AI Stock Picker

Screening Filters

1. Market Cap ≥ $15,000,000,000

   • Purpose: Focus on large, established companies.
   • Rationale:
     • A typical “AI stock picker” approach will often prioritize stability and liquidity so results are investable in size and less prone to manipulation.
     • Large-cap companies tend to have more diversified businesses, better access to capital, and more consistent disclosure, which makes them more suitable for systematic or AI-driven selection.

2. PriceAboveMA200 (Price above 200-day moving average)

   • Purpose: Ensure stocks are in a longer-term uptrend or at least not in a prolonged downtrend.
   • Rationale:
     • An AI-style or quantitative approach often incorporates trend-following or momentum to avoid stocks in persistent decline.
     • Price trading above the 200-day moving average is a common technical filter signaling positive or recovering sentiment and reduces the chance of catching “falling knives.”

3. List Exchange: XNYS, XNAS, XASE (NYSE, NASDAQ, NYSE American)

   • Purpose: Restrict to major U.S. exchanges.
   • Rationale:
     • These exchanges have higher listing standards, better liquidity, and better data quality—important for any systematic or AI-based screening.
     • It avoids many lightly traded OTC or foreign listings where spreads are wider and information quality is poorer.

4. Net Margin ≥ 12%

   • Purpose: Target companies with solid profitability.
   • Rationale:
     • AI/quant models often emphasize quality factors like profit margins to distinguish durable businesses from weak or purely speculative ones.
     • A 12% minimum net margin helps capture companies that can convert revenue into meaningful bottom-line earnings, which tends to support more resilient business models.

5. Revenue 5-Year CAGR ≥ 15%

   • Purpose: Focus on companies with strong, sustained growth.
   • Rationale:
     • A growth threshold of 15% over 5 years is fairly demanding and aligns with the idea of finding dynamic, expanding businesses—often where innovation and technology (including AI) can be meaningful drivers.
     • This filter helps screen out stagnant or slow-growing firms, keeping the universe tilted toward higher-growth profiles.

6. P/E (TTM) between 12 and 32

   • Purpose: Balance valuation—avoid both extremely cheap (potentially distressed) and extremely expensive (highly speculative) stocks.
   • Rationale:
     • A lower bound of 12 helps remove companies that may be “value traps” or priced very low due to serious underlying issues.
     • An upper bound around 32 keeps you away from the most aggressively priced names where expectations may be unrealistically high.
     • This range fits a “reasonable growth at a fair price” philosophy that many systematic or AI-based strategies adopt to manage risk.

Why Results Match the “AI Stock Picker” Idea

• The filters mimic a disciplined, quantitative framework: combining quality (profit margin), growth (5-year revenue CAGR), trend (price above 200-day MA), size/liquidity (large-cap, major exchanges), and valuation discipline (P/E range).
• This multi-factor design is consistent with how a well-built AI/quant model would narrow down a broad universe into a smaller list of robust, growth-oriented, yet not wildly overvalued stocks.