Quant: The Old New Thing

By Michal Dziegielewski, Director of Investment Research, FQS Capital Partners.

Nothing makes for better fodder for investing hype than a story seemingly ripped out of science fiction movies, such as the inevitable “rise of the machines” and the looming extinction of the most ill-fitted dinosaur of the current age: the human investor. As quantitative methods and technologies disrupt a whole host of industries, many wonder what the investment world will look like in 5 or 10 years. Will the quants take over? Perhaps not, but here’s the thing – today’s quant disruption is really part of a much bigger story, not just happening over the last 5 or 10 years, but since the dawn of modern markets, and there’s no sign it’s going to abate.

The advances we have seen over the last few years are impressive, and the ambitions of the next generation of quants applying machine learning and artificial intelligence are nothing short of incredible. But what has really changed about investing? Early 20th century London traders sat with telescopes on tall buildings and monitored output from outlying factory smokestacks; today they look at satellite images and parking lot data. In the 1920’s classic Reminiscences of a Stock Operator, Edwin Lefevre outlines a simple system of buying stocks for short term gains when their momentum takes them through key round number levels (like $50, $100, $200, etc.) Today investors can access risk premium funds trading momentum and breakout systems that accomplish much the same thing, and anyone who bought bitcoin when it first passed $10,000 would have been overjoyed, for a short while anyway. In the 1930’s, the father of value investing himself, Benjamin Graham, outlined a system of buying stocks whenever they trade at a material discount to their net current assets; now, investors can easily access smart beta strategies built around various value indicators that have been shown to produce positive returns over time. The underpinning ideas for many of these models are long-lived aspects of human nature, built around biases such as anchoring and the fundamental drivers of greed and fear.

The advent of quant as we know it today began with the computer and early traders who began using computer models to guide their trading. As detailed in his autobiography A Man for All Markets, Ed Thorp launched possibly the first quant fund in 1969, later dubbed Princeton-Newport Partners, which initially focused on applying models to convertible bonds and options. Early quants such as Ed Thorp and John Henry, who traded a trend-following system, typically fed data manually into computers, which limited speed. The next evolution was in the early-80’s when people like Thomas Peterffy, who would later start Interactive Brokers, had to literally hack into their Quotron machine, cutting the data feed and wiring it into their own computer, just to receive real-time prices they could trade on. Of course, in doing so, these traders also cut out the middle man. Less than 20 years ago “stock specialist” firms with human operators still adjusted bids and offers on every stock on the exchange. In a pattern we have seen again and again since then, computers first displaced the repetitive, low value operations of the market, and then moved up the chain. Even today, index strategies (themselves a form of quant investing), smart beta, and risk premium strategies are still steadily displacing a large part of the actively managed universe.

One of the largest misconceptions about quant investing is that investors tend to view it as a single defined strategy. Quant is not a strategy, it’s a methodology, and the fundamental methodology underpinning all good analysis, quantitative or qualitative, is the same – the scientific method. The difference between thoughtful data-driven discretionary investors and that of thoughtful quantitative investors is not necessarily their approach, but rather the computational power that can be brought to bear on certain aspects of the investment process. As more data become available, it is not surprising to see the importance of quant and real-time analysis of company fundamentals play a greater role in investing. But this doesn’t mean that alpha will disappear, just that the methods of extracting it will change, and the investors that embrace the new paradigm are likely to benefit. To quote the advice from the early model robo-advisor featured in the classic film T2: Judgment Day, “Come with me if you want to live!”

Michal is a senior member of the FQS research team and a member of the Investment Committee. Prior to joining FQS, Michal was the Strategy Head for Global Macro, Fixed Income and Credit Strategies at FIM. Before moving into that role, he was the Strategy Head for US Equity Long/ Short and prior to working at FIM, he was an Investment Analyst at Ivy Asset Management.

FQS Capital Partners is an alternative investment specialist with an evidence-based culture of continuous improvement. Led by Dr. Robert J. Frey, a former Managing Director of Renaissance Technologies, the firm seeks to exploit its unique quantitative heritage to research, develop and utilize innovative tools and techniques to better understand, rank and optimize hedge fund risks and returns. These quantitative approaches are blended with qualitative analysis, underpinned with rigorous operational due diligence, to service their investors’ long-term objectives with client focused solutions.