When it comes to trading and investing in the market, it seems as though people always give the same advice — “invest in what you know” or “look at how specific companies perform”. This has been the only real way to invest for a very long time. Using methods that rely on traditional finance, economics, and business-know-how has been the cornerstone of investing for as long as the market has existed.
But what if there was a more sophisticated, complex method that requires almost no financial knowledge at all? This is the method pioneered by Jim Simons, whose story was just brought to light in the biography The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution by Gregory Zuckerman. Simons’s net worth is $23.5 billion as of 2020, and his company’s hedge fund, Medallion, has generated annual returns of 66% since 1988, leagues above anyone in the investment world.
As a child, Simons was an absentminded thinker, always lost in thought. When he mentioned to his boss at work one Christmas that he wanted to study mathematics at MIT, he was laughed at. Simons went on to prove him wrong, earning his PhD from MIT in 1958, and made a name for himself as a mathematician throughout the next two decades. Through being a code breaker during the Cold War and then publishing a groundbreaking paper on pattern-recognition, Simons unknowingly had built the foundation he would need to lead the quantitative revolution in trading. Simons always liked investing and making money, but it wouldn’t be until 1978 that Simons began to take trading seriously.
Simon’s company, Renaissance Technologies, utilized an early form of machine learning to trade stocks. By combining trading signals from multiple sources, Renaissance was able to develop an adaptive system. This means that the system was capable of learning and adjusting its trades on its own, which was revolutionary for its time. Their unique process consisted of three steps.
First, they would identify patterns in historical pricing data. Secondly, they would ensure the patterns were nonrandom and significant. Finally, they figure out the reason behind the patterns and adjust models based on this analysis. It is important to point out that this is one of the many ways to go about quantitative trading, and many funds within the firm relied on several other methods, with this method being a cornerstone of the business. Simons also structured his company in a unique way. Everyone he employed had full access to each line of source code, allowing anyone to make experimental modifications that could improve the system. Simon’s team consisted of seasoned mathematics PhDs who were able to bounce ideas off each other easily, leading to the firm’s success. Medallion had made net profits of $5.75 billion by 2010 based on their unique method.
Quantitative Trading Today
Today, quantitative trading has been embraced by almost every large firm. There is more data than ever before, and firms utilize this data to improve their models, as more quantity leads to better quality. They’ve used alternative data, which includes data that at first glance is unrelated to stock trends, like traffic data or recommendations by social media influencers. Utilizing data science to formulate new methods, investment firms are able to predict trends better than ever before, making the market much more efficient and stable than in the past. If a stock price drops, the time for a firm to recognize that and buy it has become almost instantaneous. One can only imagine how efficient these trading methods will become in the future, and we have Jim Simons to thank for it.
About the author
I write about quantitative and behavioral economics, as well as finance. I am a rising junior at Cal Poly SLO.
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