AI Versus Manual Research: Why The Buffett Screen Is a Game Changer

How AI-Powered Buffett Screening Beats Manual Research for Faster, Smarter Value Stock Picks

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The Buffett Screen and AI-powered value investing are rewriting the rules of stock research — and if you’re still doing it all manually in 2026, you’re basically showing up to a Formula 1 race in a shopping trolley.

This article breaks down why the old way of finding value stocks is costing you time, money, and probably a bit of your sanity, and how an AI tool at MarketInvestigation.com is changing everything for retail investors who want to invest like Warren Buffett — without the sixty-year head start.

Let’s Start With a Story That Involves Me Looking Very Foolish

Picture this. It’s a Saturday morning. I’ve got my third coffee, a spreadsheet the size of a small country, and the unshakeable conviction that I am about to find the next Berkshire Hathaway. I’ve been manually pulling P/E ratios, debt-to-equity figures, free cash flow numbers, and return-on-equity data for eleven different companies. I’ve been at this for four hours. My wife has walked past my desk three times now with a look on her face that says, “I married this?”

And what have I got to show for it? Eleven tabs, seventeen browser windows, two contradictory data sources, a cold coffee, and absolutely no idea which stock is actually worth buying.

That was me. That was a lot of us. Until I found the Buffett Screen.

Now, let me be clear — I am not promising you’ll become a billionaire. If I had that kind of power, I wouldn’t be writing articles on the internet. I’d be on a yacht somewhere arguing with Jeff Bezos about legroom. But what I can tell you is this: there is a smarter, faster, and dramatically less painful way to screen stocks using Warren Buffett’s exact investment framework — powered by AI, powered by real-time data, and available for free right now.

Let’s get into it.

What Is Manual Research, and Why Is It Slowly Killing Retail Investors?

Manual research is the traditional method of stock analysis. You go out and gather the data yourself — SEC filings, earnings reports, financial databases, news archives — and then you synthesise it all into an investment thesis. For professional analysts with Bloomberg terminals, proprietary data feeds, and teams of researchers, this is manageable. For you sitting in your kitchen at 11pm with a free Macrotrends account and a dream? It’s a nightmare.

The problems with manual research are well-documented. A landmark 2024 study published in the Journal of Financial Economics by Cao, Jiang, Wang, and Yang — “From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses” — found that AI models consistently outperformed human analysts in predicting earnings across a wide range of firm-level, industry-level, and macroeconomic variables. Let that sink in. Not matched. Outperformed.

But here’s the real kicker from that same study: when humans and AI worked together, combining human contextual judgment with machine-speed data processing, accuracy improved even further. The moral of the story? AI isn’t here to replace your brain. It’s here to replace your four-hour Saturday spreadsheet sessions so you can go spend time with the people who still tolerate you.

Manual research has three critical failure modes:

1. Speed: Markets move fast. By the time you’ve manually assembled a ten-year ROE history for a company, checked its debt-to-equity trajectory, cross-referenced its P/E against sector peers, and verified its free cash flow trends, the opportunity may have moved. Professional quant funds are executing thousands of screens per second. You’re still looking for where you saved the spreadsheet.

2. Bias: Human analysts are notoriously susceptible to narrative bias — falling in love with a company’s story rather than its numbers. A 2023 paper on Large Language Models and Stock Investing noted that while human judgment adds value in complex qualitative scenarios, it frequently introduces emotional and cognitive distortions that systematic AI approaches avoid entirely. In other words, sometimes your gut feeling is lying to you. AI doesn’t have a gut. That’s actually a feature, not a bug.

3. Consistency: The biggest problem with manual research isn’t that it’s slow or biased — it’s that it’s inconsistent. You apply slightly different standards on a Monday than on a Thursday. You’re more optimistic after a good night’s sleep than after watching the news. The framework shifts subtly each time you apply it. AI applies the same rigorous criteria to every single stock, every single time, without exceptions. It doesn’t care if a company has a cool brand. It doesn’t get excited about a charismatic CEO. It just checks the numbers.

The Oracle of Omaha’s Playbook: What the Buffett Screen Actually Measures

If you’re going to build an AI-powered stock screener, why not base it on the most successful investment record in history? Warren Buffett — the “Oracle of Omaha” — has compounded returns at roughly 20% per year for over five decades through Berkshire Hathaway. The academic world has studied his methods exhaustively, and the Buffett Screen at MarketInvestigation.com operationalises those methods into a repeatable, scoreable framework.

Here are the ten core criteria that the Buffett Screen evaluates every stock against:

1. Return on Equity (ROE): Buffett looks for companies with sustained ROE above 15% — typically averaging above 20% over a ten-year period, with no individual year dropping below 15%. ROE measures how efficiently a company uses shareholder capital to generate profit. A high, consistent ROE is the fingerprint of a durable competitive business. As Validea’s analysis of Buffett’s strategy — based on Mary Buffett’s Buffettology — confirms, a ten-year average ROE of at least 15% is the minimum threshold, with stronger companies clearing 20%.

2. Net Profit Margins: Buffett favours companies with wide, consistently expanding net margins. Thin margins mean the business has little pricing power and is vulnerable to cost shocks. Wide margins — think Apple or Coca-Cola — indicate a moat that competitors can’t easily erode. The Cabot Wealth Network’s analysis confirms that high operating margins are one of Buffett’s seven core value guidelines, alongside solid ROE and low debt.

3. Debt-to-Equity Ratio: Buffett is allergic to excessive debt. He wants companies that can finance their operations through earnings, not borrowed money. The TradingCenter.org breakdown of Buffett’s indicators notes that Buffett targets a debt-to-equity ratio below 0.5. For non-financial firms, long-term debt should ideally be no more than five times annual earnings.

4. Price-to-Earnings Ratio: Buffett doesn’t just look for great businesses — he looks for great businesses at fair prices. The P/E ratio, in context with growth rates and sector comparisons, helps identify whether a stock offers a margin of safety. He’s not chasing the lowest P/E on the market, but he’s certainly not overpaying for growth.

5. Free Cash Flow: This is arguably the most important number in the entire framework. Buffett calls free cash flow the “owner’s earnings” — the actual cash the business generates after maintaining its capital base. Positive and growing free cash flow is non-negotiable. As Validea’s Buffett strategy documentation notes, FCF provides “the flexibility to reinvest in growth, pay dividends, or reduce debt” — which is exactly what Berkshire Hathaway has done for decades.

6. Three-Year Earnings Growth: Buffett requires predictable, growing earnings over time. He’s not interested in one-hit wonders or cyclical spikes. He wants companies that reliably earn more this year than last year, and more next year than this year. The Sacred Heart University study on Buffett’s investment behaviour demonstrates how Apple, one of Buffett’s most famous holdings, scores exceptionally on earnings per book value metrics compared to peers.

7. Durable Competitive Moat: This is the qualitative element — what Buffett calls the “economic moat.” Is this a business that competitors can’t easily replicate? Does it have brand loyalty, network effects, switching costs, or cost advantages that protect its margins? The Buffett Screen evaluates and classifies the moat type: brand, cost, network, switching cost, or regulatory. Without a moat, even great numbers are temporary.

8. Management Quality: Good businesses can be destroyed by bad management. Buffett famously looks for managers who treat shareholder capital with the care of an owner, who communicate honestly, and who resist the “institutional imperative” — the corporate tendency to imitate peers regardless of whether it makes sense. This qualitative assessment is encoded into the AI’s analysis.

9. Pricing Power: Can the company raise prices without losing customers? This is the ultimate test of a moat. Coca-Cola can charge more for a Coke in 2026 than it did in 2006, and consumers still buy it. That’s pricing power. A commodity business with no pricing power will always struggle with margins.

10. Margin of Safety: Buffett’s ultimate filter, borrowed from his mentor Benjamin Graham. Even the best business is a bad investment at the wrong price. The margin of safety measures the gap between intrinsic value and market price. The larger the gap, the safer the investment. This is the final gate that separates a BUY verdict from a WATCH verdict.

The Buffett Screen evaluates every publicly traded stock against all ten criteria, scores it out of 100 (the “Buffett Conviction Score”), delivers a plain-English BUY, WATCH, or NO BUY verdict, and writes an analysis — in Buffett’s own voice — explaining the reasoning. Powered by Claude AI and real-time financial data from Finnhub, it does all of this in seconds.

Seconds. Let that marinate.

The Science Behind Why This Approach Works

This isn’t just a cool tool — it’s built on fifty years of rigorous financial research. Let’s get into the evidence.

The foundational case for value investing was established by Eugene Fama and Kenneth French, whose 1997 SSRN paper “Value Versus Growth: The International Evidence” demonstrated that value stocks — those with high book-to-market ratios — outperformed growth stocks by an average of 7.60% per year across global markets from 1975 to 1995, with value stocks delivering superior returns in 12 out of 13 major markets. That’s not a statistical blip. That’s a global phenomenon.

The Fama–French Five-Factor Model, revisited in research published by the CFA Institute in 2022, confirms that while the pure value factor faced challenges in the post-2007 growth-dominated era, it retains significant explanatory power — particularly when combined with profitability and investment factors. Indeed, a 2022 MDPI study on factor-based investing through market cycles found that the value factor outperforms specifically in high-interest-rate, high-sentiment market cycles — which describes the environment many global markets have been navigating in the early 2020s.

Meanwhile, the 2024 Journal of Financial Economics study by Cao et al. on AI versus human analysts showed definitively that machine learning models process financial data with greater consistency and predictive accuracy than solo human analysts — and that combining AI’s data-processing power with human strategic judgment creates the highest-performing hybrid approach. This is precisely what the Buffett Screen does: it applies machine-speed AI analysis to a human-designed investment framework, built by one of history’s greatest investors.

A 2025 arxiv preprint on Generative AI for Stock Selection found that AI-driven feature discovery in quantitative pipelines delivered Sharpe ratios of 1.14 to 1.63 in ensemble configurations — significantly above what passive market exposure provides — when retrieval quality was controlled. The paper also found something fascinating: when the AI was given incorrect financial documentation, signals turned negative. Quality inputs matter. The Buffett Screen sources real-time data from Finnhub and applies structured, verified criteria — which is exactly what controls for this risk.

Case Study 1: Apple Inc. (AAPL) Through the Buffett Screen

Apple is one of the most studied Buffett investments of the modern era. Let’s walk through how the Buffett Screen would evaluate it using the framework.

Apple’s ten-year ROE has been consistently extraordinary — often exceeding 100% in recent years due to share buybacks reducing the equity base, but averaging above 80% even under conservative calculation methods. Its net profit margin runs consistently above 25%, one of the highest of any large-cap technology company. Its free cash flow generation is staggering — over $100 billion annually in recent years.

The Sacred Heart University case study of Buffett’s holdings found that Apple’s earnings per book value ran at 36.1% — dwarfing Amazon at 11% and Walmart at 12.7%. Its return on invested capital came in at 19.7%, compared to Amazon’s 4.9%. The paper concluded that Apple had the best valuation and margin combination of any of the stocks evaluated.

Apple scores highly on every element of the Buffett moat framework: brand power (arguably the strongest consumer brand on earth), switching costs (once you’re in the Apple ecosystem, leaving is genuinely painful), and pricing power (Apple has raised iPhone prices consistently for fifteen years and customers have kept buying). The Buffett Screen would classify this as a brand + switching cost moat and deliver a high Conviction Score — though at certain elevated valuations, it might rightfully flag a reduced margin of safety.

This is the nuance a good AI-powered tool provides: not just “is this a good company?” but “is this a good company at this price, right now?” Those are very different questions.

Case Study 2: The BNSF Railway Acquisition

In 2009, Berkshire Hathaway acquired BNSF Railway — a move that baffled commentators who thought Buffett had lost the plot buying a boring old train company. The stock market, in its infinite wisdom, had decided that railroads were old news.

Buffett disagreed. BNSF had a durable physical moat — you cannot build a competing transcontinental rail network. It had consistent operating history, rational capital allocation (management reinvested into track upgrades rather than diversifying into unrelated businesses), manageable debt for a capital-intensive industry, and solid ROE and profit margins relative to sector peers. As PicturePerfectPortfolios’ analysis of Buffett’s criteria notes, BNSF checked every box in the Buffett framework, and the acquisition became one of the most successful in Berkshire’s history.

Had the Buffett Screen existed in 2009, it would have flagged BNSF with a high Conviction Score: consistent earnings history ✅, durable competitive moat ✅, rational management ✅, reasonable debt ✅, margin of safety at acquisition price ✅. The AI doesn’t get distracted by the narrative that railroads are “boring.” It reads the numbers. The numbers said BUY.

This is the entire point. The best investment opportunities are often the ones that look boring to the herd. AI doesn’t get distracted by boring. It gets excited by metrics.

Case Study 3: The Retail Trader Who Stopped Trusting His Gut

Let me tell you about a friend of mine — let’s call him “Definitely Not Me In 2021” — who spent eight months manually researching a software company. Beautiful story. Revolutionary product. CEO was on every podcast talking about changing the world. My friend — sorry, this person — did the research. Read every earnings call transcript. Tracked the revenue growth. Felt confident. Bought in heavy.

What he didn’t do was properly interrogate the debt-to-equity ratio, which had been climbing quietly for three years. He didn’t look closely enough at the free cash flow, which was actually negative despite impressive revenue growth. He was seduced by the narrative. He didn’t apply a systematic framework.

Guess what happened next. Right. Exactly what always happens when you skip the framework.

A tool like the Buffett Screen would have flagged the negative free cash flow immediately. It would have noted the rising debt. It would have assessed the lack of a durable competitive moat — because the software space has extremely low switching costs and dozens of competitors. The verdict would have been NO BUY, or at best WATCH with significant caveats.

The AI doesn’t fall in love with stories. It reads balance sheets. That’s worth more than you think.

How the Buffett Screen Works: A Step-by-Step Breakdown

Using the MarketInvestigation.com Buffett Screen is straightforward by design. Here’s the process:

Step 1 — Enter the ticker symbol. You type in the stock you want to analyse. Any publicly traded company works. AAPL, TSLA, BRK.B, VOD, SHEL — whatever you’re curious about.

Step 2 — The AI pulls real-time data. The tool connects to Finnhub’s financial data API to retrieve the current financial metrics: ROE, net margins, debt-to-equity, P/E, free cash flow, and three-year earnings growth. This is live data, not cached approximations.

Step 3 — The AI scores the stock. Claude AI evaluates the company against all ten Buffett criteria, scoring each one and generating a composite Buffett Conviction Score out of 100.

Step 4 — You receive a verdict. BUY, WATCH, or NO BUY — clearly stated, with no ambiguity.

Step 5 — You get the full analysis. The tool produces a plain-English investment analysis written in Buffett’s own voice — identifying the moat type, explaining the reasoning behind each criterion score, flagging key risks, and articulating why the stock does or does not meet Buffett’s standards.

The entire process takes seconds. Not four hours. Seconds.

Now, this isn’t a signal to go blindly buy whatever the tool says to buy. Buffett himself would tell you that no tool replaces your own understanding of a business and your own judgment about the future. But as a screening tool — a rapid, consistent, rigorous first pass through the universe of potential investments — it is genuinely transformative.

AI vs. Manual Research: The Honest Comparison

Let’s put the two approaches side by side and be completely straight about the comparison.

Time Required: Manual research for a single stock, done properly to Buffett’s standard, takes between two and eight hours. A ten-stock screen takes a weekend. The Buffett Screen takes seconds per stock. You could screen the entire S&P 500 in an afternoon. That is not an exaggeration.

Consistency: Manual research produces different results depending on your mood, energy level, and how much coffee you’ve had. An AI applies identical criteria to every stock, every time, with zero variance. The 2023 arxiv paper on LLMs and stock investing cited by Lopez-Lira and Tang found that consistent, structured AI-based scoring outperformed traditional providers in return predictability tests — precisely because of this consistency advantage.

Bias Resistance: You are human. You have biases. You’ll overweight stocks in industries you work in, companies whose products you love, and tickers that have been in the news recently. The AI has none of these biases. It evaluates Nestlé with the same dispassion it evaluates a company you’ve never heard of.

Data Coverage: Manual research is limited by the databases you have access to, your ability to synthesise multiple sources quickly, and your knowledge of what to look for. The Buffett Screen pulls structured financial data at API speed and cross-references it against ten established criteria simultaneously.

Depth of Qualitative Analysis: This is where humans still add value — and the Buffett Screen acknowledges it. The AI provides a qualitative moat assessment and management quality evaluation, but your own industry knowledge, your reading of the news flow, your understanding of regulatory risks — these add a layer of context that the tool doesn’t fully replace. The ideal workflow is AI for the quantitative screen, human judgment for the qualitative overlay.

Cost: Manual research costs you your time, which is the most expensive thing you have. The Buffett Screen is free. Not discounted. Not freemium with the good stuff behind a paywall. Free.

The Behavioural Finance Angle: Why You Need a System

One of the most robust findings in financial academia is that individual investors consistently underperform the market — not because they lack intelligence, but because of predictable behavioural biases. Loss aversion, overconfidence, herding, recency bias, and narrative fallacy collectively drive retail investors to buy high, sell low, and chase performance in a thoroughly reliable pattern.

The solution, as behavioural finance researchers have consistently recommended, is a systematic approach — a defined, rules-based process that removes the emotional decision-making from the equation. That is precisely what the Buffett Screen provides. You don’t have to ask yourself whether you feel like this stock is a good value. The tool evaluates it against ten objective criteria and tells you.

The Fama-French Five-Factor research confirming that value stocks outperform growth stocks globally over the long run is ultimately a statement about behavioural finance: markets systematically misprice certain categories of stocks because investors collectively over-favour exciting growth stories and systematically discount boring, high-quality value businesses. A tool that systematically identifies these value opportunities — and does so with Buffett’s criteria rather than just raw book-to-market ratios — is a genuine edge.

What Makes the MarketInvestigation.com Buffett Screen Different

There are a lot of screening tools on the market. What makes this one worth your time?

First, it uses Buffett’s actual framework. Not a generic value screen. Not a quant factor model. Buffett’s ten specific criteria, including the qualitative moat assessment and management quality evaluation that most quantitative tools skip entirely. If you want to invest like Warren Buffett, this is the most faithful systematic implementation of his methodology available to retail investors.

Second, it uses real-time data. Powered by Finnhub’s financial API, the tool pulls live data rather than relying on stale cached figures. In a market that can re-rate a stock 20% in a week on earnings news, this matters.

Third, it’s powered by Claude AI. The analysis isn’t a simple formula — it’s genuine AI reasoning. Claude doesn’t just score the metrics mechanically; it synthesises them into a coherent investment thesis, identifies the key drivers and risks, classifies the moat type, and produces a narrative analysis that explains the verdict in plain English. This is qualitatively different from a screener that spits out green and red numbers.

Fourth, it’s free. The entire tool — the scoring, the verdict, the full AI analysis — is available at no cost through MarketInvestigation.com. For a retail investor who has been spending money on subscriptions to financial data platforms that give you raw numbers but no synthesis, this is a significant upgrade.

Fifth, it’s fast. I keep coming back to this because it genuinely cannot be overstated. The thing that kills retail investor research isn’t lack of motivation — it’s the sheer volume of work required to do it properly. When that barrier comes down, you actually use the tool. You screen stocks regularly. You build a watchlist with rigour. You develop a disciplined investment process. That habit is worth more than any single trade.

How to Build a Buffett-Style Portfolio Using the Screen

Let me give you a practical framework for using the Buffett Screen as part of a real investment process.

Step 1 — Generate a universe. Start with a broad list of potential candidates. This might be the S&P 500, a sector you understand well, or a list of dividend-paying stocks. You’re not trying to screen every company on earth — just a manageable universe you have some familiarity with.

Step 2 — Run the screen. Put each ticker through the Buffett Screen. Focus on stocks that score above 70 out of 100 and receive a BUY or WATCH verdict. These are your quality candidates.

Step 3 — Add qualitative context. For each high-scoring stock, apply your own knowledge. Do you understand the business model? Do you have a view on the industry’s trajectory? Are there regulatory risks the screen might not fully capture? This is where your human judgment adds genuine value on top of the AI’s quantitative rigour.

Step 4 — Check valuation. Even a company that scores 85/100 on the Buffett Screen is a bad investment if you’re massively overpaying. Review the current P/E relative to historical averages and growth expectations. Assess the margin of safety. Buffett’s rule is never to overpay for quality — the market regularly gives patient investors opportunities to buy great companies at fair prices.

Step 5 — Build the position slowly. Don’t bet the house on one screen result. Build positions gradually, watch how the story develops over subsequent quarters, and use the screen again when new financial data is released. The Buffett approach is inherently long-term — you’re not day-trading based on a conviction score. You’re identifying businesses worth owning for five, ten, twenty years.

Common Objections — And Why They Don’t Hold Up

Let’s address the pushback, because there’s always pushback. I’ve heard every version of these arguments in trading forums, investment clubs, and from that one guy at every meetup who brings a printed-out spreadsheet like it’s 2003.

“But AI can’t replace real human insight into a business.” You’re right. And the Buffett Screen isn’t trying to. It’s a screener — a systematic first filter that applies Buffett’s quantitative and qualitative criteria at scale. It tells you which stocks are worth your deeper human analysis. It doesn’t tell you to stop thinking. It tells you which things are worth thinking harder about. The distinction matters enormously. Think of it like a GPS. You still decide where you want to go. The GPS just stops you from driving in circles for four hours pretending you know where you are.

“The data might be wrong or outdated.” The Buffett Screen uses real-time data from Finnhub — one of the most widely used financial data providers in the industry. The generative AI research from arxiv explicitly found that retrieval quality is a “first-order control variable” — which is exactly why using a live, reputable data source rather than scraped or cached numbers is critical. Garbage in, garbage out. The tool is built around avoiding that problem.

“I tried a stock screener before and it wasn’t useful.” Most stock screeners give you raw numbers and leave you to figure out what they mean. The Buffett Screen gives you a scored verdict, a conviction percentage, a moat classification, and a full plain-English analysis written in Buffett’s voice. This is not a screener that shows you a table of P/E ratios. It’s a screener that tells you what those ratios mean for this specific company in this specific context. That is a qualitatively different product — and it’s free.

“Value investing is dead.” People have been writing the obituary of value investing since 2010. The growth-dominated decade of 2010–2020 had a lot of people convinced that buying expensive software companies with no earnings was the new paradigm. Then rates rose. Then multiples compressed. Then people remembered why Buffett’s criteria exist. The CFA Institute’s 2022 review of the Fama-French model notes that reports of value’s death are likely exaggerated — and that the value factor retains significant return-generating potential, particularly when augmented with profitability and investment factors. Which, incidentally, is exactly what the Buffett Screen does.

The Future of AI-Powered Value Investing

We are at the very beginning of a transformation in retail investment research. The tools that professional fund managers have used for decades — systematic screening, quantitative factor analysis, AI-assisted data processing — are becoming available to individual investors for the first time, often at no cost.

The 2025 generative AI stock selection research found that AI-assisted feature discovery in quantitative pipelines achieved Sharpe ratios that retail investors using traditional methods could never approach. The Cao et al. 2024 study demonstrated that AI is not a replacement for human judgment but a powerful augmentation of it. The academic literature is converging on a clear conclusion: the investors who will outperform in the next decade are not those who work hardest at manual research — they’re those who learn to combine systematic AI analysis with disciplined human judgment.

The Buffett Screen at MarketInvestigation.com represents exactly this convergence. It takes the most successful investment framework in history, applies it at machine speed to any stock you can name, and delivers a rigorous, plain-English verdict — free, in seconds, powered by Claude AI and live financial data.

If Warren Buffett were starting out as a retail investor today, with access to this kind of tool, he would absolutely use it. He’d probably still eat his McDonald’s breakfast and drink his Cherry Coke while doing it — but he’d be running screens in seconds instead of weeks. And he’d have a lot more time for bridge.

The Bottom Line

Manual research is not dead. Human judgment is not obsolete. But the era of spending entire weekends manually assembling financial data that an AI can process in thirty seconds? That era is over.

The Buffett Screen gives you something extraordinary: the investing wisdom of the greatest value investor in history, systematically applied to any stock on demand, with a real-time data feed and genuine AI reasoning behind every verdict. It checks ROE, net margins, debt levels, P/E ratios, free cash flow, and earnings growth against Buffett’s exact criteria. It assesses the moat, the management quality, and the margin of safety. And it tells you — in plain English — whether the stock is worth your money.

The academic evidence for this approach is overwhelming. Value investing, systematically applied, has outperformed growth globally for decades (Fama & French, 1997). AI-powered analysis outperforms solo human research on consistency and accuracy (Cao et al., 2024). Systematic, rules-based frameworks outperform emotional, narrative-driven decision-making in virtually every long-run study of investor behaviour.

The tool exists. It’s free. It’s at MarketInvestigation.com. The question is whether you’re going to keep doing it the hard way, staring at seventeen browser tabs every Saturday morning — or whether you’re going to let the AI do the heavy lifting so you can focus on what actually matters: understanding the business you’re investing in.

Put down the spreadsheet. Drink your coffee while it’s still hot. Run the screen.

Your portfolio — and your marriage — will thank you.

References

1. Cao, S., Jiang, W., Wang, J., & Yang, B. (2024). From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses. Journal of Financial Economics. https://www.sciencedirect.com/science/article/abs/pii/S0304405X24001338
2. Fama, E. F., & French, K. R. (1997). Value Versus Growth: The International Evidence. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2358
3. Korenak, B. et al. (2022). Factor-Based Investing in Market Cycles: Fama–French Five-Factor Model of Market Interest Rate and Market Sentiment. MDPI Journal of Risk and Financial Management. https://www.mdpi.com/1911-8074/15/10/460
4. CFA Institute (2022). Fama and French: The Five-Factor Model Revisited. https://blogs.cfainstitute.org/investor/2022/01/10/fama-and-french-the-five-factor-model-revisited/
5. Lopez-Lira, T., & Tang, Y. (2023), cited in: Anonymous (2025). Large Language Models and Stock Investing: Is the Human Factor Required? arXiv. https://arxiv.org/pdf/2603.19944
6. Anonymous (2025). Generative AI for Stock Selection. arXiv. https://arxiv.org/pdf/2602.00196
7. Validea (2024). Validea’s Top Warren Buffett Stocks — August 2024 (based on Buffettology by Mary Buffett). https://blog.validea.com/?p=32829
8. Sacred Heart University. The Case of Warren Buffett and His Investment Behavior. https://digitalcommons.sacredheart.edu/cgi/viewcontent.cgi?article=1639&context=wcob_fac
9. PicturePerfectPortfolios (2026). Warren Buffett’s Investment Criteria: An In-Depth Analysis. https://pictureperfectportfolios.com/warren-buffetts-investment-criteria-an-in-depth-analysis/
10. TradingCenter.org. Warren Buffett’s Stock Trading Method and Financial Indicators. https://www.tradingcenter.org/index.php/train/financial-ratios/380-warren-buffett-stock-trading-indicators
11. Cabot Wealth Network. Warren Buffett’s 7 Value Investing Guidelines. https://www.cabotwealth.com/daily/value-stocks/warren-buffett-value-investing-guidelines
12. Cheng, Y., et al. (2025). Comparison of CAPM and Multi-Factor Fama–French Models for US Market Sectors. MDPI International Journal of Financial Studies. https://www.mdpi.com/2227-7072/13/3/126

Disclaimer: This article is for educational and informational purposes only and does not constitute financial advice. Always conduct your own due diligence and consult a qualified financial adviser before making investment decisions.