SIG Founder: Why Am I Bullish on Prediction Markets?

Source: Generating Alpha Podcast

Translation: Jiahua, Chaincatcher

Who is Jeff Yass?

This week, Generating Alpha welcomes a very unique guest—Jeff Yass, founder of one of the world’s most successful trading firms, Susquehanna International Group (SIG).

Jeff is a legendary figure in finance, applying the principles ofpoker, probability, and decision theoryto the markets. Over the past forty years, he has quietly built a global trading giant, operating behind the scenes on Wall Street, trading everything fromoptions to cryptocurrencies, all based onmathematical precision and rational thinking. He is also one of the most influential yet mysterious and low-profile figures in modern finance, and this episode marks hisfirst-ever podcast interview.

In this short episode, we focused on one topic:prediction markets—why Jeff believes they are the future of humanity’s search fortruth, how they can improvebusiness and government decision-making, and how they reveal the immense power ofincentives, information, and human behavior.

Recording this episode was a real pleasure for me, and I hope you enjoy listening to it as well.

Host: Jeff, thank you so much for joining us, and for taking the time.

Jeff Yass: My pleasure, Amir, let’s get started.

The Core Value and Significance of Prediction Markets

Host: To lay the groundwork for this conversation, let me start by asking: What is your overall view onprediction marketsright now? How important are they to you and SIG?

Jeff Yass: Prediction markets have been a real passion of ours for many years. They bring tremendous value to the world. If youdon’t have accurate probabilities for events, you can’t make good decisions. And prediction markets are currently themost accurate way we know of to estimate those probabilities. So we see them as anamazing toolthat will bring huge benefits to society.

Host: From a broader perspective, how do you think prediction markets will evolve over the next decade, especially regardingregulation and gambling legislation?

Jeff Yass: In the traditional gambling space, honestly, we’re not entirely sure. But the world is gradually realizing that the exchange model, like Europe’s Betfair, where people buy and sell with each other, is afairersystem that can dramatically lower costs. Right now, the traditional gambling “vig” is about 5%, but if people can trade directly on an exchange, we think it could drop to 1-2%. That’s a huge win for anyone who wants to participate in sports betting.

But our real motivation for promoting prediction markets is touncover the truth. Our favorite example is theIraq War. When Bush first invaded, he said it would cost only 2 billion dollars, his economic advisor Lawrence Lindsay said it might reach 5 billion, and he was punished for telling the truth. The real number later turned out to be 2 to 6 trillion dollars.

If there had been a prediction market at the time, asking “What will the total cost of this war be, over/under?” I bet it wouldn’t have reached 2-6 trillion, but it would have been far higher than 5 billion, maybe around 50 billion. If the public saw that number, they’d say, “Wait, we don’t want this war! Politicians always say wars will be quick and cheap, but they never are!” So we need acredible source, and prediction markets are anobjective, trustworthy source—because if you bet wrong, you lose money.

If people had seen such a terrifying number, anti-war voices would have been much louder. Prediction markets really have the power toslow down the constant lying by politicians, and that’s the main reason I want to see them thrive.

Host: It’s almost like “the people’s truth,” rather than the polluted, spoon-fed kind.

Jeff Yass: Exactly! And it’s not just for ordinary people, but for experts too. You and I might not know the true cost of a war, but there’s always a small group who do, and they’ll bet and push the price to a reasonable level. Ordinary people can’t possibly know the cost of war, but when you see experts battling it out in the market, putting real money on the line, you cantrust that number. Just by looking at the prediction market price, you’re more professional than politicians who either make up numbers or deliberately lie.

Manipulation and Risk

Host: I guess in the future, prediction markets will be used to price more things like financial instruments and support other decisions. But how do you preventprediction markets from being manipulated?

Jeff Yass: It’s the same as preventing any market from being manipulated—if you want to move the price and there are enough participants, you’ll have tolose a lot of money yourself. For example, if you want to push the Iraq War cost below “50 billion,” fine, we’ll bet hundreds of millions against you, saying you’re wrong. Your manipulation plan will beextremely expensive, maybe hundreds of times more than running a few misleading ads (which only cost a few million, while this would cost hundreds of millions). So this in itselfprotects the integrity of the market.

Host: Going back a bit, you started out as a professional gambler, playing poker and betting on horse races. What similarities do you see betweengambling and prediction markets? Whatsystemic risks and opportunitiesdoes this bring?

Jeff Yass: I honestly don’t see muchsystemic risk. What I see ismore truth, more rational and objective probabilities entering the market. The real systemic risk ispoliticians deceiving us with lies, and prediction markets are the antidote. Of course, there may be a tiny bit of manipulation, but compared to what we face now, it’sinsignificant.Competitive marketswill smooth out any problems.

Business Applications and Hedging

Host: So overall, how do you think companies like yours will integrate prediction markets intoeveryday decision-makingin the future?

Jeff Yass: For example, in 15 days New York City will have an election (the podcast was released on October 23). If you just watch TV or read the news, it’s hard to judge the real probability—some say “it’s too close,” others say “there’s no way New York would elect someone like Maami.” But if you look at the prediction market, his odds of winning are over 90%. If you’re deciding whether to move to New York or relocate your company there, youneed to know this probability; you can’t figure it out just by reading the papers or watching the news. Having this clear number will greatly help your decision-making.

Or say you’re a real estate developer and think your property will drop by $1 million if Maami wins, you canhedgedirectly. More importantly, you caninstantly get the most reliable probability,without reading a million articles or calling polling firms—the work is already done for you, and you get the best number to guide all your decisions.

For SIG, we’re always watching the probability of presidential elections, and since the stock market rises or falls depending on who’s ahead, we use prediction market probabilities to judge whether a stock isoverreacting or underreactingto political events.

Host: I can imagine that as prediction market volumes grow,large institutions will start participating, using prediction markets instead of traditional financial instruments to hedge. You recently partnered with Kalshi as one of their main market makers. How do you see the participation of companies like yours evolving as the market develops?

Jeff Yass: Prediction markets are still acustomized productright now, with institutions not really involved yet and most volume coming from relatively small players. No giant institution has yet placed huge bets on events like “Will the Fed raise rates?” But we believe that as regulation becomes clearer and the market more popular,institutions will flock in, and we’ll seeWall Street-sized bets. Right now, Goldman Sachs and Morgan Stanley are still cautious, but that will change sooner or later.

What I really look forward to is prediction markets influencing theinsurance industry. In many places, you simply can’t buy insurance because the government has pushed prices too low and insurers have left—like in Florida. But if you use prediction markets for insurance, you can post a contract: “Will wind speeds in your area exceed 80 mph in the next 48 hours?” Suppose the probability is 10%, and you’re worried about your house being destroyed, you can bet $10,000 to win $90,000, basically covering your loss. And you only buy when there’s a real hurricane threat, saving all the claims, advertising, and operating costs, making itmuch cheaperand fully customized.

Host: And much more quantifiable! Traditional insurers always want to assess how much you need, how much they’ll pay, etc., but prediction markets are crystal clear. As these markets eventually become fully regulated exchanges, do you think liquidity will mainly come fromlarge Wall Street institutionsorretail investors?

Jeff Yass: Both. And it will create huge opportunities. For example, if you’re a weather enthusiast living in Florida and you really understand hurricane probabilities, you can create your own market and say, “I believe the probability of disaster in this area is X.” Previously, this expertise couldn’t be monetized, but now you can make money from your knowledge andhelp ordinary people lower insurance prices.

Impact, Obstacles, and Learning

Host: Do you think prediction markets willinfluence the outcome of events themselvesin the future?

Jeff Yass: No. For example, that French guy who bought huge amounts of Trump on Polymarket—total nonsense. We just bet against him, he pushed the price up, we pushed it back down, and it had no effect on the outcome. This concern isn’t zero probability, but it’s beengreatly exaggerated.

Host: So what do you think is thebiggest obstacleto the widespread adoption of prediction markets? How do we overcome it?

Jeff Yass: The biggest obstacle is, as you can see from these questions, you can seewhere things might go wrong. These things immediately come to mind psychologically—things that could go wrong. Some things might go wrong, but things are already going wrong now. So as we get used to it, that obstacle will disappear. It might taketime, but people havefears, and they tend toexaggerate the negative impact. But as the product is accepted and people see its value and how muchmoney it can save them, those fears willdissipate. It might takeseveral years, but I’mvery optimisticthat we’ll get there.

Host: Some people worry that certain decisions shouldn’t be quantified. Do you think there are any decisions or predictions we shoulddeliberately avoid quantifying?

Jeff Yass: Good question. In theory, you could even create a market: “Should I marry this girl?” Maybe your friends and family would be more objective than you... but that’s probably going too far. So my answer is basicallyno.

Host: What’s something no one is talking about now, but prediction markets could achieve in the future?

Jeff Yass: The most important thing: they canprevent wars. Every war, politicians exaggerate, saying it’ll end quickly, cost little, and have few casualties—all lies. During the American Civil War, the Lincoln administration stopped conscription in 1862, thinking the war would end in weeks, but it ended up costing 650,000 lives. If people knew the real cost and catastrophic consequences in advance, they’d desperately seek alternatives to war.

Another example isself-driving cars. Many people oppose them now because they can imagine robots going out of control and killing people. But in the next 12 months, about 40,000 peoplewill die on US roads; if we had full self-driving, I guess it would drop to 10,000, saving 30,000 lives. If prediction markets showed that traffic deaths would drop sharply by 2030, policymakers would rush to accelerate self-driving adoption—because we could clearly see how many lives would be saved. Right now, everyone hesitates: “We don’t know if it’s good or not.” With an objective number, we’d move much faster.

One Thing Jeff Yass Wants to Tell the World

Host: Before we wrap up, if you could sendone message about prediction markets to the world, what would it be?

Jeff Yass: My mother used to say to me: “If you’re really that smart, why aren’t you rich?”

Prediction markets are objective. If you think the market probability is wrong, go bet and correct it to what you think is right. If you really are smarter than the market, you’ll make a lot of money and help society get the price right. If you can’t make money, maybe you shouldshut up—maybe the market knows better than you.

This will drive all the university professors crazy, because they want to be the experts, but they’re not. A bunch ofspeculators fighting with real money every daywill be far better than any professor. Driving professors crazy is a good thing in my view.

For example: when my daughter was 12, Obama was running against Hillary in the Democratic primary. All the top political scientists on TV said “Hillary is ahead by 30-40 points, it’s a lock.” I had my daughter check TradeSports(the only prediction market at the time), and she said, “Obama has a 22% chance of winning.” The market had already seen Obama’s uniqueness and charisma, and Hillary’s lead was meaningless. My 12-year-old daughter was more accurate than the world’s top political science experts. That’s the power of prediction markets.

Learning and Life Advice

Host: Final question: If you’re ahigh school studentnow, based on your years of success and hiring experience, what would you advise young people to learn today?

Jeff Yass: Of course you should learn computer science, you must know programming and AI. But if you really want to be adecision-maker under uncertainty—which is the essence of being human—you must masterprobability and statistics.

So many decisions in the world are made under uncertainty, and if you don’t understand the mathematical foundations of probability and statistics, it’s easy to make disastrous choices. For example, hurricane season comes and there are lots of hurricanes—is that a big deal? Has it always been like this? How much volatility is there? Is this evidence of global warming or just random fluctuation? Distinguishingsignal from noiserequires knowledge.

In 1958, when the Soviet Union launched Sputnik, the US was afraid of falling behind in the space race, so the whole country started learningcalculus. Now, to get into a good college or med school, you have to learn calculus—which is absurd, because 99% of people will never use it. Butprobability and statisticsare treated as secondary, and no one is required to learn them. So our country has a bunch of people who know calculus, but almost no one understands probability and statistics, which is completely backwards.

You musttake the initiativeto learn probability and statistics, and you must understandBayesian analysis. Harvard Medical School students get probability questions about diseases wrong by 100 times—these people are super smart, but the school didn’t teach them. Even doctors, if you ask “What’s the probability I have this disease?” will just say “Maybe you do, maybe you don’t.” You’ll think: Doctor, can you tighten up the market a bit?

Host: I’m learning calculus now... Looks like I need to catch up on statistics myself.

Jeff Yass: Calculus is beautiful, it’s my favorite subject, it’s art, it’s the key to science. But for most people,it’s of limited practical use.

Host: Last traditional question (I’ve asked 39 people already, and I’m 16 now): If you could give today’s 16-year-oldsone piece of life advice (career, relationships, anything), what would it be?

Jeff Yass: If it’s relationship advice—I trust the market.Don’t date someone all your friends think is crazy. Many people get caught up, and that’s when you need your friends to tell you the truth. You can do it anonymously, let them create a small prediction market: “Am I making a huge mistake dating this person?” So many people have their lives ruined by the wrong person because no one dared to tell the truth. You need to design a mechanism to let the truth come out.

We have a flaw:the bigger the decision, the less we think. We might spend ages deciding whether to buy a stock (which has minimal impact), but when it comes to choosing who to marry or date—decisions that affect your whole life—we often make them carelessly. We completely misallocate our time.

Host: I don’t have much life experience yet, but I totally agree! And I recommend everyone listen to my episode with Annie Dukeabout decision-making, which pairs perfectly with this one. Jeff, thank you so much for today!

Jeff Yass: Good luck, I really enjoyed it too, goodbye!