[00:00] In 2007, Warren Buffett made a million-dollar bet on the dumbest investment strategy imaginable. Buy all stocks according to their size and do absolutely nothing for 10 years.
[00:16] In 2017, he won by a landslide and continues to win to this day. Index funds overall have delivered results that have been better than Wall Street professionals.
[00:29] This leads to the key question of this video. How can nothing beat the smartest traders on Earth? If money is evil, then that building is hell.
[00:41] To understand this paradox, we need to go deeper into the world Buffett bet again. $93,000! $93,000! An ongoing war between humans and machines over every penny.
[00:55] Their methods shrouded in secrecy, treated like a dark art. And I did one thing there that was quite good, because I can't tell you what it was, it's all classified. Which leads to a key question. Are these traders parasites stealing from average people?
[01:08] Or are they more like bees providing a hidden service? I used to think they were parasites until I became one myself. It began during one wild night I'll never forget, during the crypto bubble of 2017.
[01:22] That's when I realized I had it all backwards and finally understood Buffett's bet. I had been following Bitcoin since the early days when it was just a curiosity for geeks and libertarians holding a few coins for fun.
[01:34] But by 2017, Bitcoin had broken $1,000 and then $2,000, a milestone that made me realize money was about to flow into this market like never before. So I started joining these tiny new exchanges just to mess around.
[01:48] That's when something strange happened. I saw Bitcoin priced at $1,850 on one exchange, but it was trading at $2,000 everywhere else. So I placed what's called a market order, basically saying buy $100 worth at whatever the current price is.
[02:04] But my order just hung there. Nothing happened. I refreshed. Then I noticed there were no recent trades for the last few hours. I looked into something called the order book. This is the heart of how every exchange works.
[02:17] Think of the order book like jars at different price levels. Each jar represents people willing to trade at that exact price with what's called a limit order. There are buy side jars, people wanting to buy Bitcoin with dollars,
[02:32] and sell side jars, people wanting to sell Bitcoin for dollars. The fuller the jar, the more depth the market has at that price. In a normal active market, an order book usually looks like this.
[02:44] some people willing to trade right near the current price, but much more willing to buy at cheaper prices or sell at much more expensive ones. So when someone places a market order for $100 of Bitcoin,
[02:56] it automatically gets matched to the best available limit order on the sell side. If my $100 is bigger than what's in the cheapest jar, it simply moves up to the next jar and so on until my order is filled.
[03:09] That's the price I get. But what I saw in this order book was shocking. The sell side was completely empty. Nobody had Bitcoin for sale at any price on this exchange.
[03:21] So I did an experiment. I moved some Bitcoin from my wallet onto this exchange and posted a small amount to sell. And not at $2,000 where it was trading everywhere. I put it at $2,500 and it sold instantly.
[03:35] My Bitcoin vanished and $250 appeared in my account. Instead of the $200 it should have been worth. So I kept selling it at a higher and higher price. No matter what price I set, it automatically sold because there were so many market buy orders lined up to execute.
[03:51] And they would pay any price available. And at that moment, my limit sell orders were the only orders on the sell side. I was the market and had it cornered by accident. And almost as a joke, I posted one Bitcoin for $10,000 and it sold immediately.
[04:08] I emailed a friend at 2am to explain. My hands shaking. Once I ran out of Bitcoin, I rushed to buy more Bitcoin on a major exchange and try to repeat this gold mine. I waited nervously for the 15 minute transfer it took my new Bitcoin to arrive.
[04:22] But by the time it arrived, everything had changed. Other sellers had discovered this exchange and filled the sell side jars with reasonable prices. And what was strange is when I tried to post a better sell order,
[04:34] almost instantly a new order appeared undercutting me by one cent. I tried again and it jumped in front of me. Competition had arrived. These were automated market makers who constantly post limit orders near the current price.
[04:49] That's when it started to hit me. My last opportunity was the average person's gain. The more market makers competing in an exchange, the less slippage there is, meaning when you place a market order, the price barely moves and you get what we would expect.
[05:03] This perfectly mirrors the block order problem from the stock market in the 1980s. When a pension fund wanted to buy, say, $50 million worth of IBM stock, that massive order would move the price up to 8% because there wasn't enough depth to absorb it smoothly.
[05:22] Market makers solved this by constantly posting orders near the price level, reducing slippage from several percent down to just a fraction of a percent, even for large trades. Just like I discovered how traders compete to find fair prices, there's another market where competition really matters, the news.
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[06:32] But how did these automatic trading algorithms work exactly? I figured it out when I started noticing something else. As more exchanges popped up the same coin was trading at wildly different prices across different platforms It seemed like you could just buy it in one place and sell it in the other to make a risk profit of So it began I wrote to a programmer friend The crypto market is special very special We have big money billion traded
[06:58] a day in a market with almost no big players from the trading sector. It's all hackers and geeks. The prices in a panet can be 20% different between exchanges. I realized you could trade profitable loops through multiple currencies. You'd start with US dollars, buy
[07:14] Bitcoin, trade from Bitcoin to Ethereum, and then Ethereum back to US dollars, and if those exchange rates didn't multiply to exactly one, you'd make a profit. So my thought was, let's build a bot to scan all possible trading paths across all the exchanges. Our bot would
[07:31] search for circular routes where when you multiplied all the exchange rates together, you got a number higher than one. And these profitable paths existed because different buy-sell demand on various exchanges would cause the prices to drift slightly out of sync,
[07:45] as we saw before. And so on the first day of trading, the bot found a path between two exchanges, and trading $200 through this loop netted us an $8.50 profit in a second.
[07:58] For a short while, we had our own money-making machine constantly scanning for prices that didn't make mathematical sense. And when we traded a pass, the opportunity would disappear. And like before, the easy money didn't last.
[08:10] As pass with large profit percentages were discovered by others, they would beat us to it, leaving us with pass that only had a tiny profit margin. Professional firms with steeper pockets and faster algorithms arrived.
[08:23] Again, competition actually made the markets fairer for the average person. They were providing a service of information sharing, connecting previously isolated exchanges and assuring that when news broke affecting Bitcoin's value in one place,
[08:37] it would instantly spread to every exchange on Earth. What I stumbled into with a few cryptocurrencies happens today across the entire global financial system. Paul is checking Chicago prices at the same time he's trading in New York.
[08:49] Every bit of information can give him a critical advantage. Five for you, and now you're better right now. This leads to a powerful idea. We can do all of these trading opportunities called arbitrage.
[09:01] no matter the strategy, has a geometric relationship. Consider the price of Bitcoin on two exchanges, A and B. If Bitcoin costs the same on both exchanges,
[09:13] we're on this diagonal line where prices are equal. No arbitrage opportunity exists. Think of this line as a no-profit purchase. But if exchange B shows a higher price, we move off this line.
[09:26] That's a profit opportunity. Buy low and then sell high. Every arbitrage trade pushes the current market point back towards the no-profit surface. And as you add more currencies and exchanges, like my bot, you can see this in higher dimensions using planes and other shapes.
[09:44] Anytime the current market prices drift off these surfaces, there's profit to be made. Traders get paid to push the market point back to where it mathematically must be. But once you eliminate most of these mathematical opportunities, where do traders look for more profit?
[10:00] That's where things get really interesting. The next type considers where prices should be in the future. Think of this as future arbitrage. This kind of trading is statistical in nature, since the future is never certain, even if it's likely,
[10:14] where we move from these sharp no-profit surfaces to fuzzy ones. And predicting prices can happen over any time period, whether decades, days, or microseconds.
[10:26] This brings us back to the master, Warren Buffett. He credits everything he knows to Ben Graham, who in 1949 wrote The Intelligent Investor, the book on every investor's bookshelf.
[10:38] Graham taught to do research to find a company's true value, focus on earnings, growth, future profit potential. If that future seems likely and the current price doesn't reflect it, then you buy now and wait for it to unfold.
[10:51] But after you buy, you need to ignore the daily stock movements. Graham compared this to a crazy person he called Mr. Market. Mr. Market knocks on your door every single day with a new price for your shares.
[11:05] Sometimes he's wildly excited about what you have to offer. Sometimes he's deeply depressed. As Graham put it, often Mr. Market lets his enthusiasm or fear to run away with him, and the value he proposes seems to you a little short of silly.
[11:22] So a perfect example of this process was Coca-Cola in the 80s. While most investors saw Coca-Cola as a mature American soda company, Buffett saw a wildfire about to ignite globally.
[11:35] The spark was already visible. Americans consumed about 300 Cokes per year. But in China it was three, India less than one. But these numbers were starting to climb rapidly as those markets opened up.
[11:48] Buffett understood Coke was not just selling sugar water. It was selling an American lifestyle. Buffett loved the CEO with an ambitious plan to make coke available everywhere on earth to make it the world's dream.
[12:01] And so his research made this an obvious bet.
[12:16] By 1994, he put 35% of their fund, $1.3 billion, into the soda company. Wall Street thought it was crazy. Mr. Market would not have agreed. By 1998, reality caught up with his predictions.
[12:30] Coke sales exploded globally, and his investment had grown to $20 billion, his most profitable investment ever. But here's the key point.
[12:42] This kind of long-term predictive trading only works because of the traders making trades based on much shorter-term predictions. Those who inject information second by second.
[12:54] These are exactly the traders Graham warned Buffett to ignore. But what that point misses is how crucial short-term traders are. Without them, Buffett's trades wouldn't work. Which brings us to one of the greatest living traders, Mr. Market himself, Paul Tudor Jones.
[13:12] I'm in 2017. I'm in 2017. Yeah. I'm using a card. Hey, hey, listen, Vinnie, get off that. Sell 540 market. Sell 540. 540 market. Sell 540. This is the kind of trading where rubber really needs to roll.
[13:26] It where new information actually gets injected into the market prices as traders react moment by moment to the world It more chaotic because it reflecting reality itself And that what makes it so exciting Every day is something new and every day is providing new information every day every hour
[13:44] OPEC has basically come to an agreement whereby they're going to have a 7% reduction cut, and my whole feeling is that any time that you try to get 13 people to agree to anything
[13:57] and I have a chance to take a position on it, well, they're going to have to, you know, wild horses can beat me from betting against them succeeding in there. When economic data comes out, when earnings surprise,
[14:09] when geopolitical events happen... You've heard nothing else on OPAF? No. I got you. Thank you. If prices didn't make sense, Paul Tudor Jones was there within seconds injecting that new information into prices.
[14:23] Give me a number. Tell me more about it. Do it, do it, do it, there's more to it. Do not ever leave this phone. You get someone, get the other broker to come and run the orders in. Do not leave this phone.
[14:36] And so at every moment, there's always new information waiting to be injected into prices. That's right, that's right. They have an agreement between 13 people, but two of them said they want to trade, which happens to be the only time they've produced in the world.
[14:49] That was such a big problem. And the market doesn't care who does the research, whether you're managing billions or trading from your bedroom. A perfect example was the recent Pope selection,
[15:03] where a trader, Gomer, was trading on prediction markets like Kalshi. And because of the secrecy and closed-door nature of this selection, he saw that betting markets had two clear favorites that made sense,
[15:16] but he felt the pricing was mostly nonsense. And so when the white smoke appeared after just four ballots signaling they made a decision, the favorites on the betting market spiked to 85%, but Domer had spotted a key bias.
[15:30] There was a widely held belief that Americans could not be elected Pope, and he found Robert Prevost at 200 to 1 odds, which didn't make any sense to him, so he bought it.
[15:45] And minutes later, that American walked out as Pope. Stunning, it is an American Montina. Again, a great, great surprise. The market paid him handsomely for bringing research to an information-starved market.
[15:59] Anyone can be the bee. Which leads us to the final evolution, computerized future arbitrage, where machines operate in fractions of a second across all stocks,
[16:11] looking for patterns in how groups of stocks move together. The correlations here from now, that's incredible. Gary Bomberger at Morgan Stanley pioneered the idea in the 80s with what's now called
[16:25] statistical arbitrage. He focused on companies that were nearly identical, say Coke and Pepsi. They naturally moved together since they served the same market with similar products.
[16:37] And instead of a sharp mathematical line we saw with currency arbitrage, this created a fuzzy corridor where these pairs usually traded together. And when something disrupted that relationship, like when Coke launched New Coke in 1985 and consumers revolted, crashing Coke's
[16:53] price while Pepsi celebrated, Fomberger's strategy would kick in. His algorithm would buy the beaten down Coke and sell the celebrating Pepsi, betting their relationship would normalize back to the
[17:05] usual region. This reveals something important about how information flows through markets. When Coke posts strong sales and jumps 10%, statistical arbitrageurs immediately ask,
[17:17] If Coke's worth more, why shouldn't Pepsi be worth more too? They're like messengers, making sure relevant information doesn't get trapped in one stock. They spread it to every company that should be affected.
[17:29] D.E. Shaw took this idea and scaled it dramatically across thousands of stocks, and they traded all of them at the same time. And soon computers were detecting far more complex relationships. For example, all bank stocks tend to move together,
[17:42] and this looks like a fuzzy tube through their price space. And it goes much deeper. For example, when Coca-Cola announces strong earnings, the news cascades through Pepsi, but then other beverage companies,
[17:55] and then restaurants, sugar suppliers. Algorithms learn to map these real-world connections as curved surfaces, which showed how entire groups of stocks naturally move together.
[18:07] They're all interrelated. The whole world is simply nothing but a big flowchart for capital. Which leads us to the most successful trader in history, Jim Simons and his legendary, secretive, medallion fund, the Envy of the World.
[18:22] Simons came to finance indirectly. His mathematical thesis was on measuring geometric distortions, detecting when points deviated from where they should be on complex surfaces.
[18:34] The boundary is a surface or a higher dimensional surface. I showed it to Chern and he said, well, you did it in three dimensions, but it should be doable in all dimensions.
[18:49] Okay. Years later, he realized markets were just another geometric system. Prices tended to follow shapes based on real world constraints. When prices moved off their correct surface or region,
[19:03] he would calculate the exact trades needed to profit from their inevitable shift back. So Renaissance took a radically different approach from D.E. Shaw and others. Is it true that you never hired people from the investment industry?
[19:17] No. No. Well, I like to say that you can teach a physicist finance,
[19:30] but you can't teach a finance person physics. Simon took a purely mathematical approach, pattern detection without any assumptions. Well, I mean, think of a stock.
[19:43] It's a time series. you know, it has a value every day, it goes up, it goes down, it has a time series. You can analyze time series mathematically, and especially if you have hundreds or thousands of these time series.
[20:04] And in looking at the patterns of prices, I could see that there was something we could study here, that there may be some ways to predict prices, and the models got better and better,
[20:16] and finally the models replaced the fundamental stuff. They used machine learning to ingest everything Prices news weather economic data satellite imagery other sources we don know about and their computers discovered hundreds of complex surfaces that prices gravitated towards
[20:33] So a trade in this world would take the form of a computer outputting what to hold across all stocks, thousands of precise portfolio instructions, each representing a tiny statistical edge based on where prices were expected to move in the short term.
[20:49] He gave a simple example where, in New York, the weather would predict whether the markets would open slightly up or down. Often these relationships would only exist briefly before disappearing.
[21:02] Relationships so subtle that even Simon's team couldn't explain why they worked. The beauty was no one else was trading these services because they couldn't see them. This was the ultimate computerized beast, rapidly incorporating information from every
[21:16] corner of the world and spreading it through market prices. And so all traders, from the most advanced to people making wild bets in their bedrooms, they all push prices towards
[21:28] what's there at any moment. You can see it as a living hive. And today the information war has reached the seat of light. Orders are executed in microseconds and trading algorithms react in real time to the world. A great example is in 2013, someone hacked the AP news
[21:46] account and a single fake tweet about an explosion in the White House moved the entire market 136 billion in seconds, which then snapped back when algorithms realized it was fake.
[21:58] All right, this is going to say everything. P&G is now down 25%. Oh, that's true. I was about to say you just found five. Okay, I'm sorry. I'm sorry. It's a fast market. It's a fast market. The market was around 900 points and now down 60.
[22:11] The arms race for information has gone upstream. Hedge funds buy satellite imagery to count cars in Walmart parking lots before earnings. Citadel pays Robinhood for order flow to see trades before they hit the market.
[22:26] And so finally, let's step back and remember Warren Buffett's original bet. He wagered that doing nothing would beat all this sophisticated trading. How is that possibly true? In the 1960s, a young economist named William Sharpe was studying portfolio theory when he had a revolutionary insight.
[22:43] His one big idea was to look at all traders collectively as one entity. First, realize that every investor is essentially voting with their money on what the market
[22:55] should look like by putting their dollars into some combination of stocks, whether it's all in one stock or spread across hundreds. And so if you pause time at any moment and add up what everyone has their money in, you
[23:08] actually get a single collective portfolio which scarf called the market portfolio and if you simply buy this market portfolio you capture something remarkable the collective results of everyone's wisdom and so while traders
[23:22] pay to constantly change or reallocate their portfolio deciding what to own and how much to own so the market portfolio holder it's just different slices of the pie growing and shrinking as they exchange value with each other market
[23:36] portfolio holders get this ongoing reallocation for free. It's like a living organism, always evolving to perfectly match the state of the world at each moment. And Sharp's big insight is the
[23:48] market portfolio is the average of everyone's holdings, so it must deliver average performance. But since it pays no fees and everyone else does for every trade, it automatically beats the average
[24:01] after cost and gets better and better over time because costs feed away a trader's profits. And John Bogle turned this insight into the index fund in 1976. A way for anyone to own the whole
[24:16] market by holding all 500 stocks in the S&P 500 in proportion to their market value. And so your holdings get automatically updated every time new information gets injected to the market. The results were stunning. Since 1976, Bogle's original index fund has beat roughly 90% of funds.
[24:36] Jack Bogle has probably done more for the American investor than any man in the country. Jack, could you stand up?
[24:49] It wouldn't have happened without him. The truth is it was not in the interest of the investment industry, of Wall Street. It was not in their interest, actually, to have the development of the index fund
[25:02] because it brought down fees dramatically. And this is why Warren Buffett could make that bet against his own life's work. He understood every time you make a trade, you have a chance of being wrong.
[25:15] And active investors have to keep guessing. They're like running a marathon and might fall when they're wrong. I've personally gone bankrupt. I lost everything I had and had to borrow some friends three times in nine years that I've been in the business.
[25:31] Where I go sit in the park and just sit there and just go, I can't believe this. You know, this is the end. This is the end of my life. The index fund never falls. It just keeps creeping forward in the race.
[25:43] And since fees don't slow it down, it gradually drifts towards the front of the race. And so once you invest in the index, you literally have every trader on earth working for your money 24-7.
[25:56] That's why Buffett's bet continues to win. It's the most important idea in finance. Imagine yourself back on March 11th of 1942. At that time, you had invested $10,000.
[26:10] In effect, bought the S&P 500. I want you to think how much money you might have now. And now that you've got a number in your head, let's go to the next slide and we'll get the answer.
[26:22] You'd have $51 million and you wouldn't have had to do anything. You wouldn't have to understand accounting. You basically just had to make one investment decision in your life.
[26:34] It turns out the secret to winning in the most sophisticated game on Earth is refusing to play. If you follow that, of course there's one problem, buddy. You're a friendly stockbroker with a star to death.
[26:48] Thank you so much for watching this video. If you would like to stay up to date on new videos and projects I have coming, please join my email list. I have something coming up I think you'll be interested in.
[27:04] Thank you.