The Economics of Trust

When I started my career, my company sent a PDF to a bank. That PDF essentially said, “This person is trustworthy.” The next day, I picked up the phone and called someone I’d never met in another country. I told him I’d wire $200 million, and he’d wire me €195 million back. Settlement in two days.

Due to time zones, he had to send his funds first. And he did. A complete stranger wired €195 million based on a phone call.

After a few months, this stopped amazing me. It became routine. Normal, even. But when you step back and think about it, is it really normal? Or is it a crazy features of modern finance?

The Infrastructure of Trust

Getting to this point required building enormous institutional infrastructure. My company asked a third-party rating agency to evaluate it, an agency we all trust (despite 2008). Another third party audits and certifies the financial statements. Yet another trusted institution. My company banks somewhere that doesn’t panic when the account is temporarily short a billion dollars.

All of this works because everyone believes in a fourth party: the rule of law. Courts that will fairly settle disputes if something goes wrong.

This trust has enormous economic value. It dramatically lowers transaction costs. It makes markets liquid. It enables capital to flow efficiently across borders and time zones.

Why Trust Matters in Personal Finance

The general population doesn’t have the resources or expertise to evaluate every facet of every financial product. Most people don’t have time to read prospectuses or understand fee structures. Regulation helps, but it’s imperfect…and as we’ve seen recently, its effectiveness depends heavily on who’s in charge.

This is why community is crucial.

Earlier this year, Simplify launched a money market ETF and began using it as the cash component in their other funds. That seems reasonable enough. The problem? They didn’t waive the fees. Since many Simplify products are derivative-based strategies that hold up to 80% in cash collateral, investors in those funds suddenly found themselves paying an extra 10 basis points in undiscosed fees.

I spend far more time than the average investor analyzing financial products, and I wouldn’t have caught this change in a thousand years. But the FinTwit community flagged it almost immediately. That’s the power of distributed attention. It’s the only realistic check on the questionable practices that industry insiders try to slip past investors.

Building Reputation Through Process

There are people I trust who use testfol.io. They have institutional credibility and reputations to protect. I trust they’ve done their due diligence. They’re experienced enough to spot problems. They’ve paid their dues in this field.

By earning the trust of these people, testfol.io has earned mine.

(sadly) I’ve spent over 10,000 hours watching markets tick by tick. Another 10,000 hours building financial models. All of my work has been tested by others who’ve done the same. I stand on their shoulders, and they stand on mine.

This enables me to spot when someone else makes a mistake. And I call it out, just as others have called out my mistakes. Because trust is important. The bank trusts that I won’t place trades my firm can’t support. My colleagues trust they can use my models without auditing them each time. We have processes, and we trust those processes because we trust the people behind them.

The Cost of Breaking Trust

Recently, I’ve been watching someone build a Portfolio Visualizer clone for European UCITS products. It’s frankly absurd that nothing like this exists in Europe yet. Curvo has a backtester that’s riddled with bugs, and there’s essentially nothing else. Portfolio Visualizer was created by an independent developer, so why am I so concerned about the same approach here?

Because this particular developer has demonstrated a fundamental lack of understanding of basic financial concepts (and sometimes, even common sense).

A half-baked backtesting platform isn’t just “not as good” as a proper one: it’s actively worse than having nothing at all. When someone uses a flawed platform and reaches the wrong conclusion, they don’t just lose trust in that specific tool. They lose trust in the entire system. The backtesting methodology. The data. The concept itself.

There’s an even deeper problem. The more confident you are in your conclusion, the harder it is to accept you were led astray. For the average user, spotting errors in backtesting results is extremely difficult. This isn’t a crossword puzzle generator that occasionally spits out incorrect words. This is a platform where people make critical decisions about their financial future.

The stakes are real. And so is the responsibility that comes with building these tools.

Prediction Markets Paradox

Prediction markets present an interesting case study in trust dynamics. In theory, they work best when monetary incentives attract informed insiders. People with superior information trade until prices reflect accurate probabilities. That’s the efficient market hypothesis in action.

But there’s a problem. Insider participation creates adverse selection for everyone else. If you know that people with material non-public information are trading against you, why would you participate? You’re essentially guaranteed to be on the wrong side of informed trades.

This creates a fundamental tension. The mechanism that makes prediction markets accurate, insider participation, is the same mechanism that destroys trust and liquidity among retail participants.

Different platforms have made different choices. Kalshi prohibits insider trading. Polymarket allows it.

Kalshi is optimizing for broad participation and trust. If you know insiders can’t trade, you’re more willing to participate. The market might be less accurate, but it’s more accessible.

Polymarket is optimizing for accuracy. Let the informed traders set prices, and everyone else can free-ride on that information. The market might be more accurate, but retail participation could suffer.

It’ll be fascinating to see which approach wins. Does accuracy matter if nobody trusts the market enough to participate? Or will retail traders accept being potentially outmatched in exchange for access to crowd wisdom?

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