[Ian Investor]

Hi all — I’m in my 40s, not very technical, and I’m getting curious about using AI to improve my long‑term investing research. Can AI tools actually help spot potentially undervalued stocks or ETFs, and if so, how might a non‑expert use them safely?

I’m especially interested in:

• Which simple AI tools or features are actually useful (screeners, sentiment summaries, trend detection, backtests)?
• Practical, non‑technical workflows I could try — step‑by‑step at a high level.
• Common pitfalls: data quality, bias, overfitting, or blind trust in AI outputs.
• Realistic expectations for what AI can and can’t do for long‑term investors.

If you have personal experiences, easy tools or prompts that worked for you, or short checklists for vetting AI suggestions, I’d love to hear them. No need for technical detail — just practical tips and warnings. Thank you!

[aaron]

Short 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.

1. 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
2. How to do it — step by step
   1. Collect last 5 years of financials and market prices for your universe (US stocks / ETFs).
   2. 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.
   3. Use AI to score each name on a weighted rubric (fundamentals 60%, growth 20%, balance 10%, margin-of-safety 10%).
   4. Sort and produce top 10–30 candidates; ask AI for concise risk notes per name.
   5. Manual review: validate business model, management, and competitive moat; adjust scores.
   6. 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:

1. Day 1: Pick data source and access AI model.
2. Day 2: Define rubric and gather 5 years of data into a sheet.
3. Day 3: Run AI screening and get top 30 candidates.
4. Day 4: Manual review of top 10; write risk notes.
5. Day 5: Paper-trade or size small positions; set alerts and tracking sheet.
6. 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.

[Fiona Freelance Financier]

Nice point: your checklist-style workflow and explicit scoring are exactly what turns AI from a toy into a repeatable tool. I’ll add a stress‑reducing routine and practical guardrails so the process stays simple and usable long term.

What you’ll need (keep this minimal to avoid overload):

1. Data source (one reliable feed — free or paid) and a single spreadsheet or simple database.
2. An AI model or rules engine you can run weekly (doesn’t need to be the most advanced).
3. A clear, short rubric (3–6 metrics) and one composite score formula you understand.
4. A tracking sheet for positions, alerts, and a calendar for reviews.

How to do it — step by step (stress‑reducing, repeatable):

1. Pick a manageable universe (e.g., 200–500 US stocks or a set of ETFs). Smaller is calmer.
2. Define a simple rubric: valuation vs sector median, profitability (ROE or FCF margin), growth trend (3–5y CAGR), and balance‑sheet check. Limit to 4 scores so you can explain results quickly.
3. Run the AI weekly or bi‑weekly to rank names. Have it output a one‑line rationale and one key risk per name — no long essays.
4. Review top 10 yourself each week for 10–20 minutes: business model, recent news, and whether the AI missed anything obvious.
5. Position sizing: start with small test weights (1–3% each) and a hard cap per name (e.g., 5–8%) to limit stress from any single outcome.
6. Set a simple rule for follow‑up: if a pick falls X% below purchase on no news, re‑check fundamentals; if fundamentals fail, trim or sell.
7. Track performance monthly and reweight or prune positions quarterly; keep a 12–36 month window before judging effectiveness.

What to expect and simple KPIs:

• Within a week: a ranked shortlist and concise risk notes.
• Over 12–36 months: evaluate hit rate vs benchmark and average position return.
• Stress‑reduction KPIs: minutes spent weekly on review, number of active positions, and max single‑position weight.

Common pitfalls and quick fixes:

• Overcomplicating the rubric — fix: prune metrics until each one changes decisions.
• Checking too often — fix: pick a weekly routine and stick to it.
• Emotional tinkering after losses — fix: rely on pre‑defined stop/review rules.

Keep the system small, predictable, and time‑boxed. The aim is less frantic decision‑making and a steady, testable process you can trust.

[Jeff Bullas]

Nice improvements — now let’s make this reliably usable. Your guardrails are the right idea. The next step is a tight, repeatable process you can run weekly without stress.

What you’ll need

• One data source (pick one you trust) and a single spreadsheet.
• An AI model you can query weekly (ChatGPT/Claude or an internal tool).
• A short rubric (4 metrics) and one composite score formula you understand.
• A tracking sheet with positions, sizes, and a monthly review calendar.

Step-by-step (do this once, then repeat weekly)

1. Define your universe (200–500 US stocks or a handful of ETFs).
2. Set a simple rubric — example: Valuation (vs sector median) 40%, Profitability (ROE or FCF margin) 30%, Growth (3–5y CAGR) 20%, Balance sheet 10%.
3. Choose thresholds you can explain (e.g., P/E <= sector median, ROE >=10%, 3y revenue CAGR >=5%, Debt/Equity <=0.8).
4. Pull the last 5 years of data into your sheet and run the AI to score each name by the rubric.
5. Ask the AI to output top 20 with one-line rationale and one key risk for each.
6. Spend 10–20 minutes on the top 10: business model sanity-check, recent news, and red flags.
7. Paper-trade or start with small test weights (1–3% per position), cap any single name at 5–8%.

Example — what a result looks like

• Ticker: ABC — Market cap $8B — Composite 78 — P/E 12 (sector 18) — ROE 14% — 3y Rev CAGR 8% — Risk: cyclical demand exposure.

Common mistakes & fixes

• Overfitting rules to past winners — fix: keep rules simple and validate out-of-sample.
• Garbage data — fix: audit a random sample each run before trusting scores.
• Too-frequent tinkering — fix: set a weekly cadence and only change rubric quarterly.

Copy-paste AI prompt (use as-is)

Act as a disciplined investment analyst. Using the most recent 5 years of financials and market data for the specified universe, screen US-listed stocks (exclude OTC/penny). Apply these filters: P/E vs sector median, ROE >=10%, 3-year revenue CAGR >=5%, Debt/Equity <=0.8, positive free cash flow. Score each company with weights: Valuation 40%, Profitability 30%, Growth 20%, Balance sheet 10%. Return the top 20 tickers with market cap, price, composite score, key ratios, one-line rationale, and one key risk. Output a CSV-ready table and a 1-week action recommendation.

1-week action plan

1. Day 1: Choose data source, set up spreadsheet.
2. Day 2: Define rubric and thresholds.
3. Day 3: Run AI screening, get top 20.
4. Day 4: Manual check top 10.
5. Day 5: Paper-trade or size small positions; set alerts.
6. Days 6–7: Monitor and tweak only if data issues appear.

Closing reminder

Keep it small, repeatable and time-boxed. AI should narrow the field — you validate and decide. Build the habit, measure for 12–36 months, and let the data guide adjustments.

[Becky Budgeter]

Nice point — I like the weekly, time‑boxed routine and the short rubric. That’s exactly what keeps this from becoming a full‑time hobby and makes results comparable over months. I’ll add a practical, low‑friction layer you can run alongside that process to reduce false positives and make the AI’s output easier to act on.

What you’ll need

• One trusted data source and a single spreadsheet (same as you suggested).
• A simple rubric you understand (4 metrics max) and the AI tool you’ll query weekly.
• A small “sanity check” checklist and a tracking sheet for positions, entry price, reasons, and review dates.

How to do it — step by step (repeat weekly)

1. Define your universe (200–500 stocks or a set of ETFs). Keep it stable so results are comparable week to week.
2. Pick your 4 metrics and weights (example: Valuation 40%, Profitability 30%, Growth 20%, Balance 10%). Write the thresholds in plain language so you can explain them out loud.
3. Pull 3–5 years of key data into the sheet. Run the AI to rank the universe against your rubric; ask for a one‑line rationale and one key risk for each top name (don’t request long narratives).
4. Do a 2‑minute data sanity check on a random 2–3 names (prices, recent earnings surprise, data gaps). If anything looks off, correct the source before trusting the full list.
5. Spend 10–20 minutes on the top 10: validate business model, recent news, and management signals. Add or remove candidates based on this manual check.
6. Paper‑trade or size tiny test positions (1–3% each), cap any single name at 5–8%. Log the reason, entry, and a 6‑ to 12‑month review date in your tracking sheet.
7. Review monthly for data quality and quarterly for rubric changes — only change thresholds after at least 3 months of tracked results unless a data error forces an earlier fix.

What to expect

• In one week: a ranked shortlist and short risk notes you can act on.
• Over 12–36 months: evaluate hit rate vs your benchmark, average holding performance, and whether the rubric needs pruning.
• Common early results: a mix of true gems and false positives — the sanity check and small test weights protect your capital while you learn.

Variants / quick tips

• If you want growth focus: raise the Growth weight and loosen valuation thresholds slightly.
• For income/dividend focus: add dividend yield and payout sustainability checks to the rubric.
• For ETFs: screen underlying holdings, fees, and turnover instead of single-company ratios.

Quick question: which universe do you prefer — a broad US 500 list, a capped‑market‑cap slice, or a small set of familiar industries? That tells me which default thresholds to recommend.

[Jeff Bullas]

Agree — your weekly, time‑boxed routine and short rubric are the backbone. To cut false positives further, add two low‑friction layers: sector‑relative scoring (so “cheap” means cheap vs peers) and a quick red‑flag triage before you spend time on any name.

What you’ll need (kept simple)

• One data source and a single spreadsheet.
• An AI model you can prompt weekly.
• A 4‑metric rubric you can explain out loud.
• A red‑flag checklist (cash flow, debt, dilution, and surprises).

Do / Do‑not (speed without drama)

• Do score metrics relative to the sector (percentiles or z‑scores).
• Do blend trailing and forward data (e.g., 70% trailing, 30% forward) to avoid rear‑view decisions.
• Do run a 60‑second red‑flag pass before any deep dive.
• Do cap position size and pre‑write sell/review rules.
• Don’t chase single metrics (low P/E alone is a trap).
• Don’t change your rubric weekly; review it quarterly.
• Don’t trust output if your spot check finds data gaps or stale prices.

Step‑by‑step (enhanced but still light)

1. Set the rubric (example weights): Valuation 40%, Profitability 30%, Growth 20%, Balance sheet 10%.
2. Go sector‑relative: convert each metric to a sector percentile or z‑score. This avoids penalizing banks for high leverage or software for high P/E.
3. Run the AI screen on your universe for a top‑20 list with one‑line rationale + one key risk per name.
4. Red‑flag triage (under 2 minutes per name):
   • Cash flow vs earnings: is free cash flow positive and broadly tracking net income?
   • Debt: Debt/Equity not spiking; no near‑term maturity wall that cash can’t cover.
   • Dilution: share count stable or falling over 3 years.
   • Surprises: recent guidance cuts or large one‑offs?
5. Keep 10, park 10: move red‑flagged names to a watchlist; keep 8–12 clean candidates for manual review.
6. Manual check (10–20 minutes total): business model sanity, moat hints, and a quick news scan.
7. Position and track: test weights of 1–3% each; cap at 5–8%; log entry, thesis, and a 6–12 month review date.

Worked example (template, not recommendations)

• ABC Corp — Composite 78/100. Valuation: P/E 13 vs sector 18 (top 25% cheap). Profitability: ROE 15% (top 30%). Growth: 3‑yr rev CAGR 7%. Balance: Debt/Equity 0.4. Risk: cyclical demand; watch inventory builds. Red‑flags: none (FCF positive; shares flat).
• XYZ Inc — Composite 74/100. Cheap vs sector, but red‑flag: FCF negative while EPS positive and share count rising. Park on watchlist.

Insider trick: sector‑relative “fair value band”

• Create a simple “fair value band” per sector using the last 5 years of P/E and EV/FCF percentiles.
• Prefer names priced in the cheapest 30% of their sector and with profitability in the top 50% — a practical “quality at a discount” filter.

Copy‑paste AI prompt (stocks)

Act as a disciplined long‑term analyst. Using the last 5 years of financials and current sector classifications for my stock universe, do the following: 1) Compute sector‑relative percentiles for Valuation (P/E and/or EV/FCF), Profitability (ROE or FCF margin), Growth (3–5 year revenue and EPS CAGR), and Balance Sheet (Debt/Equity). Use weights: Valuation 40%, Profitability 30%, Growth 20%, Balance 10%. 2) Blend metrics 70% trailing, 30% forward where available. 3) Return the top 20 tickers with: price, market cap, composite score, key ratios, one‑line rationale, and one key risk. 4) Run a red‑flag check and mark any name that has two or more of: negative free cash flow, rising share count over 3 years, Debt/Equity > 1.0, or a major guidance cut in the last 90 days. 5) Output a concise table (CSV‑ready) and a 3‑bullet action summary.

Copy‑paste AI prompt (ETFs)

Screen US‑listed ETFs for long‑term value. For each ETF in my list, report: expense ratio, 3/5/10‑year tracking difference vs its index (if applicable), top‑10 holdings weight, underlying portfolio valuation (weighted P/E or EV/EBITDA vs category median), dividend policy (yield and 5‑year growth), and turnover. Flag as “potentially undervalued” if the ETF’s weighted valuation is in the cheapest 30% of its category, fees are ≤0.20% (or category median if specialized), and top‑10 holdings < 45% (for diversification). Return a CSV table and 1–2 lines of risk notes per ETF.

Common mistakes & fixes

• Value traps: cheap because earnings are peaking. Fix: insist on positive free cash flow and stable margins.
• Data drift: mixing sectors or stale classifications. Fix: lock your sector map; update it quarterly.
• Over‑trading: reacting to every wiggle. Fix: weekly check, monthly performance log, quarterly rubric review.
• Ignoring costs/taxes: paper returns vanish. Fix: include fees, spreads, and tax assumptions in your tracking sheet.

1‑week action plan

1. Pick your universe (US 500, capped mid/large, or 2–3 familiar industries).
2. Write your 4 metrics and weights; define plain‑English thresholds.
3. Pull 3–5 years of data; run the stock prompt to get a top‑20 and red‑flag marks.
4. Do the 60‑second triage; keep 8–12 clean names; park the rest.
5. Manual check the shortlist; start 1–3% test positions; log entries and review dates.
6. Run the ETF prompt on your short ETF list; keep 2–4 as core holds if they pass.
7. Schedule a 30‑minute monthly review; no rule changes for 90 days unless data is wrong.

What to expect

• Week 1: a clean shortlist with fewer false positives.
• Months 1–3: a mix of wins and duds, but lower stress and clearer reasons for each pick.
• Months 12–36: judge the system on hit rate and risk‑adjusted returns vs your benchmark.

Closing reminder This is a process, not a prediction machine. Keep it small, repeatable, and sector‑aware. One question to tailor thresholds: do you want a broad US 500 universe, a capped mid/large slice, or a few familiar industries to start?

Educational only — not financial advice.

[Author]

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