Abstract
Tax-aware long-short (TALS) strategies have been demonstrated to deliver superior after-tax outcomes compared to traditional tax-loss harvesting methods such as direct indexing. By leveraging gain deferral and loss realization techniques within actively managed long-short factor portfolios, TALS can produce substantial cumulative net capital losses while simultaneously achieving pre-tax alpha. However, traditional TALS implementations remain operationally complex, capital-intensive, and accessible only to high-net-worth (HNW) and ultra-high-net-worth (UHNW) investors. In this paper, we present alphaAI’s ETF-based framework for TALS, which preserves the core benefits of long-short tax efficiency while addressing key limitations of existing implementations. Our approach expands access to TALS by using liquid exchange-traded funds (ETFs) instead of individual securities, enabling broader adoption by retail investors and simplifying operational complexity for advisors. We explore potential use cases, structural configurations, and implementation benefits, positioning alphaAI’s ETF TALS strategy as a powerful new tool for tax-aware portfolio management.
Introduction
Tax-aware investing is an increasingly important area of innovation in asset management, particularly for taxable investors seeking to minimize tax drag and optimize after-tax returns. Among the most impactful advancements in this field is the emergence of tax-aware long-short (TALS) strategies, which utilize factor-driven long-short portfolios to generate sustained tax alpha. Unlike traditional long-only tax-loss harvesting approaches, such as direct indexing, TALS strategies incorporate short positions and utilize systematic portfolio turnover to defer gains and realize losses more effectively. In recent years, TALS has been shown to outperform direct indexing both in terms of cumulative net capital losses and pre-tax performance.
However, despite its theoretical and empirical appeal, traditional TALS implementations remain largely inaccessible to the broader investor base. The requirements for individual stock selection, margin and shorting capabilities, and active rebalancing necessitate complex operational infrastructure and high account minimums. As a result, TALS has been restricted to separately managed accounts (SMAs) for institutional and ultra-high-net-worth (UHNW) clients. This paper seeks to address these limitations by introducing a novel ETF-based implementation of TALS, developed by alphaAI. By substituting individual stock positions with diversified exchange-traded funds (ETFs), alphaAI's framework delivers the core benefits of TALS while eliminating the barriers to entry that have historically confined it to a narrow segment of the investing public.
The Case for TALS over Direct Indexing
Direct indexing, long heralded as an innovation in tax-efficient investing, allows investors to replicate a broad market index using individual securities. This granular control facilitates the harvesting of tax losses at the individual stock level, which can be used to offset capital gains and reduce taxable income. While effective in the early years of implementation, direct indexing strategies suffer from diminishing marginal returns over time. As positions appreciate, the opportunity to realize tax losses declines. Furthermore, the long-only constraint of direct indexing limits its flexibility in navigating volatile or upward-trending markets.
Recent empirical work, particularly by Krasner and Sosner (2024) and Liberman et al. (2023), has demonstrated that tax-aware long-short strategies can overcome many of the inherent constraints of direct indexing. By integrating both long and short positions, TALS strategies are capable of generating capital losses as a natural function of their trading activity. These losses are not merely the result of deliberate tax trades but emerge from the implementation of alpha-driven portfolio rebalancing. In simulated environments, TALS portfolios have been shown to realize cumulative net capital losses exceeding 100% of the initial investment within three years. This level of loss generation is orders of magnitude higher than that observed in direct indexing, where the typical ceiling on net losses is approximately 30% over the entire lifetime of the strategy.
In addition to superior tax outcomes, TALS strategies also exhibit stronger pre-tax performance. Because they are grounded in factor investing models, such as value, momentum, and quality, they have the potential to deliver alpha relative to a benchmark. This dual objective, achieving pre-tax alpha while maximizing after-tax efficiency, positions TALS as a more comprehensive solution for taxable investors. However, despite these advantages, the practical limitations of traditional TALS have hindered widespread adoption.
Limitations of Traditional TALS Implementations
The most significant obstacle to the democratization of TALS strategies is their reliance on individual security selection. Managing a long-short portfolio composed of hundreds of individual equities requires advanced infrastructure, including tax-lot level accounting, margin access, and systematic portfolio rebalancing engines. These requirements translate into high operational costs and necessitate large account sizes to be economically viable. Minimum investments for traditional TALS strategies often begin at $1 million, effectively excluding the vast majority of retail investors and financial advisors operating below the ultra-high-net-worth threshold.
In addition, TALS strategies that rely on individual stocks may interfere with clients' existing holdings. For example, an investor with a concentrated position in a legacy stock may not wish to liquidate or short related names, which could lead to unintended exposures or tax consequences. Traditional TALS frameworks are also relatively inflexible, making it difficult to align them with bespoke investment mandates or to deploy them across diverse advisor platforms.
These structural challenges have largely limited TALS strategies to custom SMAs offered by institutional managers or hedge funds. Although these implementations have proven effective for the small subset of investors who can access them, the broader market remains underserved. The absence of scalable, accessible TALS solutions has created a gap between the potential of tax-aware long-short investing and its practical availability. alphaAI's ETF-based approach seeks to bridge this gap.
alphaAI's ETF-Based TALS Framework
To address the limitations of traditional TALS strategies, alphaAI proposes a new framework that utilizes ETFs as the core building blocks for constructing long-short tax-aware portfolios. Rather than selecting individual equities, alphaAI constructs a diversified portfolio of long and short ETF exposures. These ETFs are selected to match the underlying index on a net basis, providing the desired exposure while dramatically simplifying implementation.
Consider a simple example where a client wants to match the S&P 500. In such a case, we could go long 200% SPY and short 100% VOO, which are both ETFs that track the S&P 500. The client’s net exposure would match the S&P 500, while the VOO short would generate losses. We would realize those losses by swapping VOO for an alternative ETF. This is merely a simple example that assumes benchmark tracking. alphaAI’s models can be customized to a wide range of investment outcomes, including aiming to beat a benchmark, generating alpha, and targeting a set level of annual portfolio volatility.
By using ETFs, alphaAI eliminates the need for granular tax-lot accounting and significantly reduces operational complexity. ETFs offer deep liquidity, narrow bid-ask spreads, and transparent holdings, making them ideal instruments for systematic portfolio management. In alphaAI's framework, portfolios can be constructed with leverage ratios such as 200% long and 100% short, achieving both desired tracking error and tax efficiency.
The ETF-based TALS strategy retains the essential mechanics of its traditional counterpart. Gains are deferred by holding onto appreciated long positions, while losses are harvested from short positions. Portfolio turnover is driven by alphaAI's proprietary artificial intelligence models, which dynamically identify opportunities for both alpha generation and tax efficiency. Importantly, this approach allows for seamless integration into existing advisory workflows and can be deployed at lower minimums ($20,000), making it suitable for a wide range of clients.
Backtesting indicates that ETF-based TALS portfolios are capable of generating cumulative net capital losses at levels similar to those produced by traditional TALS strategies. Moreover, the ability to align the portfolio with investor-specific benchmarks or volatility targets enhances its versatility. Whether the goal is to hedge capital gains, smooth taxable income, or generate after-tax alpha, the ETF TALS framework offers a flexible and powerful solution.
Tax Management Applications and Strategic Use Cases
The primary value proposition of alphaAI's ETF-based TALS strategy lies in its ability to generate losses that can be used to offset other taxable gains. These losses can be applied in a variety of scenarios. For investors who engage in active trading, the strategy can be used to neutralize short-term gains. For those undergoing significant liquidity events, such as the sale of a business or real estate, the strategy can provide an effective tool for minimizing the associated tax burden. Additionally, in years without capital gains, the strategy can be used to offset ordinary income up to the IRS limit of $3,000 per year, with the balance carried forward indefinitely.
The ETF-based TALS structure also supports strategic tax planning across multiple years. Investors can build a "loss bank" during years of market volatility or downturns and draw upon it in future periods. For clients nearing retirement or planning wealth transfers, the strategy offers a mechanism to manage embedded gains and optimize estate planning outcomes.
Upon exiting the strategy, alphaAI provides multiple pathways. The portfolio can be fully liquidated and transitioned to a long-only asset allocation at the more favorable long-term capital gains tax rate. Alternatively, the long book can be gradually adapted into the advisor's recommended strategy, ensuring continuity and minimizing disruption.
Advisor Enablement and Broader Industry Implications
The ETF-based TALS model aligns with key trends in the advisory landscape, including the shift toward tax-aware portfolio construction, automated rebalancing, and scalable personalization. For financial advisors, it represents a turnkey solution that can be deployed without disrupting existing workflows. Because ETFs are broadly supported across custodians and platforms, the strategy can be implemented with minimal operational overhead.
This accessibility has profound implications for the industry. Advisors who previously lacked the infrastructure or client base to offer TALS strategies can now deliver institutional-grade tax efficiency to a wider range of clients. This democratization of tax-aware investing opens new avenues for differentiation, client retention, and value creation.
Conclusion
alphaAI's ETF-based tax-aware long-short strategy represents a breakthrough in tax-efficient investing. By preserving the fundamental benefits of gain deferral, loss realization, and pre-tax alpha generation, while eliminating the operational constraints of traditional implementations, this framework opens the door for broader adoption of TALS strategies. For investors and advisors alike, it offers a scalable, accessible, and powerful tool for managing taxes and enhancing after-tax returns.
References
- Krasner, S., & Sosner, N. (2024). Loss Harvesting or Gain Deferral? A Surprising Source of Tax Benefits of Tax-Aware Long-Short Strategies. The Journal of Wealth Management.
- Liberman, J., Krasner, S., Sosner, N., & Freitas, P. (2023). Beyond Direct Indexing: Dynamic Direct Long-Short Investing. The Journal of Beta Investment Strategies.
- Sosner, N., Gromis, M., & Krasner, S. (2022). The Tax Benefits of Direct Indexing: Not a One-Size-Fits-All Formula. The Journal of Beta Investment Strategies.
- Israel, R., & Moskowitz, T. (2012). How Tax Efficient Are Equity Styles? Chicago Booth Research Paper No. 12-20. SSRN: http://ssrn.com/abstract=2089459.
- AQR Capital Management. (2024). Our Research into Tax-Aware Long-Short Investing: Clarifying a Few Important Things.