Carmot Capital designed an investment strategy that delivers top returns during times of market stress. Founded by George Sokoloff, PhD, CFA and Gerd Infanger, PhD in Silicon Valley in 2013, Carmot applies its experience and artificial intelligence models to manage a market neutral statistical arbitrage fund that profits from surprisingly predictable chaos.
The name Carmot comes from the alchemical process of turning base metal into gold. Carmot was believed to be a mythical element of the philosopher’s stone and is the name taken by one of few hedge funds who operate in the heart of Silicon Valley.
Carmot’s Tail Risk Plus strategy uses leading edge machine learning algorithms to generate returns during both high and low volatility periods. Unlike tail risk strategies, Carmot does not use derivatives to juice returns and delivers positive returns even in benign markets
“We are synthetically short liquidity, we are not long volatility,” says Sokoloff (pictured). “Volatility is nothing but a by-product of insufficient liquidity. When markets fail to absorb large institutional trade flows, we see more statistically significant price shocks. That works well for us. Our algorithms are tuned to detect bigger and more consistent price moves, and predict what large traders are doing with their portfolios with a high degree of accuracy. Our returns are demonstrably stronger when volatility is high but, crucially, when volatility is low we still see a part of these moves. We can still exploit that information to our advantage to make money.”
“We know that when humans panic they tend to panic together and they tend to panic in a predictable fashion. This happens among all investors, including even quantitative hedge funds when risk controls are breached and humans make decisions to cut exposure and liquidate positions.
“These are the market regimes in which we thrive; being able to predict the movement of stocks during chaotic regimes.”
Carmot’s algorithm looks for tell tale signs of large and broad portfolios being pushed through the market on a daily basis by large institutional traders. These investors usually go through a certain lifecycle of trading; they try to limit the impact on the market and increase their aggressiveness to push the trades through over time.
“This is where we come in. When institutions push multiple stocks into the market at the same time, they are forced to create specific patterns in stocks that are either being bought or sold. Our goal is to decipher the components of their trade lists and the direction these groups of stocks are going to go in future,” explains Sokoloff.
Indeed, this is a problem that machine learning algorithms are good at solving; seeking out commonalities amongst elements behaving chaotically and determining which direction these will likely go in future based on how they have moved in the past. Whether the past is a few months or a few minutes.
“Volatility is making a comeback and we are happy about that. Every time there’s been a volatility spike the strategy has done well,” says Sokoloff.
Carmot Capital is now ready to scale and to serve a broader set of Limited Partners with Sokoloff confirming it is embarking on new research, including a project to decipher what it calls “thematic volatility”. n
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