. Mugadza, G. Mwalemba, Online platform privacy policies. S. Afr. Comput. J. 35 (2023).
[34] L. Ellul, B. Manché, P. Baldacchino, N. Tabone, S. Grima, An examination of preference share issuance by companies listed on the Malta Stock Exchange. J. Corp. Gov. Insur. Risk Manag. 11, 1–24 (2024).
[35] B. Holmström, J. Tirole, Market liquidity and performance monitoring. J. Polit. Econ. 101, 678–709 (1993).
[36] S. Mayanja, Assessing the status of growth of MSMEs in a developing country: Uganda as a case study. Sci. J. Bus. Manag. 8, 83 (2020).
[37] R. Hiller, C. Schaack, A classification of structured bond portfolio modeling techniques. J. Portf. Manag. 17, 37–48 (1990).
[38] J. Geanakoplos, The leverage cycle. NBER Macroecon. Annu. 24, 1–65 (2010).
[39] E. Verbiest, Analysis of bond portfolios in an ALM context. SSRN Electron. J. (2021).
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
