aaronShort answer: Yes — AI can surface undervalued stocks/ETFs fast, but it won’t replace judgment. Done right, it narrows a long list to a high-quality shortlist you then validate manually.
The problem: There’s too much data, hidden biases, and noise. Human investors miss patterns or get stuck in confirmation bias; manual screening is slow.
Why it matters: Efficient screening saves time, reduces emotional errors, and creates repeatable, measurable decisions for long-term portfolios.
Experience / key lesson: I’ve used AI to automate screening and scoring; the best outcome is a ranked watchlist with explicit score components you can backtest. The model helps optimize process — not make the final buy decision.
- What you’ll need
- Data source (free: Yahoo Finance / Alpha Vantage; or paid: Bloomberg/Refinitiv)
- Spreadsheet or database
- Access to an AI model (ChatGPT, Claude, or a small LLM)
- Clear criteria: valuation, growth, balance sheet, cash flow, and moat proxies
- How to do it — step by step
- Collect last 5 years of financials and market prices for your universe (US stocks / ETFs).
- Define the rule set: e.g., P/E vs sector median, PEG <1.2, P/B <1.5, ROE >10%, FCF positive, Debt/Equity <0.8.
- Use AI to score each name on a weighted rubric (fundamentals 60%, growth 20%, balance 10%, margin-of-safety 10%).
- Sort and produce top 10–30 candidates; ask AI for concise risk notes per name.
- Manual review: validate business model, management, and competitive moat; adjust scores.
- Paper-trade or allocate a small test weight, track performance vs benchmark for 12–36 months.
What to expect: Within a week you’ll have a ranked shortlist and risk notes. Expect false positives — use a multi-month track record to evaluate effectiveness.
Metrics to track (KPIs):
- Number of candidates screened per week
- Average composite score of selected names
- Portfolio CAGR vs S&P 500/benchmark (12–36 months)
- Max drawdown and volatility
- Hit rate: % of picks beating benchmark annually
Common mistakes & fixes:
- Overfitting rules to past winners — fix: keep rules simple and test out-of-sample.
- Ignoring fees/taxes/position-sizing — fix: include realistic frictions in simulations.
- Blindly trusting headline metrics — fix: read the footnotes, check cash flow.
AI prompt (copy-paste):
Act as a disciplined investment analyst. Using the most recent 5 years of financials and market-data, screen US-listed stocks (exclude OTC/penny) for potential long-term undervaluation. Use these filters: P/E below sector median, PEG <=1.2, P/B <=1.5, ROE >=10%, 5-year revenue and EPS CAGR >=5%, Debt/Equity <=0.8, positive free cash flow. Rank by a composite score (fundamentals 60%, growth 20%, balance sheet 10%, margin-of-safety 10%). Output top 10 tickers with market cap, price, composite score, key ratios, one-paragraph risk note, and CSV table. Keep the output concise and factual.
Variants: swap PEG threshold for growth focus; or remove dividend filter for growth stocks; or apply to ETFs by screening underlying holdings and expense ratios.
1-week action plan:
- Day 1: Pick data source and access AI model.
- Day 2: Define rubric and gather 5 years of data into a sheet.
- Day 3: Run AI screening and get top 30 candidates.
- Day 4: Manual review of top 10; write risk notes.
- Day 5: Paper-trade or size small positions; set alerts and tracking sheet.
- Day 6–7: Monitor, refine rules based on initial results.
This is not financial advice — use it to build a repeatable process and validate with data. Your move.