The Fair Value Playbook

Why this handbook exists

I did not originally write this for the market. I wrote it for my children, Rosalie and Ferdinand, and for the younger version of myself who sensed that something important was happening in investing but could not find a simple, honest, practical explanation.

For a long time the stock market looked like a strange game. Some people treated stocks like gambling; others spoke as if professionals had a secret language ordinary people could never learn. I had no mentor, so I learned the slow way — books, mistakes, spreadsheets, painful lessons, and eventually a system.

One sentence organised the whole subject for me: price is what you pay, value is what you get ^[18]. On a screen you see prices every second. What you do not see directly is value. If you never estimate value, your decisions quietly become emotional — fear, greed, hope, panic, or the feeling that you're missing out.

That is why I built the Fairvalue Calculator. It is not magic and not a promise. It is a compass. It asks one clean question: is this stock probably cheap, fair, or expensive compared with a reasonable estimate of its intrinsic value? This handbook turns that question into a repeatable process — and shows you the science that says the process has an edge.

Why monkeys often beat fund managers

When you ask a bank to invest your money, you are often sold a feeling before you are sold a strategy. The key question is not how elegant the fund name sounds — it is what costs you pay, and who gets paid regardless of performance.

Active funds face two structural problems. First, incentives aren't always aligned: institutions may prefer their own products or whatever is easiest to sell. Second, size is a cage — a fund moving billions cannot turn as nimbly as an individual managing a personal portfolio.

The maths is unforgiving. William Sharpe's "arithmetic of active management" ^[19] shows that, before costs, the average actively-managed dollar must earn exactly the market return — so after fees, the average active dollar must underperform the market. Decades of SPIVA scorecards from S&P confirm it: across most regions and horizons, the majority of active funds trail their benchmark ^[S1]. Carhart showed that what little persistence exists is largely explained by costs and momentum, not stock-picking genius ^[20].

This does not make professionals useless. A low-cost index ETF is a perfectly sensible core. But if you choose individual stocks, you need something a brochure cannot give you: a process that stops you confusing price movement with business value. That is exactly where this method begins — and where a disciplined private investor actually holds an edge over a giant, fee-laden fund.

Price is not value

A stock price is only the last transaction between two participants. It is not an objective truth about the company. In real life you would never buy a house just because yesterday's quote jumped — yet in stocks, people do exactly that, because the market flashes prices in their faces all day.

Benjamin Graham gave us the antidote: imagine the market as a moody business partner, Mr. Market, who shows up every day quoting a price to buy your share or sell you his. Some days he is euphoric, some days terrified. You are free to ignore him — or to exploit him — but never obliged to trade ^[18]. Fair value is your anchor against his moods. It is not a prophecy; it is a rational estimate, or range, that sorts a stock into three zones: undervalued, fairly valued, overvalued.

This changes how you feel about volatility. A falling price can be a warning — or an offer. It depends entirely on value. But the anchor only works if it is honest: the more stable the earnings, the cleaner the growth, and the stronger the balance sheet, the more useful a fair-value estimate becomes. The shakier the business, the wider the range and the larger the safety margin must be.

The simple Fair Value calculator

The simple calculator has one job: teach the logic of fair value without intimidating you. You enter earnings per share and a growth rate, and the output forces a conversation between today's price and a reasonable estimate of worth.

EPS is profit per share — preferably diluted EPS, which accounts for options and other instruments. Growth should come from observed or carefully estimated revenue/earnings growth, not from hope. If revenue rises from 100 to 110, that's 10% — nothing mystical.

The biggest beginner mistake is treating a single abnormal year as normal. A one-off gain, a write-down, a cyclical spike or a crisis year can make a valuation look mathematically clean but economically wrong. That is why smoothing matters — median, mean, geometric and weighted averages reduce the influence of one-off distortions and move you toward a sustainable base level of profit and growth. (Our live engine uses a weighted, robust growth blend for exactly this reason.)

Finding ideas — tools and everyday life

Good ideas come from screeners, magazines, conversations, everyday products — even your children or partner noticing a brand everywhere. The art is not finding stories. The art is testing them.

The database and screener turn a spontaneous idea into a structured candidate: filter for stocks whose calculated fair value sits above the current price, then narrow by region, size, profitability, debt, growth and trend. This stops you confusing media attention with investment quality.

Everyday life still matters — the early iPhone was a product signal millions saw as consumers before they understood it as investors. A great product can reveal customer love, pricing power and room to grow. But you must take one more step: check whether the numbers support the story. A company can be beloved and overvalued; a boring one can be ignored and deeply cheap.

The tools, explained

The Premium Tools are a shortcut through work you could do by hand across dozens of websites and spreadsheets — but the real value is that the process becomes repeatable.

The stock card — a verdict at a glance

Every stock in the radar is a card: the company, its estimated fair value, the live price, the resulting upside, a Quality Score (how good the business is) and an Evidence rating (how much data backs the estimate).

From the whole market down to one decision

The dashboard is the control center; the screener is the discovery engine (filter thousands of companies by value gap, quality, debt, growth and strength); the stock page is the dossier (fair-value models, history, sector comparison, quality indicators); the watchlist is where discipline lives — you watch whether price and value converge before you act; and the Portfolio Manager adds the layer most private investors ignore: diversification across region, sector, size and cash.

The method in practice

The method is top-down, then bottom-up. First, classify the market — if broad valuations are high you become selective and keep cash for better opportunities; if they're depressed after a crisis, you can justify more risk. This is not magical market timing; it is risk budgeting.

Second, read the sectors — some are cheap for good reasons, others expensive for bad ones. Third, screen for undervalued names. A good candidate has fair value above price, acceptable quality, manageable debt, sufficient liquidity and no obvious data red flags; a better one adds stable revenue, consistent earnings and a trend that isn't collapsing. Fourth, inspect each company's dossier. Fifth, build and maintain the portfolio.

Timing, exits and money management

The perfect entry is usually an illusion. Waiting forever for the absolute bottom can hurt as much as buying everything at once at the top. Treat entry as a process: when the market is expensive, build positions gradually and hold reserves; when it cheapens, add risk in a controlled way. Cash becomes an option, not a permanent fear position.

Fair value is not tomorrow's target price — it is a multi-year orientation. Many value effects need three to five years to unfold, and our own evidence points to a similar horizon (Frankel & Lee found the value-to-price signal pays over ~3 years ^[6]). If you cannot hold through several difficult years, the method will feel far harder than it looks on paper.

Exits need rules too. There are three honest reasons to sell: you need the money, the investment case breaks, or the position no longer fits the portfolio. A falling price alone is not a reason. A falling price plus deteriorating fundamentals, a lower fair value and a broken trend is.

Psychology and the forgotten portfolio

The most dangerous opponent is not the market — it is your own mind. Behavioural finance exists because humans repeat the same mistakes: we sell winners too early because locking in a gain feels good, hold losers too long because admitting an error hurts, chase hype because others seem to get rich faster, and panic in crashes because headlines turn fear into a social experience.

Kahneman & Tversky's prospect theory explains much of it: we feel a loss roughly twice as intensely as an equivalent gain, which pushes us to take the wrong risks at the wrong times ^[21]. Fair-value thinking helps because it hands you a reference point. You don't just see that a price fell — you see whether it fell below value, whether value itself changed, or whether the thesis is simply broken.

Two personal lessons. Penny stocks with wonderful stories and terrible substance taught me that a low share price is psychologically seductive (you can buy "so many") but not safer. Leverage taught me that products which make the first win feel brilliant make the first big loss feel impossible — they raise the emotional temperature until a portfolio becomes a casino. The strongest lesson came from a nearly forgotten portfolio of solid companies, left alone for years to compound. Patience and process quietly beat clever activity.

Fair Value, deeply

Behind the single number on a card sits a family of models, not one formula. Earnings-power and P/E-based views, cash-flow and DCF logic, asset/book views, dividend logic, and EV/EBITDA multiples each look at the same business from a different angle. We blend them — family-balanced, weighted by the company's growth profile — into a consensus bear / base / bull range. A wide range is information: it tells you the business is hard to pin down and demands a bigger margin of safety.

Two disciplines keep the number honest. Scale & unit sanity rejects fundamentals that are off by a factor of a thousand or quoted in the wrong currency sub-unit, before they can poison a valuation. Robust growth uses weighted, outlier-resistant trends so a single freak year cannot dominate. And a plausibility gate withholds anything whose output is mathematically possible but economically absurd — a fair value ten times the price is treated as a data problem to verify, not a signal to celebrate.

The Quality factors and their science

"Cheap" alone is a trap. The graveyard of investing is full of stocks that were statistically cheap and deserved to be. That is why every company also earns a Quality Score, built from factor families that each rest on decades of peer-reviewed evidence. Crucially, we did not invent these signals — we selected the ones that replicate. Here is the full map, factor → finding → source.

On a stock page these become a transparent set of bars — you can see why a company scores well or badly, and the academic basis is named on every family.

How we proved it works

A strategy deserves trust only if it has a reason to work and that reason shows up in data. Our evidence rests on three pillars: the peer-reviewed literature above, our own long-term observations, and an implementation that never relies on a single formula. Here is what our internal studies found — stated honestly, including the spread.

Mean reversion takes time — a survival curve

The average is strong — but the spread is the truth

The edge concentrates early

Mean reversion is not one pattern

Sometimes the price rises to meet a steady fair value; sometimes the fair value falls to the price; sometimes fundamentals grow into a flat price; often both move toward each other. The convergence is real — the path varies.

Mapped onto the literature, the picture is consistent: low valuation ^[1][2][3], quality and profitability ^[5][7][8][9], and mean reversion / intrinsic-value convergence ^[4][6] have all historically mattered. Piotroski's fundamental signals added ≈7.5% p.a. to value-stock returns over 1976–1996 ^[5]. Our internal results sit in the same family of findings — strong on average, wide in dispersion.

Case studies — from undervalued to today

Numbers in the aggregate are convincing, but a single story makes the idea concrete. Here are real names the value lens looked at when they were out of favour — and where they trade now. Read them honestly: they are chosen with hindsight to show what the method looks for, they are not buy recommendations, and they are not typical. For every winner there were stocks that went nowhere or fell — the honest average and its wide spread are in the previous chapter.

Meta Platforms — the market priced in permanent decline

In an October 2022 article we worked through Meta (then Facebook) as a teaching example. The stock had collapsed to about $129 — down roughly 70% from its peak — as the market extrapolated falling profits and heavy spending into permanent decline. But the fundamentals told a different story: enormous cash flow, a dominant advertising franchise, and a balance sheet that could fund the experiment. Price had detached from value, which is exactly the gap the method hunts.

By mid-2026 Meta traded near $600 — a gain of roughly +365% from that low. The lesson is not that we predicted it; it is that when a sound business is priced for catastrophe, the gap between price and value is where patient money is made.

Two more from the same period

Goldman Sachs — featured around $297 in late 2022, near $1,091 by mid-2026 (about +268%). Apple — around $145 in late 2022, near $299 by mid-2026 (about +107%).

The same pattern shows up over much longer horizons. In our 2005 cohort, 32 stocks flagged undervalued returned on average +846% by 2025 versus +162% for the market — with Goldman Sachs the extreme outlier at roughly +14,514%. But 9 of those 32 lost money. The average is strong; the spread is the truth.

Why we leave everything else out

If value and quality work, why not add fifty more signals? Because most "signals" are noise wearing a lab coat. Academics now describe a "factor zoo" of hundreds of published predictors — and when Harvey, Liu & Zhu re-examined them with proper statistics, they argued that most should be viewed with deep suspicion: test enough variables and some will look significant by pure luck ^[22]. Hou, Xue & Zhang found that a large share of published anomalies simply fail to replicate once you correct for obvious flaws ^[25].

It gets worse after publication. McLean & Pontiff measured that predictability decays by roughly half once a factor is published and arbitraged ^[23]. And Bailey, Borwein, López de Prado & Zhu showed that with enough trials you can always produce a beautiful backtest that is pure overfitting — "pseudo-mathematics" ^[24]. A clever-looking strategy is often just a story fitted to the past.

So we apply a hard filter. We keep a factor only if it is (1) economically motivated — there's a reason it should work; (2) replicated across decades, countries and asset classes; and (3) survives out-of-sample, not just in the window where it was discovered. That leaves exactly the families in Chapter 11: value, quality/profitability, fundamental strength, earnings quality, intrinsic-value reversion, momentum, low-risk and conservative investment.

What we deliberately do not do — and why

What remains when the noise fades

Turn down the volume and the method becomes simple. Separate price from value. Buy only when value, quality and a margin of safety line up. Diversify. Hold long enough for the thesis to work. Review with rules. Sell when the case breaks — not when the headline is loud.

The process is not built to eliminate losses; it is built to stop isolated mistakes from becoming fatal. Fair value reduces the risk of paying for fantasy. Quality reduces the risk of buying a trap. Diversification reduces the risk of one wrong company destroying you. Time gives good businesses room to compound.

A rules-based approach also changes your emotional life. A crash becomes an environment to classify, not a reason to panic. A hype stock becomes a story to test, not a command to buy. A falling holding becomes a question — is fair value still intact? A rising winner becomes another — has the gap closed, or is the business still compounding?

Where this method comes from

Everything in this handbook has a history, and I think you should know it before you trust the method. It did not start in a bank or at a fund. It started in 2004 in Graz, Austria, when I was fourteen and could not get one question out of my head: how is it possible that a few investors build wealth with stocks decade after decade, while friends and relatives call the stock market a place where you lose house and home?

I answered the question the slow way. Over the next sixteen years I read more than a hundred books about the stock market, ordered one by one online. In the handbook I wrote in 2020 I did the sum: the books cost about 5,000 euros, and the same money put into the shares of the bookseller in 2004 would have multiplied about a hundred times. Education pays the best interest, people say. In this one case the shares would have paid more.

Two expensive lessons

At eighteen I made quick money with a single stock and decided I had a talent. I moved on to leveraged certificates, read candlesticks, trend lines and oscillators on several time frames, and refreshed the index on my phone even in the bathtub. Then the account was empty. That was lesson one: leverage and constant trading are a way to transfer money to the issuer.

While I licked my wounds I remembered a demo portfolio I had opened after a school stock market game: a handful of blue chips, bought once and forgotten. I reset the password and looked. It had roughly quadrupled between 2004 and 2008 without a single decision from me. The forgotten portfolio had beaten all my clever activity by a wide margin.

Lesson two came in the financial crisis. I rebuilt my portfolio with jobs and pocket money, watched it fall in 2008, and bet on falling prices almost exactly at the low. The market turned and the savings were gone a second time. Since then I do not use leverage and I do not try to time the market. Chapter 9 of this handbook grew out of those years.

The green lamp

After school and civilian service in a hospital I studied dentistry, like my father, and I run a dental practice today. The stock market never let go of me. In my student flat, a dark place with windows on one side only, I built a small office from a coffee table, a chair and a lamp with a green shade, the kind you would expect in a law firm. I called it the thinkers club lamp. Under it I read annual reports, hundreds of them.

Reading annual reports is nobody's hobby, and without a business degree most of the numbers stay closed. But if you want to know what a company is worth, there is no way around them. Every established valuation method I studied had its merit, and all of them had one thing in common: hard to learn and hard to apply for a normal person. So I looked for a way to get from a few reported numbers to a reasonable estimate of value, fast, and to test honestly whether it works.

I combined figures from the reports into formulas, first on paper. Calculating every stock by hand became tedious, so I had the formulas programmed and put the calculators on a website. That website is the reason this book exists.

Ten years in public: the dated record

Many products claim a long history. Ours can be checked. On 20 March 2016 fairvalue-calculator.com went online, and one day later I presented it in a large German investor forum and asked the members to test it. That thread still exists. It has grown to eight pages over ten years, and it contains what no company history written afterwards can offer: dated posts, concrete numbers, mistakes found by strangers, and my answers at the time.

The list below follows that record. After each line you see what kind of source carries it: a publication dated in public at the time, the handbook I published in 2020, or my own account where no outside record exists. The numbers in brackets point to the list of sources at the end of this part.

One detail from the first post matters to me. It already asked users to publish the fair values they had calculated, so that other investors could discover new ideas. The comments and votes you find on every stock today are that same thought, ten years later.

What the critics changed

The forum was not kind. Within hours members called the formula a black box, demanded statistical proof, found a problem with stock splits and pointed out that two inputs cannot value a cyclical company. They were right more often than I liked at the time.

I keep these arguments in this handbook on purpose. A history that only shows applause proves nothing. A history in which outsiders find errors and the product changes in response is hard to invent afterwards. Here are the main objections and what became of them.

The original and where it stands today

What the sources prove

• A public, usable online calculator for the intrinsic value of stocks existed under this name and at this address on 20 March 2016, with a launch post dated the next day.
• The public model portfolio by the method was created on 30 March 2016 and is still listed by its provider.
• Screener, watchlist, automatic valuation from a database, a combination of several models, quality checks, a market valuation indicator and an AI valuation of soft factors each appear in dated public posts between 2016 and 2025.
• The development never stopped: every year from 2016 to 2026 has dated entries.

What is my own account

• The years before the launch: the first formulas and tests during my studies in 2013 and my private investing before 2016. I describe them as I remember them. No outside record with a date exists for them.

What I do not claim

I did not invent fair value. Benjamin Graham described the intrinsic value of a stock in the 1930s, and discounted cash flow is older than I am. What is mine is the product: the specific combination of models, the long public development and the decision to make this kind of analysis usable for private investors.

In recent years large financial websites have added automatic fair value figures of their own. I take that as confirmation that the idea was right. I cannot say and do not say that anyone copied anything. What I can say is simple: when you read Fair Value Calculator, this is the one that has carried the name in public since March 2016, and you can check every step of it.

Where the platform stands today

The single calculator of 2016 took two numbers. The platform of today values about 35,000 stocks worldwide with 26 valuation models and scores each company on up to 37 quality factors. Valuation and quality are two separate questions: whether a stock is cheap, and whether the business is good.

• Fair value as a range: a cautious, a middle and an optimistic case instead of one number that pretends to be exact.
• An evidence status for every stock. Where the data is too thin, the fair value is withheld rather than shown wrong.
• An AI verdict per stock: one sentence on the quality of the business, one on what currently matters, and the main risk in one sentence.
• A watchlist that reads like a portfolio check: valuation, quality and diversification at a glance, with an optimizer that suggests stocks which fit what you already hold.
• Comments and votes on every stock, the community idea from the first post of 2016.
• A free academy of 16 short video lessons, and a connection that lets AI assistants read the valuations directly.
• A methodology page that explains the models, the data sources and the limits in the open.

Browsing and valuing single stocks is free. The method is the same one this handbook teaches: separate price from value, insist on quality, diversify, and give it time.

Sources for Part IV

All sources were read again on 11 October 2026. The forum thread is in German.

[S1] Wertpapier-Forum, thread "Fair Value Calculator", page 1: launch post of 21 March 2016 and first criticism. https://www.wertpapier-forum.de/topic/48870-fair-value-calculator/

[S2] Wertpapier-Forum, page 2: backtests March to June 2016, stock split debate. https://www.wertpapier-forum.de/topic/48870-fair-value-calculator/page/2/

[S3] Wertpapier-Forum, page 3: helper calculators, tutorial video, market valuation, database 2016 and 2017. https://www.wertpapier-forum.de/topic/48870-fair-value-calculator/page/3/

[S4] Wertpapier-Forum, page 4: relaunch of 8 May 2019, detailed calculator of 4 December 2019. https://www.wertpapier-forum.de/topic/48870-fair-value-calculator/page/4/

[S5] Wertpapier-Forum, page 5: criticism of data and cyclical stocks, replies January to April 2020. https://www.wertpapier-forum.de/topic/48870-fair-value-calculator/page/5/

[S6] Wertpapier-Forum, page 6: 2020 to July 2026 (DCF and PEG, several models, free access, AI valuation, rebuild). https://www.wertpapier-forum.de/topic/48870-fair-value-calculator/page/6/

[S7] Wertpapier-Forum, page 7: feedback and corrections in July and August 2026. https://www.wertpapier-forum.de/topic/48870-fair-value-calculator/page/7/

[S8] Wertpapier-Forum, page 8: discussion up to 27 September 2026. https://www.wertpapier-forum.de/topic/48870-fair-value-calculator/page/8/

[S9] Klein, Peter (2020): "Die Fair Value Calculator Methode. Wahrer Wert schlägt den Markt", handbook, 77 pages (German). https://s0c1cccdcaae15a1c.jimcontent.com/download/version/1609693918/module/9220685476/name/ebook%202020.pdf

[S10] wikifolio "Fair Value Calculator" by Dr. Peter Klein, created on 30 March 2016. https://www.wikifolio.com/de/de/w/wf11224488

[S11] fairvalue-calculator.com: methodology, data and trust, current state. https://www.fairvalue-calculator.com/methodology

Evidence map, glossary & references

Evidence map

Glossary — every term, in plain language

No finance degree needed. Every word you'll meet in the app and in this book, explained for beginners. Skim it once, then come back whenever a term trips you up.

The essentials

Fair value — a defensible estimate (shown as a range) of what a company is truly worth, built from its fundamentals — independent of today's mood-driven share price.

Intrinsic value — the same idea: the "real" worth a business should command based on what it earns and owns.

Value gap / Upside — fair value minus price, in percent. A big positive number means the stock looks cheap versus its worth.

Margin of safety — buying meaningfully below fair value, so you're protected if your estimate is a little off.

Mean reversion — over years, price and fundamental value tend to drift back toward each other. This is the engine that makes value investing work.

Quality Score — a 0–100 blend of profitability, growth, cash generation, balance-sheet strength and risk. It tells you how good the business is — separate from how cheap it is.

Evidence rating — how much clean, audited data backs an estimate (High = lots of solid data; Low = thin data, treat with care).

Bear / Base / Bull — a conservative, a central, and an optimistic fair-value estimate, so you see the realistic spread instead of one false-precise number.

Signal (Hold / Sell) — the app's simple rule derived from price versus fair value, your entry price, and the long-term trend.

MA250 — the average price over the last 250 trading days (≈ one year); a simple long-term trend line.

Market cap — share price × number of shares: the company's total value on the stock market.

Profitability & quality

Gross Profitability — gross profit ÷ total assets; how much raw profit the company squeezes from everything it owns — a hard-to-fake sign of a strong business.

Return on Assets (ROA) — net profit ÷ total assets; how efficiently the company turns its assets into profit.

Return on Equity (ROE) — net profit ÷ shareholders' equity; how much profit is earned on the owners' money.

Profit Margin — net profit ÷ revenue; how many cents of every sales dollar end up as profit.

Asset Turnover — revenue ÷ total assets; how much sales the company generates per dollar of assets.

Growth

Revenue growth (YoY) — sales versus the prior year; is the business actually expanding?

Δ (delta) — the "Δ" before a metric means change versus last year. ΔROA, ΔGross margin, ΔAsset turnover, ΔEBITDA margin rising = the fundamentals are improving.

Equity growth — how fast shareholders' equity (retained value) is compounding.

Asset growth (scored in reverse) — very fast asset growth often precedes weaker future returns, so a slower, disciplined grower can score better.

Cash & balance-sheet strength

OCF margin — operating cash flow ÷ revenue; the real cash (not just accounting profit) the core business produces.

FCF margin / FCF Yield — free cash flow (what's left after running and maintaining the business) ÷ revenue, or ÷ market value; the cash owners can actually use.

Accruals quality (OCF/NI) — operating cash flow versus reported profit. Profits backed by real cash (ratio near or above 1) are higher quality than paper profits.

Solvency (E/A) — equity ÷ assets; the share of the company funded by owners rather than debt. Higher = a stronger, safer balance sheet.

Debt-to-Equity — debt relative to equity; lower leverage means lower financial risk.

Altman Z-Score — a classic bankruptcy-risk score from several balance-sheet ratios; higher = financially safer.

Current ratio — short-term assets versus short-term bills: can the company cover the near term?

Cash / Assets — cash as a share of assets; a liquidity cushion for tough times or opportunities.

Piotroski F-Score — a 0–9 checklist of fundamental health (profitability, cash, leverage, efficiency). Higher is stronger.

Momentum & risk

Price momentum (3m / 6m / 12-1m) — the recent price trend; historically, stocks trending up have tended to keep outperforming short-term.

52-week high proximity — how close the price is to its one-year high; near the high often signals strength rather than being over-extended.

Realized volatility — how much the price swings; calmer stocks have historically delivered better risk-adjusted returns.

Beta — sensitivity to overall market moves; low-beta stocks have historically rewarded patient investors.

Net share issuance / buybacks — fewer shares (buybacks) lift per-share value; issuing many new shares dilutes existing owners.

Valuation multiples

P/E — price ÷ earnings; what you pay per dollar of annual profit. P/B — price ÷ book value (net assets). P/S — price ÷ sales. EV/EBITDA — company value including debt ÷ operating earnings, a debt-aware multiple. For all of these, lower usually means cheaper.

Dividend yield — the annual dividend as a percentage of the share price.

Terms you'll see in the app

Watchlist — your saved stocks, tracked with live Hold/Sell signals and performance since you added them.

Diversification — spreading across sectors, regions, company size and correlation so a single bad bet can't sink your portfolio.

Market Valuation & Cash Range — whether the overall market looks cheap, fair or expensive right now, and a suggested cash buffer.

Penny / micro-cap guard — extra caution flags on very small, thinly-traded stocks where data and prices are less reliable.

ESG — Environmental, Social & Governance: how responsibly a company treats the planet, its people, and how it is run.

References

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Risk & compliance

This handbook is educational research only and is not financial advice, investment advice, or a recommendation to buy or sell any security. Examples, backtests, internal samples and statistics are historical or hypothetical and may not repeat. Value can underperform for years; cheap stocks can be value traps; concentration can destroy a good average. You are responsible for your own decisions, taxes, costs, risk tolerance and horizon. Past performance does not guarantee future results.