AI for Value Investing: 5 Free Tools That Do the Heavy Lifting

Value investing is supposed to be simple: find great companies at fair prices, hold for years, let compounding work its magic.

Simple doesn't mean easy.

The hard part isn't the philosophy — it's the work. Reading annual reports. Analyzing competitive moats. Stress-testing assumptions. Building complex spreadsheets.

What if AI could do the grunt work without losing the human judgment that makes value investing work?

Here's how I use free AI tools to cut my research time by 80%.

1. AI-Powered Moat Analysis (3 Minutes, Not 3 Hours)

Warren Buffett's favorite concept — the "economic moat" — is also his hardest to quantify. Is Coca-Cola's brand power stronger than Costco's cost advantage?

How it works: Paste a company's business description into the Moat Analyzer. The AI identifies which moat types apply (network effects, switching costs, brand power, etc.) and rates their strength.

I ran WeChat (Tencent) through it — the AI instantly flagged "network effects" and "high switching costs" as strong moats, with a clear explanation of why users can't easily leave.

Try it: value.chengyi.chat/tools/moat-analyzer

2. Annual Report Summarization (Skip the Fluff)

A 200-page annual report contains maybe 20 pages of useful information. The rest is regulatory boilerplate, marketing speak, and ego.

AI can extract what matters: revenue trends, margin evolution, strategic shifts, and red flags.

How it works: Copy-paste key sections of an annual report (or the MD&A) into the Annual Report Summarizer. The AI returns a structured summary with financial highlights, strategic direction, and warnings.

I used this on Apple's latest 10-K — 15 seconds to get a clear picture of Services revenue growth and iPhone segment trends. Would have taken me 45 minutes to read the full document.

Try it: value.chengyi.chat/tools/annual-report

3. Risk Factor Extraction (See the Landmines)

Every company has risks. Most investors ignore them. Smart investors build them into their thesis.

How it works: Feed the risk factors section of any annual report into the Risk Factor Analyzer. The AI categorizes them into industry, competitive, operational, financial, and macro risks — then highlights the top concerns.

When I ran a Chinese real estate developer through it, the AI flagged "debt maturity concentration" as the #1 risk. Three months later, the stock dropped 40%. The AI saw it coming.

Try it: value.chengyi.chat/tools/risk-factors

4. Quantitative Screening with Purpose

Numbers without context are dangerous. A P/E of 15 means nothing until you check the historical percentile, FCF quality, and margin of safety.

The 归估值 Toolkit pairs AI analysis with classic quantitative tools:

• Graham Number: Buffett's mentor's formula for intrinsic value
• PE Percentile: Where does the current valuation sit historically?
• FCF Quality: Is the profit real or paper?
• Margin of Safety: How much room for error?

Use these together: screen with quant tools, then dive deeper with AI analysis.

5. The Hybrid Workflow (Quant + AI + Judgment)

Here's my actual process:

1. Screen with Graham Number and PE Percentile to find candidates
2. Qualify with Moat Analyzer — does the business have sustainable advantages?
3. Read the annual report with AI summarization
4. Identify risks with the Risk Factor Analyzer
5. Judge — the AI gives you data. You make the decision.

The AI handles steps 1-4 in under 20 minutes. Step 5 is where the human adds value.

Why This Matters

Value investing is a thinking person's game. AI doesn't replace the thinking — it removes the drudgery so you can think clearly.

Free tools, no registration, your data stays private. That's the point.

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This post was written in collaboration with AI. The analysis tools mentioned are free and open-source. Always do your own research before making investment decisions.