Navigating Impairment Testing Through the COVID-19 Pandemic: Effect on Asset Value
Keywords:
Financial reporting, GAAP compliance, fair value assessment, economic downturnAbstract
The COVID-19 epidemic necessitated that businesses worldwide confront significant market fluctuations, which led to an increase in asset reassessments as they navigated abrupt income declines, operational disruptions, and rapidly evolving economic conditions. Impairment testing became a significant focus for companies in sectors such as hotel, real estate, retail, and transportation that were significantly affected by lockdowns and fluctuating demand, particularly those with long-lived assets. Historically, impairment assessments made sense depending on constant cash flow projections and predefined discount rates, therefore enabling the appraisal of asset value throughout time. The broad economic consequences of the epidemic forced companies to review and change their current valuation practices to fit unpredictable and volatile market conditions. Incorporating scenario assessments to account for expected recovery timetables and recession effects forced companies to change their impairment testing policies; this often resulted in significant impairment penalties as asset values were reassessed to match lowered expected future cash flows. This change required companies to use flexible, proactive approaches to assess their assets and allow changes as fresh economic data became available. They had to strike a compromise between compliance with legal standards and this adaptability to guarantee adherence to financial reporting guidelines and faithfully display of financial health under uncertainty. Many businesses relied on a mix of industry-specific and macroeconomic projections to more precisely project future cash flows despite these changing elements.The choice of appropriate discount rates has become more complex since companies have to take into account increased risk premiums and more volatility, which leads many of them to choose higher discount rates that represent the different market risks related with the pandemic. The epidemic underlined the need of include sensitivity studies in impairment testing to accommodate various degrees of economic recovery, so helping companies to create more strong forecasts in demanding conditions.
References
1. Hill, N. C., & Sartoris, W. L. (1988). Short-term financial management. Macmillan.
2. Maresova, P. (2006). ACCESSIBILITY CHANGES AND THEIR PERCEPTION BY THE ELDERLY AFTER THE COVID-19 PANDEMIC. Education, 2010.
3. Center, E. R. (2009). Our mission. Retrieved May, 15, 25-33.
4. Mahomed, H., Gilson, L., Boulle, A., Davies, M. A., Khan, S., McCarthy, K., ... & Engelbrecht, E. (2019). The evolution of the COVID-19 pandemic and health system responses in South Africa and the Western Cape Province–how decision-making was supported by data. District health barometer, 20, 265-282.
5. Staunton, D. (2017). Money Market Funds in the EU: A Review of Recent Literature and Regulatory Developments. Themes in Alternative Investments, 2017(1131), 45.
6. Young, A. Q. (2012). Welcome and Introduction.
7. Katzmarzyk, P. T., & Janssen, I. (2004). The economic costs associated with physical inactivity and obesity in Canada: an update. Canadian journal of applied physiology, 29(1), 90-115.
8. Exchange, N. S. (1976). Annual Report & Accounts. Nigerian Stock Exchange.
9. Baker, S. M., Hunt, D. M., & Rittenburg, T. L. (2007). Consumer vulnerability as a shared experience: Tornado recovery process in Wright, Wyoming. Journal of Public Policy & Marketing, 26(1), 6-19.
10. Sherraden, M. S., Huang, J., & Ansong, D. (2017). Financial capability. In Encyclopedia of Social work.
11. Clarke, B. (2016). 4 Cultural reforms in Irish banks. Walking the walk during the COVID-19 pandemic. Pandemic Crisis and Financial Stability, 127.
12. Ladić, M., Bajt, V., & Vlasta, J. (2018). National Integration Evaluation Mechanism. Slovenia. Report for year.
13.Nolan, A. (2017). Health: Funding Access and Efficiency. The Economy of Ireland: Policy-Making in a Global Context, 6(1), 356.
14. Brown, J. E., Isaacs, J. S., Krinke, U. B., Lechtenberg, E., Murtaugh, M. A., Sharbaugh, C., ... & Wooldridge, N. H. (2017). Nutrition through the life cycle (p. 624). Boston (MA): Cengage Learning.
15. Ford, B., & Jenkins, T. (1974). Banker. Dealer and Unofficial Agent', Apollo, c, 416-25.
16. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
17. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
18. Gade, K. R. (2019). Data Migration Strategies for Large-Scale Projects in the Cloud for Fintech. Innovative Computer Sciences Journal, 5(1).
19. Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).
20. Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
21. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2019). End-to-End Encryption in Enterprise Data Systems: Trends and Implementation Challenges. Innovative Computer Sciences Journal, 5(1).
22. Muneer Ahmed Salamkar, and Karthik Allam. Architecting Data Pipelines: Best Practices for Designing Resilient, Scalable, and Efficient Data Pipelines. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
23. Muneer Ahmed Salamkar. ETL Vs ELT: A Comprehensive Exploration of Both Methodologies, Including Real-World Applications and Trade-Offs. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
24. Muneer Ahmed Salamkar. Next-Generation Data Warehousing: Innovations in Cloud-Native Data Warehouses and the Rise of Serverless Architectures. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
25. Muneer Ahmed Salamkar. Real-Time Data Processing: A Deep Dive into Frameworks Like Apache Kafka and Apache Pulsar. Distributed Learning and Broad Applications in Scientific Research, vol. 5, July 2019
26. Naresh Dulam, et al. “Kubernetes Operators: Automating Database Management in Big Data Systems”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
27. Naresh Dulam, and Karthik Allam. “Snowflake Innovations: Expanding Beyond Data Warehousing ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
28. Naresh Dulam, and Venkataramana Gosukonda. “AI in Healthcare: Big Data and Machine Learning Applications ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Aug. 2019
29. Naresh Dulam. “Real-Time Machine Learning: How Streaming Platforms Power AI Models ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
30. Sarbaree Mishra. A Distributed Training Approach to Scale Deep Learning to Massive Datasets. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
31. Sarbaree Mishra, et al. Training Models for the Enterprise - A Privacy Preserving Approach. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
32. Sarbaree Mishra. Distributed Data Warehouses - An Alternative Approach to Highly Performant Data Warehouses. Distributed Learning and Broad Applications in Scientific Research, vol. 5, May 2019
33. Sarbaree Mishra, et al. Improving the ETL Process through Declarative Transformation Languages. Distributed Learning and Broad Applications in Scientific Research, vol. 5, June 2019
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