Since January 2020, artificial intelligence-driven stock predictions have consistently outpaced traditional market benchmarks. I Know First’s Combined Long/Short Strategy, guided by a self-learning AI algorithm, returned +739.39% over this period, compared to +126.68% for the S&P 500. This article examines the data, methodology, and risk profile behind those results.

How AI Stock Predictions Work

I Know First uses a proprietary AI system built on neural networks, genetic algorithms, and chaos theory to generate daily stock predictions across 13,500+ assets. The system processes market data across six time horizons (3-day, 7-day, 14-day, 30-day, 90-day, and 12-month) and produces a signal for each asset, indicating the predicted direction and confidence level.

Every 24 hours, the algorithm re-trains on fresh data, adapting to changing market conditions. This self-learning mechanism is central to the strategy’s ability to dynamically switch between long and short positioning, capturing opportunities on both sides of the market.

The Combined Long/Short Strategy

Cumulative Performance: Jan 2020 – Jun 17, 2026

The chart below tracks the cumulative performance of the Combined Long/Short Strategy (green) against the S&P 500 (grey dashed) from January 29, 2020, through June 17, 2026. The shaded area represents periods where the strategy outpaced the benchmark.

Source: I Know First AI Predictive Algorithm, From January 2020 up until June 17, 2026. Past performance does not guarantee future results.

The strategy’s +739.39% cumulative return compares to +126.68% for the S&P 500 over the same period. This outperformance of +612.71% reflects the algorithm’s ability to capture gains on the long side during bull markets while managing exposure during downturns through short positioning.

Full-Period Performance Metrics

The table below summarizes key risk-adjusted metrics for the strategy versus the S&P 500. Sharpe and Sortino ratios use monthly returns annualized by multiplying by the square root of 12, with a risk-free rate of 4.0% annually.

Notably, the strategy achieved a maximum drawdown of -17.20%, significantly less severe than the S&P 500’s -33.92% peak-to-trough decline over the same period. This reflects the downside protection that short positioning can provide during sharp market corrections.

Annual Returns Breakdown

Year-by-year performance highlights the strategy’s consistency. The table below compares annual returns and maximum drawdown for the I Know First strategy versus the S&P 500 from 2020 through mid-2026.

The strategy delivered positive returns in every year of the period, including 2022 when the S&P 500 declined -19.95%. The AI’s dynamic allocation between long and short positions allowed it to navigate the Federal Reserve rate-hiking cycle that year, returning +15.34%. The maximum annual drawdown never exceeded -17.20%, compared to -33.92% for the index in 2020.

Drawdown Analysis

Drawdown measures how far a portfolio falls from its previous peak at any point in time. A smaller maximum drawdown indicates stronger capital preservation. The chart below compares rolling drawdowns for both strategies.

Source: I Know First AI Predictive Algorithm, From January 2020 up until June 17, 2026. Past performance does not guarantee future results.

The S&P 500’s maximum drawdown of -33.92% occurred during the COVID-19 crash in March 2020. During the same event, the I Know First strategy’s short positioning mitigated exposure, and the drawdown remained contained. The 2022 bear market similarly showed this divergence: where the S&P 500 experienced a sustained decline driven by rate hikes, the strategy’s AI signals shifted toward short positions, limiting peak-to-trough losses.

The Role of AI in Improving Stock Predictions

Traditional stock predictions often rely on fundamental analysis, technical indicators, or macroeconomic forecasts. I Know First’s approach is different: the algorithm processes thousands of market signals simultaneously, identifying non-linear patterns that are invisible to conventional methods.

The self-learning architecture means the model continuously updates its understanding of market dynamics. Each rebalance period is driven by updated AI outputs, not static rules. This adaptability is what allows the strategy to remain relevant across diverse market regimes: rising rates, falling rates, bull markets, and corrections.