https://www.ft.com/content/83f4e562-65be-11e7-9a66-93fb352ba1fe A host of emerging investment managers imbued with a solid grounding in science and algorithms represent the cutting edge of disruption on Wall Street. “We’re the new generation,” says Shalin Madan, chief investment officer of Bodhi Tree Asset Management, which he set up after 20 years of investing in hedge funds himself. After seeing how the industry was evolving, he said the choice was simple. “I had the choice between being the disrupter or the disrupted.” Quantitative hedge funds that use complex algorithms to sniff out and systematically take advantage of millions of small but lucrative trading opportunities across the world are proving increasingly popular, reflecting in part a long run of underwhelming performance and expensive fees across the broader industry. Now, the widening access to faster and cheaper computing power, coupled with an explosion of new digital data sources like satellite images, internet chatter and online commerce, has encouraged a new wave of scientists and programmers to turn their expertise towards financial markets. The challenges are profound. But with many big quant hedge funds also seeing returns moderate as their size swells, some investors are cautiously beginning to turn to these smaller, fledging funds rather than big, established players like DE Shaw, Renaissance and Two Sigma. A few years ago, most investor money was ploughed into a star “prop” trader leaving one of Wall Street’s blue-chip investment banks or an established portfolio manager splitting from his firm, while quant upstarts would struggle to raise money without a long record at a bank or asset manager. But these days, some investors are more willing to take a punt on emerging managers — especially if they have a solid grounding in science and the algorithmic investment world. “Our machines never sleep,” says Andrej Rusakov, one of the co-founders of Data Capital Management. “The future is systematic investment. There is no way it isn’t.” There were 179 new hedge funds launched in the first quarter of 2017, according to HFR. The data provider estimates that about 55-60 of them were quant funds. While HFR does not have historical data on this area, it says that “anecdotally” the number of quant hedge fund launches is clearly on the rise. Assets managed by so-called systematic hedge funds have doubled over the past decade and hit a record $500bn this year, according to recent Barclays research. Even this probably understates the popularity, as many traditional mutual funds are turning to quant techniques to improve returns. So-called “emerging” hedge fund managers often do better than their bigger peers. Research by Preqin, a data provider, shows that new hedge funds with assets of $300m or less have consistently enjoyed higher rolling 12-month returns than the broader industry since January 2012, and returned 14.1 per cent over the past year. They also boast a higher “Sharpe ratio”, a popular measure of returns when adjusted for the riskiness of their bets. While Preqin’s data are on new hedge funds overall rather than just quants, some investors are convinced that the future belongs to these players. Jeff Tarrant of Protégé Partners has launched a new $1bn vehicle to solely invest in start-up investment funds that use artificial techniques like machine learning. “Jeff Bezos picked off the bookstore business. Apple totally picked off the music business and Netflix totally changed television. Now [machine learning] is going to pick off the hedge funds,” he told the FT earlier this year. Many of these start-ups will clearly fail, but even some of the hedge fund industry’s biggest stars think this is where the wind is blowing. Paul Tudor Jones, a hedge fund pioneer, has invested in two new quant hedge funds, Numerai and CargoMetrics. Steven Cohen of Point72 has also invested in Quantopian, a novel “crowdsourced” quant hedge fund, and like Mr Jones is investing heavily in data scientists and programmers to help his main firm. Nonetheless, there are some significant hurdles. While data and technology costs have come down considerably, they are still pricey to shoulder without any institutional backing — which is still tough to get. Investors are turning more towards quant funds and are more open to investing in emerging ones than in the past, but many remain wary of what looks like a “black box” to non-experts. Moreover, the established quant powerhouses collectively employ thousands of PhD-level data scientists, mathematicians and programmers that are already scouring global markets for any tradeable patterns. Many aspiring quants who think they’ve found a lucrative pattern in markets are often disappointed when they actually begin trading and discover that the signal proves illusory, or fades quickly from view. “The ability to acquire data are much easier and cheaper than it was, and there is a lot of strong entrepreneurial talent coming up,” says Stephen Prince, the North American head at Tetragon, an investment group that invests in hedge funds. “Because of that, there are some compelling opportunities among a handful of new managers. But you have to keep your eyes open to the challenges.”