Jim Simons, and Renaissance Technologies, has one of the best track records ever. It’s also a mystery. Greg Zuckerman’s latest book, The Man Who Solved the Market, helps pull back the curtain — a little bit — to see exactly what drives their returns.
Unfortunately, the lessons from the book are not in the strategy itself. It’s not something anyone can simply replicate.
Instead, the book does a great job explaining the history of the firm, the people, and the massive amount of effort and complexity that went into their success.
If anything, these were the lessons that stood out.
1. Jim Simons didn’t build the trading model. He built the team that built the trading model. A few major players stand out in the book because of their breakthroughs. But it’s a collaborative effort based on Simons’ early experience code-breaking for the IDA.
2. The goal was to build an automated trading model. But that’s not how things started. The technology wasn’t advanced enough to fully automate anything early on. Trades still had to be phoned in. Human interference was a repeated risk. It eventually caught up to their needs.
In the early days, the model was a mix of advanced technical trading and gut instinct. It took a while before they fully automated the model.
3. Brilliance does not equate to better behavior. These were some of the most brilliant math and science minds around. When the fund was making money, things were great. Every day was a party. That created a problem. Because their wildly successful trading model rarely experienced severe losses, panic set in for many members of the team when losses arose.
In the early days, when gut and intuition played a bigger role in trading decisions, fear and greed were obvious. Building a trading model was supposed to eliminate the influence of emotion. It almost worked.
Even fully automated, Simons stepped in several times to override the model. The first time was following the Dot-com bust. The second time was during the “Quant Quake” in 2007, with the fund down 20%. His reason was survival. Was it the right decision? It’s hard to know how things would have turned out had they just trusted the model.
4. Trading models aren’t immune to losses. Despite being arguably the most sophisticated trading model ever, it wasn’t perfect. The fund’s annual returns were phenomenal. 39% annualized returns. Only one year ended in a loss.
Still, there were a few bad days.
Leverage played a role in the huge gains. But it also worked against them during the two episodes mentioned earlier where Simons overrode the model. It compounded the losses until panic drove Simons to intercede.
3. Timing is everything. When you look back on the success of certain investors, it’s often a “right place, right time” set of circumstances. For Simons and the team, it was the rise of computers.
The computing power needed to run the model wasn’t available from the start, but it eventually caught up with their needs. By the early 2000s, their “computer room” was the size of several tennis courts.
4. Data, data, data. The focus on data was groundbreaking for its time. Simons collected all the historical data he could get his hands on. He started with books, magnetic tape from commodity exchanges, interest rate data from the Federal Reserve, and even manually recording daily closing prices for currencies. Later, they added opening, closing, and intraday price moves. One person became dedicated to the collection and cleaning of data.
The goal was to search for patterns in the data that gave them a slight edge in making money over time — not unlike a casino. When that wasn’t enough, they literally tested the financial data against everything else — sunspots, lunar phases, weather data, public sentiment — in the search for patterns.
One passage from the book relays why data was so important to their effort:
Renaissance staffers deduced that there is even more that influences investments, including forces not readily apparent or sometimes even logical. By analyzing and estimating hundreds of financial metrics, social media feeds, barometers of online traffic, and pretty much anything that can be quantified and tested, they uncovered new factors, some borderline impossible for most to appreciate.
“The inefficiencies are so complex they are, in a sense, hidden in the markets in code,” a staffer says. “RenTec decrypts them. We find them across time, across risk factors, across sectors and industries.”
Even more important: Renaissance concluded that there are reliable mathematical relationships between all these forces. Applying data science, the researchers achieved a better sense of when various factors were relevant, how they interrelated, and the frequency with which they influenced shares. They also tested and teased out subtle, nuanced mathematical relationships between various shares — what staffers call multidimensional anomalies — that other investors were oblivious to or didn’t fully understand.
The amount of data they collected almost from the start is mind-boggling even when you compare it to what the average investor can easily access today. Of course, it pales in comparison to the amount they use today. It’s the fuel that feeds the model that drives thousands of daily trades.