Tax Loss Harvesting and the CARES Act: Strategic Tax Planning Amidst the Pandemic

Authors

  • Piyushkumar Patel Accounting Consultant at Steelbro International Co., Inc, USA Author
  • Disha Patel CPA Tax Manager at Deloitte, USA Author

Keywords:

CARES Act, Tax Loss Harvesting

Abstract

The COVID-19 pandemic brought unforeseen economic disruptions, challenging businesses worldwide to reassess financial strategies to safeguard liquidity and manage significant losses. The CARES Act emerged as a critical piece of legislation, providing tax relief that allowed corporations to harness tax loss harvesting and net operating loss strategically (NOL) carrybacks. This analysis explores how the CARES Act altered tax planning dynamics by temporarily lifting prior restrictions on NOLs, enabling companies to carry back losses to profitable years for immediate tax refunds. Additionally, the Act allowed businesses to capitalize on tax loss harvesting, creating opportunities to offset taxable gains with losses, thus preserving much-needed cash flow. By offering a lifeline to corporations affected by pandemic-driven revenue declines, these provisions provided immediate tax refunds and bolstered liquidity when businesses faced severe cash flow constraints. Through real-world examples, we highlight how these CARES Act measures enhanced financial resilience, allowing companies to recover some prior investments and mitigate ongoing financial stress. This paper underscores the significance of flexible tax policy during economic downturns, revealing how targeted adjustments in tax legislation can help businesses navigate economic volatility, optimize liquidity, & pave the way for recovery. The impact of these policies extends beyond individual corporations, showcasing a broader economic response where strategic tax relief mechanisms can play a role in stabilizing entire sectors under crisis. By examining the effects of the CARES Act’s tax provisions, this analysis provides insight into the decisive role of policy in financial crisis management. It highlights the continued importance of adaptable tax frameworks to sustain economic resilience.

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References

Planning, F., & Primer, A. (2017). Executive.

Wójcik-Czerniawska, A. (2017). Effective Methods of Tax Reduction for Organizations in the Era of COVID-19. In Economic Resilience and Pandemic Response (pp. 211-221). Routledge.

Panama reached high-income, L. A. (1985). STRONG FUNDAMENTALS PRE-PANDEMIC. World Economic Outlook, 1995(2005), 2015.

Food, D., Grants, F. T. A., & Urges, A. P. T. A. (1980). In Transit.

Saez, E., & Zucman, G. (2019). The triumph of injustice: How the rich dodge taxes and how to make them pay. WW Norton & Company.

Flanagan, J. L. (2019). Reframing Taxigration. Tenn. L. Rev., 87, 629.

Ahle, H. R. (2006). Anticipating Pandemic Avian Influenza: Why the Federal and State Preparedness Plans Are for the Birds. DePaul J. Health Care L., 10, 213.

Alston, P., & Reisch, N. (Eds.). (2019). Tax, inequality, and human rights. Oxford University Press.

Hardcastle, L. E., Record, K. L., Jacobson, P. D., & Gostin, L. O. (2011). Improving the population's health: the Affordable Care Act and the importance of integration. Journal of Law, Medicine & Ethics, 39(3), 317-327.

Martin, P., & Rutledge, Z. (2004). ARE UPDATE. Postmenopausal hormone therapy: Benefits and risks.

Freudenberg, N. (2014). Lethal but legal: corporations, consumption, and protecting public health. Oxford University Press.

Jimenez, B. S. (2013). Strategic planning and the fiscal performance of city governments during the Great Recession. The American Review of Public Administration, 43(5), 581-601.

McCaffery, E. J. (2008). Fair not flat: How to make the tax system better and simpler. University of Chicago Press.

White, J. (2017). On the cusp of change. Article in “Energy Perspectives, p34.

Myers, N. (2019). Pandemics and polarization: Implications of partisan budgeting for responding to public health emergencies. Rowman & Littlefield.

Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.

Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.

Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).

Gade, K. R. (2017). Migrations: Challenges and Best Practices for Migrating Legacy Systems to Cloud-Based Platforms. Innovative Computer Sciences Journal, 3(1).

Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).

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).

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

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

Muneer Ahmed Salamkar, and Karthik Allam. “Data Lakes Vs. Data Warehouses: Comparative Analysis on When to Use Each, With Case Studies Illustrating Successful Implementations”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019

Muneer Ahmed Salamkar. Data Modeling Best Practices: Techniques for Designing Adaptable Schemas That Enhance Performance and Usability. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Dec. 2019

Naresh Dulam, and Venkataramana Gosukonda. Event-Driven Architectures With Apache Kafka and Kubernetes. Distributed Learning and Broad Applications in Scientific Research, vol. 3, Oct. 2017, pp. 115-36

Naresh Dulam, et al. Snowflake Vs Redshift: Which Cloud Data Warehouse Is Right for You? . Distributed Learning and Broad Applications in Scientific Research, vol. 4, Oct. 2018, pp. 221-40

Naresh Dulam, et al. Apache Iceberg: A New Table Format for Managing Data Lakes . Distributed Learning and Broad Applications in Scientific Research, vol. 4, Sept. 2018

Naresh Dulam, et al. Data Governance and Compliance in the Age of Big Data. Distributed Learning and Broad Applications in Scientific Research, vol. 4, Nov. 2018

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

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

Sarbaree Mishra, et al. Improving the ETL Process through Declarative Transformation Languages. Distributed Learning and Broad Applications in Scientific Research, vol. 5, June 2019

Sarbaree Mishra. A Novel Weight Normalization Technique to Improve Generative Adversarial Network Training. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019

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Published

08-04-2020

How to Cite

[1]
Piyushkumar Patel and Disha Patel, “Tax Loss Harvesting and the CARES Act: Strategic Tax Planning Amidst the Pandemic ”, Distrib Learn Broad Appl Sci Res, vol. 6, pp. 842–857, Apr. 2020, Accessed: Dec. 31, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/271

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