How our AI forecasting system works — from the challenge of predicting markets, to the data we use, the three prediction horizons, and the strategies that turn signals into action.
Financial markets are notoriously difficult to predict. Even professional investors struggle to consistently beat the market. The S&P 500, representing America's largest 500 companies, moves based on countless factors: economic data, company earnings, global events, investor sentiment, and more.
Why is this so hard? Because markets are influenced by millions of people making decisions based on incomplete information, emotions, and unpredictable events. A company's stock might drop because of a CEO's tweet, rise because of a breakthrough in another industry, or move based on economic data that won't be released for weeks.
But what if we could identify patterns that humans miss?
Fear and greed drive many investment decisions, leading to poor timing. How many times have you sold stocks during a market crash out of fear, only to miss the recovery?
Even professional analysts can only monitor a limited number of indicators at once. They might notice that interest rates are rising, but miss that this is happening alongside improving corporate earnings and falling unemployment.
Our machine learning model analyzes Loading... simultaneously, finding relationships that would be impossible to spot manually. It never sleeps, never gets emotional, and processes thousands of data points to make objective predictions.
Our model doesn't just make one prediction — it makes three, each optimized for different investment strategies. Why three horizons? Because different investors have different time frames and risk tolerances. A day trader needs different information than someone saving for retirement.
Instead of trying to predict exact prices (which is nearly impossible), our model makes a simpler, more practical prediction:
Perfect for active traders who want to capitalize on short-term market movements driven by quarterly earnings, economic reports, and market sentiment.
Responds quickly to changing conditions but may generate more false signals due to market noise.
Ideal for swing traders and medium-term investors who want to capture business cycles and seasonal patterns in the market.
Balances responsiveness with stability — not too jumpy, not too slow. Captures quarterly business cycles effectively.
Best for long-term investors focused on fundamental economic trends rather than short-term market noise.
Our most accurate model because longer timeframes smooth out market volatility. Ideal for retirement planning.
Different investors have different time frames and risk tolerances. A day trader needs different information than someone saving for retirement. Our model provides signals optimized for each investment style.
The key insight: While individual market movements seem random, there are underlying patterns in how the market responds to economic conditions over time. Our model finds these patterns by analyzing how the market has behaved in similar economic situations throughout history.
Our model analyzes over Loading... different economic indicators from multiple sources. Why so many indicators? Because the market is influenced by many factors simultaneously. Just like a doctor needs to check multiple vital signs to diagnose a patient, our model needs to examine multiple economic “vital signs” to predict market direction.
The magic happens in the relationships: While any single indicator might be misleading, the combination of Loading... reveals the true economic picture. For example, if interest rates are rising (usually bad for stocks) but corporate earnings are growing rapidly (good for stocks), our model weighs both factors to make a balanced prediction.
All of this data goes back to 1970 — over 50+ years of market history to learn from!
S&P 500 Price & Returns
Current market levels and momentum
VIX (Fear Index)
Market volatility and investor sentiment
Volume & Volatility
Trading activity and market stress
P/E Ratios
How expensive stocks are relative to earnings
Dividend Yields
Income generated by the market
Shiller P/E
Long-term valuation metrics (inflation-adjusted)
GDP Growth
Overall economic health
M2 Money Supply
How much money is in the economy
10-Year Treasury Rates
Interest rate environment
Federal Reserve Policy
Monetary conditions
Buffett Indicator
Market cap vs. GDP (Warren Buffett's favorite)
Momentum Indicators
How trends are developing
Mean Reversion Signals
When markets are oversold or overbought
Market Regime Detection
Identifying bull vs. bear markets
Markets go through cycles — bull markets, bear markets, recessions, booms. By training on 50+ years of data, our model has seen every type of market condition.
This extensive history helps the model recognize patterns that repeat across different market cycles.
We use two sophisticated strategies to convert our model predictions into actionable investment decisions. Instead of an all-or-nothing approach, we scale our market exposure based on how confident we are.
These strategies help maximize returns when we're confident, manage risk when uncertain, and reduce portfolio volatility compared to buy-and-hold approaches.
“When in doubt, go with the majority”
“Size your bets based on conviction”
Every day, our system automatically collects data, runs predictions, and updates the forecasts. This automated process ensures you always have the most current market analysis available.
Fresh economic indicators from multiple sources including FRED, Yahoo Finance, and Multpl.com
Three models analyze data for different horizons, looking for patterns that have historically predicted market direction
Clear LONG/CASH signals with position sizing based on model consensus and conviction levels
Every quarter, we add new market data and retrain our models to ensure they stay current with changing market dynamics. Markets evolve over time, and economic relationships that worked in the 1980s might not work in the 2020s.
Quarterly retraining ensures our models stay current with changing market dynamics while not being so frequent that they chase noise.
Dive deeper into the CatBoost architecture, feature engineering framework, cross-validation methodology, and risk management framework on our Methodology page.