Money Stuff: The Video of Dumb Investment

A central problem for financial regulators is that people want to sell risky financial products to retail investors, and retail investors really want to buy them, but then later they regret it. Life would be pretty easy, for the regulators, if the retail investors all desired what was good for them, if they all wanted to prudently invest in low-cost diversified productive investments. Then the regulators could just prohibit selling spicier stuff to retail. “I’ve got an exchange-traded fund that provides three times the daily performance—,” some company would say, and the regulator would say “nope!” and move on. “I want sell private-company—,” nope! “I want to offer an emerging asset class of bets on football—,” nope! “Doge—,” nope! Go ahead and sell that stuff to, you know, hedge funds with at least $1 billion of assets. But for retail investors, nope.

The problem is that a lot of retail investors really do like some fun in their investments, and get angry if the regulator paternalistically tells them what they can and can’t invest in. “Why do hedge funds get to buy all the fun stuff, while I am stuck with index funds,” they ask. 

And so the regulators have to let retail investors buy all sorts of stuff, stuff that will predictably go wrong for a lot of them, and that they will predictably complain about. “Why didn’t you stop me from buying that terrible stuff,” the retail investors will eventually ask the regulators.

The standard solution is disclosure: “You can sell risky stuff to retail investors, but you have to tell them what they’re buying and how it might go wrong.” One problem with this is that the retail investors probably won’t read the disclosure. Another problem is that even this might be too paternalistic. In the US, there are rules that require companies to publish financial information and risk factors if they want to sell stock to retail investors, but some companies do not want to disclose this information, so they remain private and only sell stock to institutions. And regulators and financial-industry executives view it as a serious problem that retail investors are not able to buy shares in the companies that do not disclose their information! “We need to find ways to get retail investors access to private companies,” they say, quite seriously. Why do hedge funds get to buy the stocks that don’t disclose their financial information, while retail investors are stuck with the ones that do?

I have proposed my own solution, the Certificate of Dumb Investment, in which, to buy a risky thing, you have to go to the local office of the US Securities and Exchange Commission and sign a form saying “I want to buy a dumb investment. I understand that the person selling it will almost certainly steal all my money, and that I would almost certainly be better off just buying index funds, but I want to do this dumb thing anyway. I agree that I will never, under any circumstances, complain to anyone when this investment inevitably goes wrong.” This may or may not deter retail investors from buying dumb stuff — maybe it will encourage them? — but the important advantage is that, if they lose all their money, they can’t complain to the regulator afterward. It solves the regulator’s problem.

Nobody seems to like this, though, and so another option is what you might call “investor education,” or even “financial literacy.” The idea is that, to trade some particular spicy thing, retail investors have to review some information about the thing and perhaps pass a test of their comprehension. Go ahead and trade options, if you can answer a few basic questions about what an option is. 

What is the mechanism of this solution? How does it make retail investors better off? You could imagine a few possibilities:

1. The investors will learn enough about the product to trade it successfully. You take a one-hour regulator-mandated course on football betting, and you get good enough at football betting to make money consistently, or at least not lose too much.
2. The investors will learn enough about the product to decide not to trade it. You take a one-hour regulator-mandated course on leveraged single-stock ETFs, and you are like “wait actually I don’t have any edge in predicting the daily movements of the underlying stocks so I’m going to just sit this one out.”
3. The training will be so long and boring, or so difficult, that no one will pass it and so no one will be able to trade the product. “We love innovation and are happy to let anyone trade zero-day options,” the regulators can smirk; “they just have to answer a few simple questions first.”

The Financial Times reports:

South Koreans wanting to pour money into risky investment products will first have to watch a training video as regulators strengthen oversight of an army of retail traders known for their aggressive strategies and tolerance for volatility.

From Monday, brokerages will automatically block investors wanting to put their funds into leveraged or inverse ETFs and who cannot provide the certification number given to those who have completed the one-hour online training.

The training focuses on the structure of leveraged ETFs, the cost of hedging and the compounding effect that can magnify gains and losses. Those investing in derivative products overseas will have to do a three-hour mock trading session.

Love it! An equity strategist lays out the problem:

“When the upward direction is set, you can make high returns quickly but these leveraged products also mean that you can fall into hell when the direction is reversed.”

Really what you want is for the videos to be hosted by cynical former derivatives structurers who have now gone straight. “Let me tell you the tricks we used to use,” the host can say, between drags on a cigarette. I guess the problem is you don’t want to make the product sound too cool. If the training is too good — too technical, too informative — then it will just inspire more people to trade leveraged ETFs.

Elsewhere, the Wall Street Journal reports:

“There’s a really bright line between investing and gambling,” [Charles Schwab Corp. Chief Executive Officer Rick] Wurster said in an extensive interview with The Wall Street Journal. “I really worry about the message that’s being sent to young investors that you’ve got to get these quick hits.”

Robinhood CEO Vlad Tenev has emerged as one of the most persuasive messengers to young investors. At his company’s annual summit for active traders earlier this year, Tenev compared playing the markets to circling a racetrack, where the “machine can make all the difference.” Trading, he said, was “high stakes,” and “one of the most intense lifestyles out there.” And to make his point, Tenev wore a race-car driver’s jumpsuit emblazoned with Robinhood’s logo. 

Right, that is the problem in a nutshell, the regulators surely want retail investing to be the least intense possible lifestyle, but when you give investors the choice between “it’s like driving a race car” and “shhh shhh shhh don’t chase quick hits,” you can see why a lot of them pick the race car.

One event that you might want to predict is: Will the stock of Alphabet Inc. go up or down? Alphabet closed at $310.52 per Class C share on Friday; you could imagine a prediction-market contract that pays off $1 if Alphabet closes above $310.52 today and $0 if it closes below. (Or you could imagine a more linear contract that pays $1 for each dollar that Alphabet closes above $310.52, and negative $1 for each dollar below. [1] ) That contract could be listed on a prediction market like Kalshi or Polymarket, and if you had some edge in predicting Alphabet’s stock price, or some gambling interest in doing so, you could express it with the prediction contract.

This does not really happen, for two reasons:

1. If you want to predict Alphabet’s stock price, you can do that in the stock market. If you think it will close above $310.52, you can just buy the stock! Or buy a call option! Or maybe you can find a market maker who will sell you an over-the-counter zero-day binary option. The market for stocks and stock derivatives is large and varied and liquid, you can get as much Alphabet exposure as you want, and it would be weird to go to Kalshi to get it.
2. This sort of contract — a prediction-market bet on whether a single stock [2]  will go up or down — is pretty clearly a “securities-based swap” under US law, a broad term that covers most single-stock derivative contracts. So it is regulated by the US Securities and Exchange Commission and subject to special rules that would make it hard for the prediction markets to list. (Not legal advice!)

In particular, the SEC prohibits insider trading in the stock market, and it extends the same rules to the stock derivatives market. If you are an executive at a public company and you have inside information about an upcoming merger, you can’t buy your company’s stock, and you equally (more so?) can’t buy call options.

The rules in commodities markets are at least slightly different. As we have discussed, historically the point of commodity markets was to let, like, grain producers and grain consumers hedge their grain-price risk; prohibiting them from trading on insider information would make no sense. There are insider trading prohibitions in the commodity markets — you can’t generally front-run your employer, etc. — but they are generally thought of as a bit looser than the prohibitions in the stock market. (Not legal advice!)

How this applies to prediction markets is a bit baffling, and some people do go around saying that insider trading in prediction markets is perfectly legal, though I certainly would not take that as legal advice. Still the broad point is something like this:

• You work at, say, Alphabet.
• You have inside information about some upcoming good news that will move the stock higher.
• “Ooh I should buy some stock,” you think, but then you remember that insider trading is illegal and you’d go to jail.
• “Maybe I could buy some stock options,” you think, and then you remember that insider trading on options is illegal and you’d go to jail.
• “Maybe I could pop on over to the prediction markets and see if there’s anything relevant there,” you think. Is insider trading in prediction markets illegal? I mean! Bill Ackman told you that it wasn’t. (Not legal advice!)

Can you buy a contract that pays off $1 if Alphabet’s stock goes up? No, you can’t, for the reasons I laid out above. But the Information reported on Friday:

Over the past week, a handful of accounts on Polymarket, the predictions site, bet OpenAI would release a new large language model by December 13. On Thursday December 11, OpenAI released GPT-5.2, and four of these accounts together made over $13,000, according to the trades displayed on their accounts.

The payout is adding fuel to suspicions that a handful of accounts on prediction sites such as Polymarket and Kalshi aren’t just lucky—they’ve got access to private information about tech companies including Google and OpenAI, perhaps because the account owners work there. As the popularity of these prediction sites jumps, more companies are making sure policies that have long prohibited employees from trading stocks based on confidential information also include prediction markets, which allow users to place small bets on events from Taylor Swift’s engagement to the chance of a SpaceX public offering. .,,.

Last week, a Polymarket account made over $1 million in a day, according to its trade history on the site, with an accurate series of bets about Google’s 2025 search data. This performance raised suspicions among internet commentators that a Google insider was behind the account. A spokesperson for Google declined to comment on whether the company has rules against insider trading on prediction markets.

If you have accurate inside information about Google search data, you could trade the stock, but that is obviously illegal and also messy: If the search news is good, you might buy the stock, but if there’s offsetting bad news that you didn’t know about, the stock might go down. Stock prices embed a lot of other stuff besides the single prediction you want to make. Just betting on the search data directly is a cleaner way to monetize your edge, and it is, you know, arguably more-ish legal-ish.

Also, while people instinctively think that insider trading in the stock market is bad, prediction-market intuitions are different. Prediction-market people do kind of philosophically love insider trading in prediction markets. The Information adds:

In fact, some companies, including Google and Anthropic, have established their own internal prediction markets. Employees can bet—without using any real real money—on questions such as when a team will finish a project.

In these cases, the markets’ predictions are kept inside the company, so they do not harm the company, said Dan Schwarz, who built Google’s current prediction market and served as chief technology officer of forecasting site Metaculus. For these internal prediction markets, rather than discouraging insiders, “you’re trying to get insider trading,” he said. “You’re trying to get people to reveal what they know.”

The point of a prediction market is to make prices correct, so insider trading seems helpful. (It is second-order unhelpful, because if prediction markets are full of insider traders then there’d be no one to trade against.)

And so one possible use case for prediction markets is “I have inside information, I want to insider trade, but the stock market is pretty regulated and frowns on insider trading. Is there a less-regulated market where I could accomplish the same thing?” I don’t think this is a correct analysis, but then I also didn’t think that commodities futures markets could offer sports betting, so what do I know.

I guess one story you could tell is:

1. Developing modern generative artificial intelligence models is good for the world. We will all be richer and happier and have more stuff if robots can do all of our work for us, etc. Every researcher working in an AI lab is contributing to the wealth and happiness of humanity.
2. Developing quantitative trading algorithms is pretty much zero-sum. If one hedge fund or proprietary trading firm develops a better algorithm, it will tend to make more money at the expense of other funds or firms. Every researcher working on quant trading is competing for a slice of a fixed pie.

You don’t have to believe this story, and I don’t think I do. You could, for instance, think that generative AI is bad. Or you could think that hedge funds and prop trading firms compete to make markets more efficient and liquid in ways that are good for humanity, that they are in positive-sum competition to allocate capital efficiently. Still I feel like the story above is kind of a standard intuitive one.

If you believe that story, it will inform how you think about, for instance, the liquidity of the respective job markets:

1. It is very important that AI researchers be put to their best and highest use. If you are an AI researcher and you are even a little bit unhappy with your job, you should immediately move to a different AI lab where your skills can be put to better use. The fate of humanity depends on it!
2. It is very important for hedge funds and prop trading firms to preserve their trade secrets. Those trade secrets are not fundamental research, shared science that makes humanity better off. They’re just tricks that the hedge funds play on each other, and it’s not sporting for one fund to steal another’s tricks.

Again! You don’t have to believe that story! But we talk a lot about the extensive gardening leaves of quantitative finance researchers, who often have to go years between quant finance jobs, so as to preserve their previous employers’ trade secrets. Meanwhile in AI, the Wall Street Journal reports:

OpenAI told staff this past week that it was ending a compensation policy that required employees to work at the company for at least six months before their equity vests.

The change to the “vesting cliff,” announced by applications chief Fidji Simo, is designed to encourage new employees to take risks without fear of being let go before accessing their first chunk of equity, according to people familiar with the matter. ...

The decision to loosen or do away with restrictions meant to ensure new hires stick around reflects the frenzied competition for top-tier technical talent within the AI industry. Tech companies typically have a one-year vesting cliff for new employees, preventing them from having to give away stock to hires who leave quickly or don’t work out.

But with AI companies including Meta Platforms, Google and Anthropic wooing top researchers with pay packages that can be worth $100 million or more, researchers and engineers have been able to hold out for the most-attractive terms, and in many cases have been quick to leave jobs they have found not to their liking.

The industry norm in AI is apparently that they’ll give you $50 million to come work for them, but you can leave whenever you want. It must be very important!

Similarly with AQR:

AQR Capital Management is riding high again, topping benchmarks across strategies and growing assets at a record pace. In the process of engineering a comeback, however, the quant pioneer has reined in a policy that once made it an outlier among hedge funds: An unusually open approach to explaining how it invests. …

Yet AQR is more discreet regarding the details behind its methods these days, wary of revealing the strategies that give it an edge — differentiated alpha, in industry jargon. Research papers delving into the fund’s trading ideas come less frequently, partners are increasingly reticent, while fresh money is flowing into more opaque products, including tax-aware and multi-strategy funds.

“Are we less transparent now? Yes, absolutely,” said John Huss, co-head of AQR’s macro strategies group. That’s because “we think that we’re finding a lot more idiosyncratic or differentiated alpha than 10 or 15 years ago.” …

Factor-based strategies that AQR has long employed — cheaper stocks outperforming more expensive ones, for example — have gone through stretches of weak performance and face increased competition. Now, the firm has added harder-to-copy techniques: quicker integration of new data and research, unique trading signals and a growing embrace of machine learning.

One story that you might tell here is that understanding what drives stock price returns is both a genuine contribution to human knowledge and a way to make money. (Much as large language models are.) If you want to attract the very best researchers of stock-price returns (or LLMs), the ones who have outsized ambitions to advance human understanding, it is a nice recruiting perk if you let them publish their results. But once the fundamental discoveries are public and you are competing for incremental advances, maybe you keep those to yourself.

Another story that you might tell is that “cheap stocks outperform more expensive ones” is in some sense a genuine contribution to our understanding of the world and the sort of result you might proudly publish, while “we can build a long-short portfolio to maximize the tax losses our clients can harvest under the US Internal Revenue Code” is more … you know, that one’s just for you and your clients, nobody needs to read that in a journal.

As far as I can tell, the preferred career path for a young person these days is:

1. Get a prestigious job at an investment bank, private equity firm or big tech company; 
2. Post day-in-the-life/get-ready-with-me/my-5-to-9-after-9-to-5/etc. content on TikTok; and
3. Eventually leave the finance/tech job to be social media influencer.

But here’s a guy who did the opposite: He apparently started as a TikTok creator, and then went to a bunch of financial industry recruiting events to record somewhat comic content. “Do you even know all four values at Goldman,” he asks, etc. Disclosure: I used to work at Goldman and now am a media influencer, so.

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