QuantumStreet AI Launches Long-Short Equity Strategy on The Back of Strong Performance

The AI-driven investment strategy helps investors navigate market volatility from geopolitical fractures and elevated valuations

NEW YORK, March 12, 2026 /PRNewswire/ -- QuantumStreet AI ("QuantumStreet"), an IBM partner company specializing in AI-driven investment solutions, today announced the launch of its Long-Short Global Equity strategy, designed to provide institutional investors with enhanced global equity exposure and improved risk-return characteristics. This product launch follows recent outperformance across a range of its long-only and long/short strategies underpinning $7.5 billion in client assets. 

At a time when elevated valuations, trade disruption and geopolitical instability are putting pressure on traditional portfolios, the strategy gives allocators a systematic way to maintain equity market exposure while hedging against drawdowns.

"Equity markets are absorbing geopolitical shocks, trade disruption and stretched valuations simultaneously, and that's exactly the kind of environment where the ability to go both long and short matters most," said Art Amador, President at QuantumStreet AI.

The strategy employs a long-short 130/30 framework, maintaining 130% long and 30% short gross market value, benchmarked against the MSCI World Index. It is built on QuantumStreet's proprietary machine learning system, which integrates structured financial data (including macro, fundamental and technical signals) with unstructured data sources such as global news flow, earnings call transcripts and regulatory filings.

In backtesting from February 2012 through November 2025, the strategy produced an annualized return of 22.91%, compared with 11.50% for the MSCI World Index over the same period. Its Sharpe ratio—a measure of return earned per unit of risk taken—was 1.41, versus 0.82 for the benchmark, meaning the strategy generated meaningfully better performance for each unit of volatility investors absorbed. Across five- and 10-year periods, the strategy more than doubled the index's return while adding only modest volatility.

Chris Natividad, CIO at QuantumStreet added: "Our AI is evaluating macro conditions, fundamentals, technicals, news sentiment and regulatory filings across thousands of securities at the same time, identifying cross-regime patterns and sector dislocations. The scale of unstructured data easily produces unacceptable levels of "noise" if you do not have the right range of models to drill down to alpha-producing signals."

The strategy's underlying machine learning system uses proprietary knowledge graphs to connect policy language, macro impacts, sector sensitivity, earnings data and global news flow, generating investment signals that adapt dynamically to shifting market regimes. All signals are fully explainable through the SHAP (SHapley Additive exPlanations) framework, which decomposes each forecast into the individual contributions of its underlying drivers. This gives portfolio managers full transparency into why the model is recommending each position, providing the confidence to explain performance and strategy to underlying beneficial owners.

The strategy is available to asset allocators globally, including pensions, endowments, foundations, insurers and asset managers seeking an AI-enhanced alternative to traditional long-short equity approaches.

About QuantumStreet AI

Founded in Silicon Valley as an IBM partner company, QuantumStreet AI is a global leader in AI-powered investment solutions, and its technology underpins over $7.5 billion in globally deployed strategies. Its investment solutions leverage advanced deep-learning, explainable models, and large-scale data analytics to build transparent, rules-based portfolios across major asset classes. These are used by banks, asset managers, insurers, pension plans, and institutional investors seeking data-driven, superior outcomes. For more information, visit quantumstreetai.com

Note: Performance figures cited are based on backtested, hypothetical results. Past or simulated performance is not a reliable indicator of future returns. Backtested results have inherent limitations and do not reflect actual trading, advisory fees, or other costs. Alternative modeling techniques or assumptions might produce significantly different results.

Media Contact:
Sam Barber
Pitchr.ai
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SOURCE QuantumStreet AI