A Note on My Books and CoursesHardcover Books: The Sketchbook of Wisdom & BoundlessClick here to buy Boundless (My book on timeless lessons for a meaningful, boundless life)Click here to buy Sketchbook (A hand-illustrated treasury of life’s greatest ideas)Click here to buy the combo (Boundless + Sketchbook (Get both books together and save — wisdom you’ll keep for a lifetime)Mastermind Value Investing membership: Learn, connect, and grow with a private community of serious value investors. Click here to join now.Value Investing Almanack newsletter: Deep, timeless investing insights delivered to your inbox every month. Click here to join now.This is from sometime around 2005 while I was just two years into my role as an equity research analyst. I was working on a mid-size auto ancillary company that had been on my watchlist for months.Auto ancillary was then an unglamorous industry, and the company had been doing roughly the same thing for decades. But it had a reasonable track record of generating cash, and recently its stock price had been drifting lower. It felt like the sort of under-the-radar situation that might turn into a good investment if the numbers lined up.I started in the usual way, pulling up a decade’s worth of financial statements. I went line by line through revenues, operating margins, capital expenditures, debt levels, and cash flows. Nothing leapt off the page. Everything looked… just fine. I decided to dig deeper and run the numbers.That’s when I opened Excel and began building what I thought at the time was a “proper” model. I laid out my growth assumptions, built in projections for capital expenditure and working capital changes, and even accounted for things like seasonal sales patterns.My models often had multiple tabs then, and included my base case, an optimistic case, and a pessimistic case. All the inputs were linked so that changing one assumption would ripple neatly across the entire model.So, for the better part of a week, I worked diligently on that model, adjusting revenue growth and margin estimates, tweaking discount rates, adding sensitivity analyses, and benchmarking against industry averages. By the time I was done, the model “told me” that the company was worth ₹1,038 per share. That number looked crisp and scientific, and I was proud of arriving at that.I sent the file to a senior colleague whose judgement I respected. He opened it, scrolled for maybe half a minute, and then asked me a question: “Okay…nice model…but do you actually like this business for what it really is? And if you were to pick 2-3 businesses from the auto ancillary space, would this business be there?”I remember hesitating, because the truth was I hadn’t thought about it in such plain terms. I had the number. I had the analysis. But somewhere in the pursuit of modeling the company’s future and also trying to do it precisely, I’d stopped asking the simpler, more important questions.I also see it clearly now that my senior colleague’s question wasn’t meant to criticise my work. It was meant to cut through it. “Do you actually like this business?” is a deceptively simple question, but it forces you to step outside the comfort of the Excel sheet and confront reality. At that time, my reality was that I didn’t know. I knew the growth, margins, and valuation numbers, but I hadn’t formed a conviction about the business itself.When I think about that moment, it brings to mind one of John Maynard Keynes’ lines that I’d internalized only later in my career:It’s better to be approximately right than precisely wrong.In hindsight, my model looked rigorous, but in reality, it was built on a foundation of estimates and guesses about the next 3-5 years. And these were guesses that, no matter how carefully considered, could still be completely wrong. I had spent hours crafting an exact figure that gave me a false sense of certainty.This is a trap many analysts and investors, especially early in their careers, fall into. Numbers feel objective, while judgement feels subjective. So we gravitate toward what can be measured and shy away from what must be decided. But markets reward sound judgement more than perfect models, because real-world outcomes are rarely the same as your forecast, no matter how good it looks in Excel.Over time, I’ve come to see financial and valuation analysis less as a quest for one or a few exact numbers and more as a range of reasonable possibilities. If I believe a business might be worth between ₹800 and ₹1,200 per share and I can buy it for ₹500, the exact figure doesn’t matter much. What matters is the size of the gap, also called the “margin of safety,” or the cushion that protects you from being wrong on some of your assumptions.Warren Buffett once explained this with a wonderfully simple analogy:If we see someone who weighs 300 or 320 pounds, it doesn’t matter — we know they’re fat. We look for fat businesses.Charlie Munger often follows such comments with his own brand of blunt wisdom:There’s no one easy method that can be mechanically applied by a computer that will make someone who pushes the buttons rich. You have to apply a lot of models.And as I learned from Charlie later, those models aren’t just mathematical ones but also come from history, psychology, biology, and everyday observation.Numbers are important, but they’re just one lens. The real skill is knowing when they’re telling you something meaningful and when they’re simply giving you the illusion of precision.Looking back, the company I was studying back then wasn’t bad, but it wasn’t the kind of fat pitch Buffett and Munger talk about. The returns in my model depended on optimistic assumptions, like higher growth, better margins, and smoother competitive conditions than history suggested.The investment passed my spreadsheet test but not what Buffett may have called the “scream test”:It’s sort of automatic. If you have to actually do it with pencil and paper, it’s too close to think about. It ought to just kind of scream at you that you’ve got this huge margin of safety.It’s the idea that a truly great opportunity should be so obvious that you don’t need complex or a lot of calculations to see it.It’s here that another of Munger’s lines resonates:Things that are not worth doing, no matter how good they are, are useless.That company was a perfect example. I could make the model sing, but the underlying business just wasn’t compelling enough to justify the effort.I eventually moved on, and in hindsight, that was the right decision. Sometimes the smartest choice is to walk away from something that looks okay on paper but doesn’t inspire genuine conviction.These days, I still build and work on models, but they are much simpler and more basic compared to what I was doing 20 years ago. Also, I start with a much simpler filter — the same one my colleague applied with that single question. Before opening Excel, I ask myself: “Do I really like this business?”That means thinking about the quality of the business, its competitive moat, the people running it, and whether I’d be happy owning it even if the market shut down for five years. If I can’t answer that plainly, I stop. Only after I feel good about those fundamentals do I start running the numbers, and even then, the purpose of the model is to confirm my judgement, not to “create” it.You see, precision can make you feel safe, but clarity is what actually keeps you safe. A precise valuation is worthless if it’s built on flawed assumptions. A clear, common-sense judgement that is backed by a wide margin of safety will protect you even when the future doesn’t unfold exactly as you expect.That shift, from pursuing precision and exactness to seeking clarity, even if approximate, has been one of the most valuable changes in my approach since those early days.You’ll often find good investors building their track records not by being the most precise people in the room, but by being the clearest thinkers. They focus on what’s knowable, they stay within their circle of competence, and they refuse to let complexity cloud their judgement. And while they may use plenty of numbers, they never mistake an Excel model for reality.That old company analysis taught me a lesson that no textbook could: in investing, the precision doesn’t save you. The margin of safety does. You don’t need to measure the ocean with a ruler. You just need to know when the tide is high enough to float your boat, and when it isn’t.Once you learn to see it that way, you’ll never again feel the urge to pin everything down to ₹1,038.Two Books. One Purpose. A Better Life.“Discover the extraordinary within.”—Manish Chokhani, Director, Enam Holdings“This is a masterpiece.”—Morgan Housel, Author, Psychology of MoneyClick here to buy BoundlessClick here to buy SketchbookClick here to buy the combo (Boundless + Sketchbook)The post The ₹1,038 Illusion appeared first on Safal Niveshak.