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Separately Managed Accounts (SMAs) have become increasingly popular in the digital assets industry, offering a personalized and transparent way to manage investments. This article focuses on how SMA allocators – including family offices, multi-strategy platforms, hedge funds, and high-net-worth individuals (HNWIs) – can manage the complexities of SMAs, from due diligence to active allocation management. In addition, this piece explores what SMAs are, why they are gaining traction, their operational setup, and how AI-powered tools like GenieAI are transforming their management and performance monitoring.
SMAs are investment accounts owned by individual clients, family offices, allocators, and investment funds, but managed by professional trading teams. Unlike traditional pooled funds, SMAs provide full transparency, customizability, and control over asset ownership and trading strategies. In the volatile world of crypto, this transparency and tailored approach resonate strongly with both individual and institutional investors.
With customization options, clients can align their portfolios to specific strategies, risk tolerances, and goals. Direct ownership of assets ensures full transparency, offering visibility into trades, holdings, and fees. Investors retain control over decision-making, including deposits and withdrawals, while individual ownership can also enable tailored tax strategies for enhanced efficiency.
For trading teams managing SMAs, operational efficiency is crucial. Many rely on subaccounts from exchanges like OKX, Bybit, Binance, Coinbase Prime, and Deribit. These subaccounts ensure client trading activities are segregated for better risk management. With API integration, teams can execute trades and manage risk at scale. Additionally, custom permissions offer granular access controls, allowing managers to trade securely without compromising client data.
For allocators evaluating trading teams to manage SMAs, rigorous quantitative and qualitative due diligence is non-negotiable. First, ensuring the trading team’s historical performance is credible and consistent is critical. Typically, trading teams will send a factsheet with an overview of the trading strategy (or strategies) as well as a CSV of daily strategy returns. It’s paramount for allocators to be able to verify the accuracy of this information via direct testing of a set of test API keys provided by the trading team.
Allocators also need to understand the strategies employed, including their risk-return profiles and historical drawdowns. Metrics such as annual return, monthly returns, Sharpe ratio, Sortino ratio, max daily and monthly drawdown are the first quantitative metrics needed to analyze a strategy. Additional valuable metrics are correlations with existing strategies employed by the allocator, correlations and betas with benchmarks like Bitcoin, historical market exposure/neutrality, trading fees, as well as performance across various market regimes.
Assessing the trading team’s qualifications, experience, and ability to manage diverse market conditions provides vital qualitative insights into a trading team. For systematic strategies, it’s paramount to understand the trading team’s technical expertise in generating signals and integrating into strategies with institutional-grade capacity. Strategy capacity is quite important, as trading teams will be generally deploying the same strategy across multiple allocators allocating from a few hundred thousand to a few millions of dollars per strategy. Watching capacity is essential to avoid significant alpha decay.
Lastly, allocators need to evaluate infrastructure for security, compliance, and scalability. API connectivity to the exchanges and trading venues needs to be robust and bug-free to ensure that the discrepancy between the paper-traded strategy and the live strategy is minimized.
Post-launch, allocators are tasked with ongoing monitoring and evaluation of their trading strategies. AI-powered tools are redefining how allocators and trading teams manage and monitor SMAs, offering unparalleled insights and operational efficiency.
Allocators need to monitor and optimize strategy performance once it’s live. This includes granular P&L calculations across multiple timeframes, such as daily, weekly, monthly, and rolling periods, to identify trends. Trade-level insights, including metrics like win rates, profit-to-loss ratios, and execution quality, help assess a strategy's effectiveness, such as a 65%-win rate and a 1.8 profit-to-loss ratio reflecting strong risk management. Additionally, cumulative and attribution analysis is crucial for tracking long-term performance and identifying the trades or market conditions driving results.
Allocators need to assess how strategies compare to benchmarks or peers to make informed decisions. This includes evaluating both raw and risk-adjusted returns (e.g., Sharpe and Sortino ratios) against other strategies or indices. They also require the ability to tailor benchmarks to reflect specific exposures, such as comparing a BTC-denominated strategy's performance to BTC’s baseline return. Additionally, alpha and beta analysis is essential for understanding how strategies generate alpha and their correlation to broader market movements or factors.
For SMAs denominated in crypto assets like BTC, ETH, or other coins, allocators need specialized tools. These include in-kind return calculations to track returns in the native asset rather than fiat currency, which is crucial for crypto-native investors. Additionally, relative performance analysis allows for comparing strategy results to the performance of the underlying coin, helping assess the incremental value generated by the strategy.
Understanding how strategies perform across different market conditions is vital for managing SMAs effectively. Allocators need regime-specific insights to analyze performance during bull, bear, and sideways markets, helping identify strategy strengths and weaknesses. Stress testing is also essential, allowing for the simulation of extreme scenarios, such as rapid market crashes or volatility spikes, to evaluate a strategy's robustness. Additionally, dynamic behavioral analysis can detect shifts in trading behavior across regimes, enabling managers to adjust proactively and optimize strategy performance.
AI enhances the ongoing monitoring of SMAs by providing real-time data and proactive alerts. Anomaly detection flags unexpected deviations in performance or risk metrics, such as sudden drawdowns or increased leverage. Performance threshold alerts notify managers when strategies exceed or fall below predefined limits, enabling timely interventions to maintain optimal performance and risk management.
AI-driven optimization tools enable dynamic adjustments to portfolio allocations, maximizing returns while minimizing risks. Dynamic strategy weighting adjusts exposure based on market conditions, risk tolerance, and objectives. Correlation analysis ensures portfolios benefit from diversification by analyzing the interdependencies between strategies. Additionally, efficient frontier analysis optimizes portfolios to achieve the best possible return for a given level of risk, enhancing overall performance.
Generative AI capabilities, combine with robust quantitative models, provide deep performance insights, helping allocators and managers understand not just the "what" but the "why" behind results. Attribution analysis identifies the drivers of performance, such as specific trades, strategies, or market factors. Market regime comparisons help understand how strategies perform under varying conditions, allowing for better positioning of portfolios. Additionally, benchmarking and peer analysis enable comparisons across multiple dimensions to ensure competitive performance, offering valuable insights for informed decision-making.
GenieAI has emerged as the leading software solution for allocators and trading teams managing SMAs in the crypto space. Its Portfolio and Risk Management System offers unique capabilities, including data consolidation that aggregates performance, risk, and exposure data across exchanges like Binance, Bybit, OKX, Coinbase Prime, Deribit, and others. Advanced analytics provide real-time insights, attribution analysis, and benchmarking tailored to each SMA. The AI-powered optimization leverages proprietary algorithms to dynamically optimize allocations across multiple SMAs. With a user-friendly interface, allocators and managers can easily track performance and risks. Custom reporting capabilities enable trading teams to generate detailed client reports in seconds, enhancing transparency and communication.
As crypto markets continue to evolve, SMAs are poised to play an even larger role in attracting institutional investors. With GenieAI, trading teams and allocators gain access to cutting-edge tools that simplify SMA management while enhancing performance and transparency.
GenieAI is the premier, award-winning AI-powered Portfolio and Risk Management System helping digital asset funds build and protect their alpha. Our technology enables investors to anticipate market trends and navigate risks in ways that were once unimaginable -- all while managing portfolios with unprecedented precision and workflow automation.
GenieAI’s PMS/RMS is complemented by GenieAI’s AI Lab, a cutting-edge technology platform empowering asset managers with advanced tools to drive their in-house R&D. AI Lab helps the world’s leading digital asset managers drive their innovation at lightning speed.
GenieAI’s leadership team is a powerhouse of expertise, including PhDs specialized in Machine Learning and Artificial Intelligence with a focus on financial applications, Wharton MBAs in Finance, and seasoned capital markets professionals.
GenieAI is a winner of Hedgeweek’s 2024 Risk Management Solution of the Year Award, recognizing our leadership in AI and PMS/RMS technology. GenieAI is backed by leading investors, including Sierra Ventures, Coinbase Ventures, Bain Capital Ventures, Venrex, and Goodwater Capital.
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