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How ETFs circumvent IRS wash-sale rules

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2 years ago

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Institutional investors are harvesting ETF losses for tax purposes, then placing their assets in highly correlated funds — regardless of so-called wash-sale restrictions, a new study found.

In theory, IRS guidelines prohibit investors from buying “substantially identical” securities 30 days before or after selling them. 

In practice, fund managers, pensions, insurance firms, endowments and other institutional investors “engage in substantial swapping” of ETFs with holdings that are 99% or more the same thing to the tune of $417 billion in assets since 2001 and $106 billion in 2022 in transactions that “seem to lack economic substance beyond harvesting capital losses,” according to a working academic paper released this summer and revised last month by four professors of business and management. The findings, which echo those of another working paper from earlier this year, shed more light on how ETFs help financial advisors and their clients offset the taxes on capital gains by booking losses in their portfolios.

“While the economic intent of the wash sale rule is straightforward, significant uncertainty remains as to the permissibility of tax deductions achieved through ETF swaps,” the report’s authors — Michael Dambra of the University of Buffalo and Andrew Glover, Charles M.C. Lee and Phillip Quinn of the University of Washington — wrote in the introduction. “Specifically, the IRS has not ruled on what constitutes a ‘substantially identical’ security, leaving financial advisors to navigate a foggy legal landscape. Some advisors seem to take the regulatory silence as tacit permission to swap ETFs that hold identical securities or that are even benchmarked to the same index (e.g., Lasser 2011). Others argue that if an investor’s economic position has not changed after swapping ETFs, the spirit of the wash sale rule has likely been violated (e.g., Fischer 2010). Against this backdrop of legal uncertainty, the extent to which investors engage in tax avoidance through ETF wash sales remains largely unknown.”

READ MORE: How a newly unified GOP government will affect ETFs

Representatives for the SEC declined to comment on the report’s conclusions and referred questions to the IRS, which didn’t provide a response.

The findings essentially “confirmed what all of us expected,” but “what was striking about the study was being able to demonstrate that the loss harvesting was material enough to be measured,” said Steve Rosenthal, a senior fellow at the Urban-Brookings Tax Policy Center, a nonpartisan think tank.

Tax strategies around possible wash sales have been “going on for decades and decades and decades,” he noted. The rise of ETFs — which topped $10 trillion in assets for the first time in September in a shift fueled by technology, lower fees and tax advantages — has altered the picture. But it’s not clear whether IRS policymakers or members of Congress will try to rein in the wash-sale practices documented in the report.

“I don’t think they view this as high on their agenda, because there’s other tax evasion that goes on. This is lawful, and the question is whether it’s pushing the limits,” Rosenthal said in an interview. “It’s just easier now. There are more vehicles, there are more opportunities, there is more technology to help plan and there are more people marketing these strategies as a result of the ease.”

The study hasn’t been published by a peer-reviewed journal, and the researchers listed some possible “sources of noise” in the data they tracked from quarterly SEC filings of firms’ holdings known as Form 13F and granular trading records from financial technology firm AbelNoser Solutions, a Trading Technologies company. Some swap trades of correlated ETFs could have occurred at random, between the quarterly filings, at lower than 99% matches in their holdings or at an even greater volume when considering the growth of ETFs, the authors wrote.

“Exchange-traded funds provide an efficient way for investors to circumvent the trading frictions associated with the wash sale rule,” Dambra and the other academics wrote. “Specifically, investors can sell a depreciated ETF security and realize a capital loss while simultaneously purchasing another ‘nearly identical’ ETF security. This form of swap trading allows investors to maintain a substantively identical economic position while harvesting a capital loss that can be used to offset realized gains and other taxable income. With an explosion in available ETFs over the past two decades, these securities have become ideal vehicles for circumventing the wash sale rule.”

READ MORE: The most wonderful time of the year, for tax-loss harvesting

Their research suggests that ETFs can offer even greater tax efficiency than many experts have pointed out in the past — or that the IRS may be ignoring the enforcement of a rule that has restricted loss harvesting maneuvers for more than a century.   

“The expansion of ETFs has provided investors with a new, low-cost tool whereby capital losses can be realized without disturbing an optimal portfolio,” the authors wrote. “Similar to the findings in Li (2024), we find the introduction of a near-identical ETF leads to more volume activity for the incumbent ETF. Next, we find that tax-sensitive institutions hold a more diverse set of highly correlated ETFs, invest a larger portion of their AUM in these ETFs, engage in more swapping between near-identical ETFs and capture more capital losses with this swapping activity. We estimate conservatively that capital loss recognition attributable to annual swapping among tax-sensitive institutional investors is in the tens of billions of dollars. While this behavior is becoming increasingly widespread and economically important, regulators have remained silent on where ETFs fit in their definition of ‘substantially identical securities.’ We contribute to the policy discussion on the potential costs of that continued silence.”

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AI-Driven Automation and Continuous Accounting Frameworks

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5 days ago

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August 15, 2026

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The accounting profession is undergoing a fundamental structural transition as enterprise finance departments shift from periodic month-end closes toward automated continuous accounting models. By integrating specialized machine learning algorithms directly into enterprise resource planning (ERP) platforms, chief accounting officers are transforming financial reporting from a retrospective exercise into a real-time operational asset.

The Shift from Periodic Close to Continuous Financial Reporting
Traditional accounting workflows heavily relied on manual data reconciliation, spreadsheet calculations, and multi-week closing cycles at the end of each fiscal period. In contrast, continuous accounting frameworks utilize automated software agents to process, validate, and post transactional data in real time as business activities occur.

Automated bank reconciliation tools cross-reference incoming bank feeds, invoice records, and purchase orders automatically. By resolving transactional variances instantly throughout the month, corporate accounting teams eliminate the traditional workload spikes associated with quarterly and annual closes.

Machine Learning in Audit Trails and Anomaly Detection
Advanced natural language processing (NLP) and machine learning tools are redefining internal audit and financial control environments. Automated systems analyze 100% of general ledger entries, identifying anomalous transactions, duplicate payments, and unauthorized journal entries in real time.

Rather than relying on random statistical sampling, corporate internal auditors can focus their attention on high-risk flags automatically surfaced by algorithmic monitoring platforms. This continuous risk assessment strengthens internal controls over financial reporting (ICFR) and significantly reduces fraud risk.

Evolving Roles for Accounting Professionals
As routine data entry and manual reconciliation tasks become fully automated, the skill set required for accounting professionals is shifting toward data analysis, system design, and strategic business advisory.
– Systems Governance: Accountants are increasingly responsible for monitoring algorithmic accuracy and managing data integration pipelines.
– Business Partnership: Finance professionals leverage real-time financial dashboards to advise operational leaders on margin management and working capital allocation.
– Regulatory Compliance Management: Accounting teams utilize automated platforms to ensure compliance with dynamic tax codes and international accounting standards.

Core Implementation Recommendations
1. Deploy Automated Reconciliation Tools: Integrate continuous transaction processing modules into existing enterprise ERP architectures.
2. Establish Algorithmic Governance Controls: Implement strict internal testing protocols to ensure automated accounting rules comply with GAAP/IFRS standards.
3. Reskill Accounting Teams: Invest in training finance staff on data analytics, workflow automation, and predictive financial modeling.

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Accounting

Global ESG Reporting Standards and Double Materiality Compliance

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1 week ago

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August 9, 2026

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Corporate accounting departments face expanding reporting expectations as international sustainability disclosure standards achieve regulatory enforcement across major global jurisdictions. Chief Accounting Officers (CAOs) and corporate controllers are establishing rigorous internal accounting controls to treat Environmental, Social, and Governance (ESG) metrics with the same data precision, auditability, and governance as traditional financial statements.

Regulatory Harmonization Under Global Sustainability Frameworks
The implementation of standardized sustainability reporting frameworks—notably rules established by international sustainability accounting boards—has created unified expectations for public and large private enterprises. Corporations must report standardized metrics covering greenhouse gas emissions (Scope 1, 2, and material Scope 3), energy utilization, workforce demographics, and supply chain governance.

In Europe and other participating international jurisdictions, double materiality principles are mandatory. Under double materiality, organizations must report both how external sustainability risks impact corporate financial performance, and how internal corporate operations affect surrounding environmental and social structures.

Integrating Sustainability Metrics into Core ERP Systems
To provide auditable non-financial data, enterprise organizations are integrating specialized carbon accounting and ESG management platforms directly into core ERP systems. Automated data collectors capture energy utility invoices, logistics fuel consumption metrics, and vendor compliance records in real time.

Establishing automated, traceable data pipelines ensures that non-financial reporting is supported by clear audit trails. This structured approach allows external financial auditors to provide reasonable assurance on sustainability disclosures during annual corporate reporting cycles.

Financial Impacts and Capital Market Disclosure
Accurate ESG reporting directly influences corporate cost of capital and institutional credit ratings. Commercial lenders and institutional asset managers systematically incorporate sustainability metrics into risk pricing models. Companies that demonstrate transparent, verifiable progress in operational energy efficiency and climate risk mitigation benefit from expanded access to green bond markets and lower debt pricing.

Action Steps for Accounting Leadership
1. Implement Double Materiality Frameworks: Conduct comprehensive assessments to identify material financial and operational sustainability metrics.
2. Build Auditable Non-Financial Data Pipelines: Automate ESG data collection within core accounting software to ensure data integrity.
3. Align Sustainability with Annual Financial Filings: Prepare non-financial disclosures concurrently with financial statements to satisfy regulatory audit expectations.

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Accounting

Modernizing Internal Controls: Machine Learning and Continuous Monitoring in Auditing

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2 weeks ago

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August 5, 2026

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Internal audit departments and corporate risk managers are modernizing internal control frameworks by shifting from periodic sampling techniques to continuous monitoring and machine learning analytics. As operational data volumes increase across enterprise organizations, automated control testing ensures financial integrity, prevents corporate fraud, and streamlines annual audit engagements.

The Limitation of Periodic Audit Sampling
Historically, internal and external auditors evaluated internal controls by reviewing random samples of financial transactions—often analyzing less than five percent of total ledger entries. In complex enterprise environments, periodic sampling methods carry inherent risks of overlooking localized financial misstatements, unauthorized disbursements, or operational control breakdowns.

In 2026, progressive internal audit functions are utilizing automated continuous monitoring platforms that evaluate one hundred percent of financial transactions in real time. Continuous control auditing systems continuously monitor general ledger entries, procurement approvals, and expense reimbursements across all operating subsidiaries.

AI-Powered Fraud Detection and Anomaly Identification
Machine learning models trained on historical corporate financial data excel at identifying subtle transactional anomalies that indicate potential fraud or operational error. Automated systems instantly flag duplicate invoice payments, unapproved vendor creation, unusual journal entry timing, and unauthorized override of authority thresholds.

When an anomaly is detected, the automated auditing platform generates an instant risk alert, allowing internal audit teams to investigate root causes immediately. Early detection prevents minor operational errors from escalating into material weaknesses in financial reporting.

Streamlining External Audit Preparation
Continuous internal control monitoring delivers significant benefits during annual external financial audits. External audit firms can review continuous audit logs and automated control testing documentation, reducing the time required for manual field testing.

This integrated approach lowers overall audit compliance fees, reduces administrative burdens on corporate accounting staff, and provides senior management and audit committees with real-time visibility into the organization’s overall risk profile.

Core Implementation Guidelines
1. Transition to 100% Data Testing: Replace legacy sampling methods with automated continuous audit monitoring systems.
2. Deploy Anomaly Detection Algorithms: Implement machine learning models to identify unauthorized transactions and operational control overrides.
3. Align Internal and External Audit Workflows: Coordinate continuous control testing protocols with external auditors to optimize annual compliance cycles.

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