Why Investing Is Hard, and Trading Is Even Harder

When you first start investing, it’s very easy to fall into a mental trap: that markets are predictable. It’s an especially easy trap to fall into if you happen to be right on your first attempt. You form a thesis about an industry or a company, it plays out, and suddenly it feels like you’ve cracked something. Some of those theses genuinely are strong and well reasoned. But the reality is that markets are far more random than they look from the outside — and being right once tells you a lot less than it feels like it does.

The consensus problem

Here’s what makes it harder still: your thesis is usually built on information the market has already processed. In other words, the consensus — what most investors already believe and expect — is already reflected in the price.

This idea is simple to state but genuinely mind-bending the first time you actually sit with it, so let me slow down. In most markets, price reflects the current value of a good. The stock market doesn’t work that way. A stock’s price bakes in not just what a company is worth today, but what the market expects it to be worth in the future — its expected growth is already priced in.

Say your thesis is that credit card payment volume will keep growing, so Visa and Mastercard look like good investments. Sounds reasonable. But if that growth is obvious enough for you to see it, it’s obvious enough for the market to see it too — and the market has already baked that expected growth into today’s price. You’re not getting it for free. You’re paying for it upfront, today, with the price you buy at.

Which means: to actually make money from a thesis like that, being right isn’t enough. You have to be right when the market is wrong — accurately estimating growth in a case where the market is underestimating it (or overestimating it, if you’re brave enough to bet against consensus and short the asset). That’s a much higher bar than “I have a reasonable thesis.” It’s genuinely hard, and very few people can do it consistently.

The Efficient Market Hypothesis

There’s a whole academic theory built around this idea, and it’s worth knowing: the Efficient Market Hypothesis (EMH). In plain English, it says that asset prices already reflect all available information, which is exactly the consensus problem above, formalized. EMH usually comes in three flavors:

• Weak form — prices reflect all past price and volume data. This is the one that rules out most technical chart-reading as a reliable edge: if the pattern is visible in the price history, it’s already visible to everyone else too.
• Semi-strong form — prices reflect all publicly available information, not just past prices. Earnings reports, news, industry data — the moment it’s public, it’s priced in. This is the one that matters most for the Visa/Mastercard example above.
• Strong form — prices reflect all information, public and private. This one is the most debated, since it implies even insider information couldn’t give you an edge — which doesn’t really hold up, and it’s the version most academics push back on.

None of the above is globally recognized as the right one, but if you believe in the strong form, trading makes no sense — there would be no room for inefficiencies to exploit. On the other side, I think there’s good evidence that markets sometimes struggle to process information and weigh it correctly. Two examples of this: the retail-inflated GameStop price a few years ago, and the 2008 financial crisis, where some banks lost 90% of their value way later than when the underlying risk had actually built up.

Being wrong

There’s a question that matters more than “am I right?” — and it’s “what happens if I’m wrong?” It’s tempting to skip it, because when you’re right, everything is easy: the market moves in your favor and you look smart. The real test is what happens when it moves against you — can you defend the position, or does it take you out completely?

The example I keep coming back to is XIV. XIV was an exchange-traded note that let you bet volatility would stay low — essentially the inverse of the VIX (the market’s benchmark measure of expected volatility). For years it was one of the best-performing trades around, because volatility mostly does stay low, most of the time. Then on February 5th, 2018, volatility spiked violently in a single session, and XIV lost over 90% of its value overnight. The fund was liquidated. And this wasn’t some retail product run by amateurs — it was managed by Credit Suisse, a major global bank, with professional quants and risk models behind it. Being professionally managed didn’t save it, because the position simply couldn’t survive being wrong.

That’s why asking what happens if you’re wrong matters more than confirming you’re right: a good thesis that can’t survive being wrong isn’t actually a good position, no matter how professional the team behind it is.

There’s a deeper reason to keep asking that question too, beyond risk management. Continuously trying to prove your own thesis wrong — instead of only looking for evidence that confirms it — is a lot closer to the scientific method than to how most people actually invest. Science doesn’t advance by confirming what we already believe, it advances by trying, and failing, to disprove it. Investing works the same way: a thesis that survives your best attempt to falsify it is worth a lot more than one you only ever tried to confirm. That’s also why this approach holds up over the long run, instead of quietly relying on a lucky streak of confirming what you wanted to believe anyway.

What about trading?

Investing, at its core, lets you buy something at a fair price and simply wait — you’re putting your money into something you believe will appreciate over time, and time is doing a lot of the work for you.

Trading is a different game. It’s operation-based — you’re not making one long-term bet, you’re trying to profit trade by trade, and a profitable trade usually means you’ve found and exploited a market inefficiency. Those inefficiencies are genuinely hard to detect, and even when you find one, it doesn’t stay open for long. I wrote about one of the more durable ones — the gap between what the market expects volatility to be and what it actually turns out to be — in buying vs selling options.

And to make the game harder still, you’re not competing against other individuals with a laptop and a hunch. Large hedge funds already dominate these opportunities, especially on large and mega-cap stocks, and especially on short timeframes. They have the infrastructure, the data, and the speed to find and close an inefficiency faster than almost anyone else.

How can a retail trader actually be profitable?

In my opinion: with good strategies, precise execution, and strict risk management. Not one of these — all three, together. A great strategy with sloppy execution leaks money. Great execution without risk management eventually meets the one trade that wipes out months of gains. This is genuinely the hard part of trading, and it’s not really about being smarter than the market — it’s about being more disciplined than it, on the timeframes and setups where being disciplined actually matters.

It’s also exactly what we’re building into Ctrl-Trade: tools for tracking risk properly across your whole portfolio, keeping your cost basis and P&L honest through every roll and assignment, and making it easier to execute your process the same way every time instead of improvising under pressure. None of that makes the market less efficient. It just makes you a little more disciplined against it.

Trade Well,

Francesco