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 had never met in another country. I told him I would transfer $200 million, and he would pay me back $195 million. Settlement within two days.
Due to time zones, he had to transfer his money first. And he did. A complete stranger transferred €195 million via a phone call.
After a few months this no longer surprised me. It became routine. Normal even. But if you take a step back and think about it, is that really normal? Or is it a crazy feature of the modern financial world?
The infrastructure of trust
Reaching this point required building a massive institutional infrastructure. My company asked an outside rating agency to rate it, an agency we all trust (2008 notwithstanding). Another third party audits and certifies the annual accounts. Another trusted institution. My company banks somewhere where there is no panic if the account temporarily falls short by a billion dollars.
All this works because everyone believes in a fourth party: the rule of law. Courts that settle disputes fairly when something goes wrong.
This trust has enormous economic value. It reduces transaction costs dramatically. It makes markets liquid. It ensures that capital can flow efficiently across borders and time zones.
Why trust is important in personal finance
The general population does not 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 is imperfect. And as we have recently seen, its effectiveness depends greatly on who is in charge.
That’s why community is crucial.
Earlier this year, Simplify launched a money market ETF and began using it as a cash component in their other funds. That seems reasonable enough. The problem? They have not waived the fees. Because many Simplify products are derivatives-based strategies that include up to 80% cash collateral, investors in these funds suddenly had to pay an additional 10 basis points in undisclosed fees.
I spend far more time than the average investor analyzing financial products, and I wouldn’t have noticed this change in a thousand years. But the FinTwit community spotted it almost immediately. That is the power of distributed attention. It is the only realistic check on the questionable practices that industry insiders try to elude investors.
Building reputation through processes
There are people I trust who use testfol.io. They have institutional credibility and reputations to protect. I trust they have done their due diligence. They are experienced enough to detect problems. They have paid their dues in this area.
By earning the trust of these people, testfol.io has earned mine.
(unfortunately) I’ve spent over 10,000 hours watching the markets tick by tick. Another 10,000 hours building financial models. All my work has been tested by others who have done the same. I stand on their shoulders, and they on mine.
This allows me to see when someone else is making a mistake. And I name it, just as others have mentioned my mistakes. Because trust is important. The bank trusts me not to place transactions that my business cannot support. My colleagues trust that they can use my models without having to check them every time. We have processes and we trust those processes because we trust the people behind them.
The cost of breaking trust
Recently I watched someone build a Portfolio Visualizer clone for European UCITS products. It is frankly absurd that something like this does not yet exist in Europe. Curvo has a backtester that is full of bugs, and there’s really 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 shown 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 real platform: it’s worse than having nothing at all. When someone uses a flawed platform and comes to the wrong conclusion, they don’t just lose confidence in that particular tool. They lose confidence in the entire system. The backtesting methodology. The data. The concept itself.
There is an even deeper problem. The more confident you are in your conclusion, the harder it is to accept that you have been led astray. For the average user, it is extremely difficult to detect errors in the backtest results. This isn’t a crossword puzzle generator that occasionally spits out incorrect words. This is a platform where people make crucial decisions about their financial future.
The stakes are real. And that also applies to the responsibility that comes with building these tools.
Forecasting markets paradox
Prediction markets provide an interesting case study in trust dynamics. In theory, they work best when monetary incentives attract knowledgeable 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 leads to adverse selection for everyone else. If you know people are trading against you with material, non-public information, why would you get involved? You are 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 optimizes for broad participation and trust. If you know that insiders cannot trade, you are more willing to participate. The market may be less precise, but it is more accessible.
Polymarket optimizes for accuracy. Let the informed traders set prices, and everyone else can piggyback on that information. The market may be more accurate, but retail participation may suffer.
It will be fascinating to see which approach wins. Does accuracy matter if no one trusts the market enough to participate? Or will retailers accept that they may be outdone in exchange for access to the wisdom of the public?
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