Sandbar's focus on idiosyncratic factors sets it apart from its peers in equity market‑neutral

With investors sometimes struggling to find hedge funds that deliver uncorrelated, consistent returns, Sandbar Asset Management stands out from its peers. Its success in running an equity market-neutral strategy is a reflection of its founder and chief investment officer, Michael Cowley

When establishing Sandbar in 2017, and subsequently launching the Sandbar Global Market Neutral Fund, Cowley built on the reputation and experience he gained working at some of the world’s biggest and most respected hedge funds. Immediately before founding Sandbar, Cowley was a portfolio manager at Millennium Capital Partners, managing a global equities long/short portfolio within strict market-neutral parameters. He began his investment career in 2006, managing a similar strategy at Citadel, and has also worked at Balyasny Asset Management and MC Squared Global Investors.

This experience resonated with investors looking for a discretionary manager with a consistent and persistent track record – particularly of risk management and controlling drawdowns. This expertise was also why, in October, Sandbar launched the Lumyna-Sandbar Global Equity Market Neutral Ucits Fund, run pari passu with the existing master/feeder Cayman fund.

So far this year, the Cayman fund is up around 7.5%, with a net cumulative return since inception in August 2018 greater than 12% and a Sharpe ratio of 1.4 with 5.65% volatility.

The Ucits fund – available on the Lumyna platform, previously the Bank of America Merrill Lynch alternative Ucits funds platform – attracted $100 million in assets under management (AUM) at its launch and has since grown to $280 million. Lumyna expects the Ucits version to attract several multiples of that amount over the next few months.

Cowley’s process is both simple and complex. In a nutshell, the investment process utilises hard data to populate proprietary models of global large-cap companies in order to form a differentiated view of company expectations from which the team makes its picks of longs and shorts.  

This methodology provides diversity and is focused on idiosyncratic risk. Every stock carries inherent risk but, by understanding the various risks – market, sector and factor – Cowley can focus on those specific to a single company or stock but not affecting the market as a whole or the overall industrial sector in which the company operates. 

This focus on hard data and quantifiable risks is what sets the strategy apart. “We have an advantage over purely 100% systematic or quant-driven funds. We have an overall picture. Our models rank the most attractive buy and sell opportunities. We take a true fundamental view into the stock and then allocate,” he explains.

 

Hard versus soft data 

The investment process itself is discretionary, but by quantitatively analysing only hard data it attempts to remove as much subject bias as possible. 

“We don’t engage in macro forecasting,” he says. For example, Cowley is not interested in a forecast that, over the next five years, BMW stock may rise because of estimated future car sales. He believes taking data from today and extrapolating five years into the future is a recipe for failure and heightens risk in a portfolio.

“We can rank and process information on a company quicker than the market – as well as the impact of that data analysis and magnitude of impact on share prices. We only track hard, realised data,” says Cowley. If the analysis suggests a stock is mispriced, it is looked at as a possible buy or sell opportunity.

“We remove human emotion and confirmation bias from the process,” he confirms. Sandbar does not have analysts that believe in a company, stock or sector. Rather, they look to ensure that, within the model, they treat all stocks the same, tracking hard data from multiple sources relevant to the company or sector only. By tracking and interpreting this data faster and smarter than the competition, Sandbar has been able – even with a short track record – to demonstrate its process works and attract interest from investors.

Having run the same strategy previously at other hedge funds also gives investors the confidence that Sandbar’s process is repeatable and sustainable. “Our core process is quant-driven, hard data, not speculative and not subjective,” Cowley reiterates.

The model avoids ‘soft’ data. Macro sensitivities such as Brexit are included in industry and company models but, in keeping with the philosophy, macro views are not expressed in the portfolio. Rather, Cowley says most large-cap companies play in a global market and are rarely focused purely on their domestic market. 

While other discretionary traders may have macro and geopolitical bias, Sandbar’s process produces a data-driven idiosyncratic story for each company. The team spends its time picking stocks by understanding the fundamentals of the companies and not putting macro risk on top of that.

By not introducing views on markets, sectors or other factors such as political risk into the fundamental process, Sandbar creates a portfolio with low residual market risk. “We don’t spend time speculating whether we should be long UK or short Germany or long industrials and short consumers. By having a more complex front-end-loaded model, it makes it easier for us not to rotate or reverse the book because of a changing view of a stock or the world,” he notes.

“We are very honest about what we do and what we don’t do. We look to create a portfolio as close as possible according to idiosyncratic risk. We don’t have a view on systematic risk. To us it is inconsistent to put risk into the portfolio, so we focus on the stock only looking to capture the mispricing spread between two assets. This is what you should do to pick true market-neutral long/short idiosyncratic stocks,” says Cowley.

This methodology creates a lower set of risks within the portfolio. It also means the stock picks and pairing can come from any companies in the same industry anywhere in the world. The key is not to be concerned about pairing long and short by sector or geography, for example, but only looking at the spread the trade can ultimately capture.

Cowley emphasises he does not look for longs and hedge those positions with a short. Instead, Sandbar tracks the data in a sector such as automobiles, and finds some level of inconsistency in how markets are pricing data points for two or more assets. This dispersion spread shows an inconsistency across a data point.

“Compared to our peers, we don’t look to take sector views or introduce sector bias. We are not trying to predict or play the cycles. We take a universe of names thrown up by our models and dissect them. We look at where in the sector and subgroup levels there is an inefficiency, and twin assets in that group,” he notes.

 

A global playing field

Sandbar’s playing field is global. The portfolio sees all sectors as global. Where a stock is listed is irrelevant because competition is global. “It would be illogical to cover stocks by sector and geography. We wouldn’t look at, for example, Caterpillar in the US and put it in the US machinery bucket. Caterpillar isn’t competing against companies in the US. Its competition is in emerging markets,” explains Cowley.

Sandbar avoids systematic risks in the portfolio by using this technique. “Our universe is set up to look at global companies and global subsets. That gives us a lot of companies from which we can pick pairs: sets of companies, potentially a dozen or more, that may be interesting. We find a clean pair of trades.”

The models generate fair value looking at different scenarios, risk/return ratios, downside potential and other factors. 

Around one-third of all stock price performance is systematic risk. By taking a market-neutral approach, Sandbar reduces systematic risk to as close to zero as possible. The models isolate the remaining two-thirds of idiosyncratic risk. 

“We can’t control or measure all idiosyncratic risks. We won’t get everything right but, if we isolate the idiosyncratic risk and remove systematic risk, when things go wrong it will be because the model is not reflecting an idiosyncratic event we had no ability to foresee,” explains Cowley, adding, “We can’t control all the variables. We can get things wrong. But we have a diversified book and we are never highly concentrated.”

Holding periods, on average, range from six to nine months – usually less than a year. The portfolio tries to capture short-term inefficiencies and mispricing based on the hard data received in the present rather than looking into the future. 

The portfolio, however, could have a position for two years in a stock because the data keeps getting better and fair value keeps rising. But, as soon as the model says the fair value has been achieved, the stock is taken out. 

“We don’t play momentum or confirmation bias. We don’t hold onto winners or losers. If the data changes against us, we close out. That is a way for us to manage risk and ensure there is no style drift,” confirms Cowley.

Two-thirds of the dynamic portfolio management is generally composed of paired ideas, with the remainder discrete long/short ideas. Positions are adjusted for conviction and catalyst timing and ensure portfolio neutrality.

The portfolio holds at least 100 positions, usually closer to 150. The models drive day-to-day position size moves. 

Cowley believes there is an economy of scale in having models that can incorporate the most important components of a stock or sector. For example, for airlines, the price of jet fuel, how many seats are sold and at what price. Only available hard data is used and fed into models. “We are not trying to fit data to our own view or purpose,” Cowley explains.

 

Environmental, social and governance (ESG) factors 

Interestingly, ESG factors are fed into the models. “We look at ESG from a fundamental perspective. For example, where companies make engines for cars, which ones are developing electric vehicle systems quicker or using more recycled materials? The key thing for us are fundamentally driven factors. Logically, if you have bad corporate governance and financial accounting issues, you will be a target as a short. That’s also an ESG issue.”

The Ucits fund, as with the Cayman master/feeder, runs with high single-digit volatility. Because it is focused on liquid and large-cap securities, it has no problems with daily dealing. 

The Ucits fund is domiciled in Luxembourg with three currency classes: euro, sterling and US dollar, with the minimum investment $1 million for institutional investors. Management fees range from 1% to 1.4%, with performance fees of 15% or 20% depending on share class.

Scaling up should not be a problem as Cowley believes the portfolio could easily accommodate $3 billion or more AUM as stocks targeted are, on average, in the $10 billion cap range with average daily trading volume of $50 million or more.

Sandbar launched its offshore fund in August 2018 and the Ucits in October 2019, and investors have been confident in the institutional running of the company and its focus on risk management. Cowley and his team’s experience at some of the best – and biggest – hedge fund houses has given him an appreciation of the value of strong operational infrastructure and a solid back-office team.  

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