I Know First Machine Learning Strategy Delivers +9.76% in Q1 2026 While Point72, Citadel Wellington, Millennium and Balyasny Suffer Losses

I Know First AI Strategy: March & Q1 2026 Results

I Know First’s Combined Long/Short Strategy — powered by deep learning, neural networks, and genetic algorithms — delivered consistent positive returns throughout Q1 2026, a quarter in which both major equity indices and the world’s largest hedge funds recorded losses.

The strategy takes a fully directional position per approximately 28-day rebalance cycle: fully long or fully short, based entirely on the AI algorithm’s multi-horizon signal and predictability scores. This systematic, rules-based structure eliminates the discretionary bias that caused multi-strategy platforms to hold crowded positions into the March volatility spike.

Source: I Know First AI Predictive Algorithm, From January 2026 up until March 31, 2026. Outperformance = IKF return minus S&P 500 return.

Figure 1: March 2026 — I Know First AI Strategy vs. S&P 500 and major hedge funds.
Source: I Know First AI Predictive Algorithm | Reuters (April 1, 2026) | Preliminary figures.

How AI Identified Opportunities While Hedge Funds Struggled

The question institutional investors should be asking after Q1 2026 is not why hedge funds lost money — it is why AI-driven systematic strategies found opportunities in the same environment where discretionary managers failed. The answer lies in the fundamental architecture of machine learning-based forecasting.

I Know First’s proprietary algorithm processes thousands of market signals daily across multiple time horizons simultaneously. Unlike human portfolio managers, the algorithm carries no anchoring bias, no career risk, and no organizational pressure to hold a position beyond the evidence. When geopolitical signals and cross-asset correlations shifted in March, the algorithm’s multi-horizon confirmation framework — requiring directional agreement across timeframes before sizing a position — systematically avoided the sector concentrations that produced losses across the industry.

Why Markets and Hedge Funds Declined in March 2026

To appreciate why AI-powered systematic strategies outperformed, it is important to understand the specific dynamics that caused losses across equity markets and discretionary hedge fund platforms in March 2026.

The primary catalyst was a sudden and severe escalation of the conflict in the Middle East involving Iran. The shock triggered a rapid repricing of risk assets across global equity markets, producing what market analysts described as a “4-standard-deviation” volatility event. The S&P 500 dropped -5.09% in March, bringing Q1 2026 losses to -4.63%. The Nasdaq declined approximately -6% for the quarter, with large-cap technology names driving the index-level decline.

The losses in discretionary hedge funds were amplified by a structural problem: crowded positioning. Multiple large platforms held overlapping long exposures in the same sectors and factor tilts. When the Iran shock triggered simultaneous de-risking, correlations that appeared low under normal conditions converged sharply. Forced selling beget more selling — a classic de-grossing loop that disproportionately hurt the pod-shop model.

Fundamental long/short equity managers were hardest hit, with the median fund down -4.77% in March according to Goldman Sachs data. By contrast, systematic and AI-driven equity strategies, which rebalance on fixed rules rather than discretionary conviction, navigated the same environment with significantly lower drawdowns and, in I Know First’s case, positive returns.

Confirmed Hedge Fund Returns: March & Q1 2026

The following performance data is sourced directly from Reuters reporting (April 1, 2026), citing persons familiar with the respective funds. All figures are preliminary.

Source: Reuters reporting, April 1, 2026 (hedge fund figures — preliminary). I Know First AI Predictive Algorithm, From January 2026 up until March 31, 2026.

Figure 2: Q1 2026 — I Know First AI Strategy vs. major indices and hedge funds.
Source: I Know First AI Predictive Algorithm | Reuters (April 1, 2026) | Preliminary figures.

Six Years of AI-Driven Outperformance: Since Inception (2020)

Q1 2026 is not a one-quarter story. The I Know First Combined Long/Short Strategy has compounded consistently over six years and four distinct market regimes: the COVID-19 equity crash of March 2020, the 2020 recovery, the 2022 rate-driven bear market, and now the 2026 geopolitical drawdown. Across all of them, the AI algorithm has demonstrated a consistent ability to identify directional opportunities that generate superior risk-adjusted returns versus the benchmark.

Source: I Know First AI Predictive Algorithm, From January 2020 up until March 31, 2026. Risk-free rate = 4%. Annualized volatility = monthly standard deviation × √12. Partial months excluded from risk metric calculations.

Figure 3: I Know First AI Strategy vs. S&P 500 — cumulative % return since inception (January 2020).
Source: I Know First AI Predictive Algorithm, From January 2020 up until March 31, 2026.

A Sharpe ratio of 1.87 versus 0.47 for the S&P 500 — and a Sortino ratio of 3.41 versus 0.73 — confirm that I Know First’s outperformance has been generated with well-managed downside risk. The maximum drawdown of -14.75% across six years compares favorably to the S&P 500’s -24.77% peak-to-trough decline over the same period. These are not returns generated through excessive concentration or leverage. They reflect systematic, AI-driven stock selection applied consistently across multiple distinct market environments.

The Case for AI-Powered Investing in 2026 and Beyond

March 2026 has provided institutional allocators with a live stress test that separates systematic AI-driven strategies from discretionary human management. The data is clear: while the hedge fund industry faced its worst monthly drawdown since 2022, machine learning-based strategies identified and captured positive returns in the same environment.

The structural advantages that produced these results are not cyclical. Predictive analytics, deep learning signal generation, neural network pattern recognition, and genetic algorithm optimization are compounding capabilities — they improve with more data and more market cycles. The I Know First algorithm has now operated through more than six years of real-world market conditions, continuously learning and adapting to new regimes.

For institutional investors, family offices, and sophisticated allocators evaluating AI-powered portfolio management, Q1 2026 offers a compelling data point: a +14.39% outperformance over the S&P 500, positive returns during the hedge fund industry’s worst quarterly drawdown in years, and a six-year Sharpe ratio of 1.87 versus 0.47 for the benchmark. The investment management industry is at an inflection point. Systematic AI-driven forecasting — built on machine learning, neural networks, and quantitative strategy — is increasingly the primary engine of institutional equity alpha generation.