I'm Fabio Brina — founder of Zyberno.com and author of the Brina Gap: the published valuation framework that ranked first among all pure valuation metrics tested on the complete, survivorship-free S&P 500 — open data, open code, citable DOI. 18 years following markets, 12 years managing my own portfolio, 6 years guiding investors through portfolio decisions. Built for the long run.
The Brina Gap measures the difference between the growth a business can fund from its own returns and the growth the market is already pricing in.
A framework for identifying equity mispricing by comparing two independently derived growth rate estimates — what the business can structurally deliver, versus what the market price implies it will.
Validated on the complete, survivorship-free S&P 500 — every constituent 2010–2024, including delisted firms. In head-to-head testing it ranked first among all pure valuation metrics — ahead of book-to-market, earnings yield, EPV, and the 90-year-old Margin of Safety, the foundation of value investing.
Traditional valuation anchors in the past. Price-to-earnings ratios, earnings yield, and historical DCF models all rely on demonstrated performance. But markets price the future. The result is a systematic blind spot: a stock can look cheap relative to history while the market has already correctly identified deteriorating economics — or look expensive while the market persistently underestimates a compounding machine.
The Brina Gap is an original framework developed and authored by Fabio Brina.
The two estimates are structurally independent: gf is derived entirely from financial statements, with no reference to price. g* is derived entirely from the current enterprise value, with no reference to capital efficiency. Their comparison is non-circular and informationally additive.
Validated on the complete point-in-time S&P 500 — not a handful of hand-picked names — every constituent 2010–2024, including firms later acquired or delisted. The owner-earnings Margin of Safety couldn't beat a coin flip (51.2%); the Brina Gap beat it head-to-head (+3.9pp, p = 0.004). Its sharpest edge is flagging overpriced stocks: 58.9% accuracy (p ≈ 3×10−8), with value traps — stocks that look cheap but aren't — caught at 59.5%. In 2012 it flagged NVIDIA as undervalued, years before the AI boom — a call that beat the market by +40.4 percentage points a year.
Two independent signals. The Margin of Safety tells you if the stock is cheap relative to what the business has historically generated for owners — it reads the past. The Brina Gap tells you if the market is underestimating what the business is capable of going forward — it reads present capital economics against what the current price implies about the future.
The sharpest reading is the warning. A stock that looks cheap on its history while its Brina Gap is negative is a VALUE TRAP: the price still assumes more growth than the business can fund. Across the complete S&P 500, 2010–2024, those stocks went on to underperform 59.5% of the time. AT&T in 2019 looked 74% undervalued on a DCF; the Gap said the discount was deserved, and it lagged the market by 5.5 points a year. When both lenses point positive — DOUBLE DISCOUNT — the stock is cheap on history and priced for less growth than it can fund: the place to start digging.
This matrix is live on the Zyberno.com stock reports of about 2,700 US companies, calculated in real time from company filings. The paper also documents where the Gap fails, and why (Align Technology, FY2015).
Cite as: Brina, F. (2026). The Brina Gap: A Framework for Identifying Growth Mispricing in Equity Markets (Working Paper v3.0). Zyberno Research. DOI 10.5281/zenodo.20651608.
A survivorship-free audit of 16 fundamental metrics on the complete S&P 500. ROIC wins; the textbook sustainable-growth screen cancels itself out — the sustainable-growth paradox; and after risk adjustment, no screen earns alpha.
Zyberno was built to close this gap. Founded in 2025, it gives individual investors access to the same analytical frameworks used by institutional professionals — free, with no signup, and with methodology built in at every step.
Every tool in the platform came directly from real challenges encountered in years of advisory work: clients who knew what they wanted to analyse, but lacked the tools to do it effectively. Every tooltip, every built-in strategy, every calculation reflects a real question from a real investor.
The methodologies are transparent, the data is pulled directly from company financial statements and regulatory filings — not third-party aggregators — and every metric is explained in plain language. Because a tool you can't understand is just expensive guesswork.
One pattern I kept noticing after years of investing and working inside the industry was how financial tools and analysts adjust their outputs toward what looks reasonable. Zyberno was built on a different principle. The math produces a number — from fundamentals, from ROIC, from reinvestment rate, from a reverse DCF — and that is what we show. Every assumption is visible. Every input is reproducible. If the model says a business is worth $500 and it trades at $300, that is the output. The job of the investor is to decide whether the assumptions are right, not to distrust the result.
I started learning about investing at 14. Not because anyone taught me — because I already knew, with unusual clarity for that age, that I wasn't going to spend my life making someone else rich with my time.
Like most beginners, I made the classic mistakes first. At 18, with real money finally available, I turned to trading and leverage. I was actually decent at it — disciplined, cautious, never overleveraged. But I'm calm by nature, and trading isn't. Watching positions, reacting to noise, living inside price movements that had nothing to do with what the underlying business was actually doing. The wins were real, enough to keep me at it. But never quite enough to justify what it cost.
The real cost wasn't the losses. It was time. Years of mental energy producing results I could have matched by doing nothing. It took that frustration before I picked up The Warren Buffett Way by Robert Hagstrom. That book introduced me to value investing as a discipline. Not a strategy. A way of thinking about businesses.
The question changed. Instead of "what will the price do?" I started asking "what is this business actually worth?" Graham came next, then Buffett's own letters, then Munger. I'd been looking at the whole thing backwards — chasing price when I should have been understanding value.
"Rule No. 1: Never lose money. Rule No. 2: Never forget rule No. 1."
— Warren Buffett / The philosophy that guides everything at Zyberno
When I finally made the switch, my first real share was Berkshire Hathaway. Real in every sense — not a leveraged position on a price movement, but actual ownership of a fraction of a real business. For the first time, I wasn't betting on what a stock would do. I owned a piece of what a company was.
Twelve years of managing my own portfolio using value investing principles followed — alongside six years as a financial advisor working directly with individual and institutional clients on investment decisions. Since the account opened in October 2022 (October 6, 2022 to April 25, 2026), my personal portfolio returned +218.56% against the S&P 500's +89.39% over the same period, as reported by Interactive Brokers. Returns are time-weighted, include unrealized gains on open positions, and have not been independently audited. The portfolio consists of approximately 10 long-term positions across different sectors, managed with a buy-and-hold value investing approach. Every figure here can be documented: the performance report generated by Interactive Brokers itself, and the records of my advisory work, are available on request to journalists, researchers and partners at [email protected]. I'm based in northern Italy, investing independently and building financial research tools that reflect the same standards I apply to my own capital.
I've always believed that rigour and honesty are the same thing in investing. If a framework has failure modes, you document them. If a tool makes assumptions, you state them. If a number is uncertain, you say so. The alternative — dressing things up to look more certain than they are — is how most financial products are sold. It's not how I work. That's what I'm most proud of in everything I've built.