Jim Simons: How To Achieve a 66% Return Per Year (7 Strategies)

what if I told you that there was an investor who achieved much higher returns than Warren Buffett Ray dalio Peter Lynch George Soros Charlie Munger and essentially all of the other great investors that you can think of not only did he beat them his returns blew theirs out of the water so if we look at this Warren Buffett's annual average return is 20.1 percent dalio's 13 Lynch 29.2 percent Soros 20 Charlie among a 19.8 percent Jim Simmons 66 average return out of all of the investors that I've studied no one has got a yield anywhere near close to his and this is not just over one or two years this is over the past 31 years of consistently just destroying the market to put that into perspective at how impressive that is if you invested a hundred dollars and were able to achieve a 66 return every year after 31 years that one hundred dollars would be worth 665 million 810 000 to regularly achieve something that high is unheard of until Jim Simmons came along but how did he do it Jim Simmons relies on quantitative analysis to decide the trades he makes he's a numbers man he was Math Genius growing up getting a bachelor's and PhD in mathematics when he was young just for some reason math turned him on now the old excuse is when do you ever use math in the real world well Jim Simmons was about to answer this question in a big way he used his math skill to become Uber Rich originally he used it to break codes with the National Security Agency as well as teaching at MIT although this paid good on a relative scale this is not generally the profession you'd go for to amass a fortune he then made the decision that he wants to step into finance and trading where the real money is now naturally Simmons keeps his trading Secrets behind closed doors as much as possible of course if you had a fund earning a 66 return every year making you a multi-billionaire you probably would and tell your secrets either however there is quite a bit that has gotten through the cracks on his trading strategies so in this video we're going to go over seven of his key strategies that Simmons used to trade in the market back in 1982 quantitative analysis was not a thing the models that we see Traders use today no one had thought of so people generally relied a lot on perception and homemade systems to trade for the first two years Simmons did just this he used ordinary fundamental and Technical approaches to trade in the market a lot of it was based around intuition and instinct no models for the first two years like normal people do it was a similar approach to the way everyday regular Traders trade in the market today except unlike most Jim actually made money because one he was a math genius and two because back in those days there were so many more opportunities less competition less computers trying to Arbitrage the market so he was able to make a decent amount of money this way but at least According to Jim he felt that it was mainly luck and also emotionally draining not knowing if you're going to be making profit the next day or not heart we were extremely successful I think it was just plain good luck but nonetheless we were very successful but this was a very gut-wrenching business you know you come in one morning you think you're a genius and the next morning you come and you feel like a jerk of the Market's against you he just liked the emotional side of trading and he wanted to find a way where you could almost guarantee that money would be made kind of like at a casino the house is always going to profit because the odds are rigged in their favor they don't get emotional they just want you to keep playing Simmons aspired to create a model like this but for trading in the stock market where if you just keep trading you're guaranteed to win because the odds are in your favor in order to do this Simmons needed a lot of data for quantitative analyst data is obviously key and he would gather information on everything whether annual reports monthly quarterly reports uh the historic data itself volumes you name it whatever there is we take in terabytes of data a day so his team would gather all of this data up backtrack it across history and search for anomalies where is this something consistently odd happening that I can profit from and the anomaly might be something like every time the date is leading into Christmas the stock would increase in price most investors would try explaining the reasons why Simmons did not care why he just cared that the data showed it consistently happened and once he found the anomaly he simply would buy the stock leading into Christmas and sell after Christmas each time making a profit that is a simple example of an anomaly trading but sermons would do this across thousands of data sets and search where the abnormalities were that he could benefit from [Music] secondly he and his team would look for Trends this was an approach that worked very well in the early days of trading the best place to go find trans at least back in the day was in the commodity Market copper gold silver oil corn wheat these things would often follow a pattern so here's a typical example of the price data of a commodity for a year we'll say it's wheat Simmons would zoom in on a smaller time frame say 20 days and he would notice that the commodity is trending maybe it's trending upwards because less farmers are able to sell during this time or trending downwards because of oversupply as we said before Simmons doesn't care on the reasons why all he cares about is that it is happening and how he can profit so if it was trending upwards he would trade it and buy and it was downwards he could short it and make some profit on the way down the system worked very well for him early on even though it sounds quite easy but after a while people started to catch on with this method and it became more and more obsolete years ago such a system would work not beautifully but it would work so you'd make money you'd lose money you'd make money but so you would see it was a very vestigial system Simmons would need to upgrade a strategy if he wanted to keep making money in the market or risk going fast in the book The Man Who solved the markets Gregory Zuckerman stated that Medallion which is Simmons Flagship fund that averages the 66 return earned profits from trending and reversion predicting signals especially a one called Deja Vu reversion in terms of trading is when a price of a particular stock will revert back to the average or it will revert back to a particular metric sometimes it will fall below the average sometimes above but the job of Simmons team would be to profit from these fluctuations for example you would place a stock like apple in the algorithmic model then it would look at a bunch of different data points maybe Revenue Book value PEG ratio and the price the tangible Book value and next it would start to look for patterns where would it see the same things happen over and over again where would you get that feel of deja vu and maybe the model would spot their overtime Apple generally has a price the tangible Book value of 43 sometimes it will dip below sometimes above but the pattern would always reverse back to 43. again Simmons would see this he wouldn't care about the reasons why he would just know that it happens and he would take advantage of this pattern his firm would simply make a trade when the price goes below 43 it reverts back to 43 and he makes some profit if it goes above 43 he could short the stock and they would just keep doing this until the pattern would change that's a basic example of reversion predicting signals and maybe in the early days it was as simple as just a few data points that you can profit from but as more and more Traders started to click onto these techniques Simmons would develop more complicated models to profit in the market instead of just simple pairs he created a fund that involved complex signals and Equity trades it will go through all data it could find on a particular stock correlates multiple variables and then use machine learning and their models to find irregularities where they can make profits in order to create models that could take in such vast and complex amounts of data then find patterns and then profit Jim would need to find a team of very smart people like the Avengers but instead of helping the planet they would help solve complex trading algorithms people with phds Doctorate Degrees top one percent of IQs people who could code instead of going for people on Wall Street and with Finance backgrounds Jim looked to universities he started hiring people with Doctorate Degrees in math and physics astronomers and statisticians people who could solve complicated problems they didn't necessarily have to have a finance background in fact he didn't want this Simmons said the important thing was that they were very smart I like to say that you can teach a physicist Finance but you can't teach a finance person physics and this great team of what can best be described as nerds would begin to crack the codes for trading in the stock market and the smart thing about Simmons strategy was that he didn't hire these people as employees he hired them as partners the only way that you can be an owner in The Medallion fund is to be employed there so this way all of these very smart workers are incentivized to make as much money as possible because the money would not just go to a company but to themselves as well so you have hundreds of these PhD level Geniuses all with one incentive to make as much money in the market as possible this was a recipe for success during the mid-1980s a top researcher of Simmons team began identifying ghost patterns in market prices so the project was based on similar code cracking that Jim and his team had worked on at the National Security Agency the main idea was to look for patterns that others failed to detect and then profit from Earth The Medallion fund would feature a collection of a minimum of 8 000 signals based on the short-term patterns of the entire batch of scientists who work for Medallion the fund then leveraged these signals AKA they borrowed money to trade in the market countless times per day Reports say that they borrowed around 17 for every one dollar that they invested and this was a bit of a secret to their success the fact that they didn't even have to use their own money they could borrow the money quickly make some trades and profit and then return the borrowed amount back or just do the same thing again and again the code chief executive of Simmons company said on an unlevered basis AKA not borrowing our models produce modest returns with very low volatility borrowing money helped change these returns into something that wasn't modest at all and basically put the company's Returns on steroids the beauty of hiring PhD nerds over regular investors is that they would have one focus on the numbers astronomers physicists mathematicians they don't worry too much on the emotions of the market what investors are saying the strength of the brand or anything like that no to them it's kind of like a science experiment or putting together an equation the only thing that matters is the numbers and making sure that they work so instead of solving problems on how to get to space or the force needed in a rocket or solving a physics calculation they would solve trading algorithms just like a science experiment you just need to keep changing the variables does this one work does this one make profit no let's try another does this one work no let's try another does this one no let's try another and they'll just keep fiddling with the equation and the variables until they find something that makes them a profit by their very nature they were perfectly suited for this type of work as Jim Simmons was growing his fund so too was the rise of machine learning so machine learning is the ability of a machine to use information to improve performance on some set of tasks whether that task be email filtering speech recognition chess or finding profitable trades in the stock market just like with chess the machine can analyze thousands of different moves and the consequences of making each move it can predict if a move is beneficial for the player or if it's going to make the position worse and it's the same thing with a stock machines can test thousands of different moves and see which one leads to making money it's what's called machine learning so you find things that are predictive you might guess oh search and such should be predictive might be predictive and you test it out in the computer and maybe it isn't maybe it isn't the beauty about machines is they can test almost most infinite amount of data and theories in the time it takes for a human to test one thing the machine can do a million tests and Simmons realized this very early on so he Incorporated machines into his trading strategy which gave him a huge advantage over his competition so those are seven strategies that Simmons used to become what he is known as today the greatest investor on Wall Street and also the most successful hedge fund manager of all time so if you're currently a PhD major or just a high IQ individual the next move might not be to become a professor teacher engineer maybe you can also get into trading and become the next Jim Simmons but if you do be sure to learn from these seven strategies

Channel: Cooper Academy

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Keywords: jim simons, jim simons how to pick stocks, jim simons investing, how to invest, how to trade in the stock market, how to invest in the stock market, strategies for investing in stocks, jim simons stocks, jim simons stock market, jim simons trading strategy, jim simons trading, jim simons the man who solved the market, jim simons math, jim simons trader, jim simons documentary, jim simons investor

Id: cm7kkHtZiJA

Length: 15min 27sec (927 seconds)

Published: Thu Nov 17 2022