Q: What is the history of the company and the fund?

Cognios Capital is a quantitative investment management firm founded by members of the management team in 2008. Jonathan Angrist, Chief Investment Officer and President, has spent almost 20 years developing the firm’s proprietary ROTA/ROME® portfolio construction and investment selection methodology. 

ROTA/ROME® focuses on a company’s Return on Tangible Assets (ROTA) and Return on Market Value of Equity (ROME). While ROTA/ROME is a value-based philosophy, it recognizes growth as an important component in assessing a company’s intrinsic value. 

The Cognios Market Neutral Large Cap Fund is the flagship product of Cognios, which also advises the strategy in a Limited Partnership structure and in separate accounts, and also offers long-only strategies in a separate account structure. Among those long-only strategies are three growth strategies that came to the firm when it acquired the operations of Oxford Creek Capital in 2012. Oxford Creek was owned by Jim Stowers, III, the former President, CEO and Chairman of American Century Investments, who now serves on the Advisory Board of Cognios.

As of March 31, 2016, the employee-owned firm had approximately $500 million in assets under management, with $250 million in the market neutral strategy. Net assets in the Cognios Market Neutral Large Cap Fund are currently $105 million and $200 million gross. The remainder of assets is in various long-only growth and value strategies. 

Q: What is your investment philosophy?

Our core belief is that using ROTA/ROME® is the best way to determine whether a particular investment should be made. Using these metrics helps us identify which stocks we want to own and if we should buy them based on when they are trading at attractive prices.

A company with a high ROTA, or return on tangible assets, is a good business to invest in. A company with a high ROME, or return on market equity, is trading at an inexpensive price. 

In the long book of our market neutral strategy, we want to own companies with high ROTA and high ROME. In our short book, we essentially look for the opposite to identify bad businesses or stocks trading at expensive prices. 

When determining ROTA, we always use a company’s cash profits, not accounting profits, and tangible assets as opposed to total assets — meaning goodwill is excluded from the calculation. 

Though goodwill is an interesting number, it does not tell us anything about individual assets; instead, it shows whether a management team paid more than book value. Using ROTA allows us to compare businesses by looking at the underlying economic dynamics of their assets.

Q: How do ROTA and ROME work?

To illustrate how ROTA works, we can look at an example of someone investigating two businesses. They have $1 million to invest in one, and with it will need to buy hard assets like property, plant and equipment, while leaving enough for working capital and cash on the balance sheet. 

With that $1 million investment, their research shows the first business will make $200,000 a year, while the second has a history of making just $50,000. The first is a 20% ROTA ($200,000/$1,000,000) business and the second a 5% ROTA ($50,000/$1,000,000) business. With a few exceptions, high ROTA businesses are always better businesses because the business makes more money. 

The same hypothetical example can show how ROME functions and how it differs from ROTA. 

Some years after this investor bought the business, he decides to sell it. He originally invested $1 million and had 100,000 shares, so the stock price reflecting the cash he invested in the company is a $10 per share. 

However, because it is a good business with 20% ROTA, the owner is able to negotiate to sell it at $20 a share. So in addition to having made $200,000 a year, he receives $2 million for the assets that he paid $1 million for. 

The business’s 20% ROTA does not change, however. Its balance sheet still has $1 million in tangible assets, and the business is still making $200,000 a year, so its return on tangible assets remains 20%. 

Its ROME, or return on market equity, is 10%, which is determined by dividing the $200,000 per year profits by the new owner’s $2-million stock purchase. His company is a 20% ROTA business with a 10% ROME.

When comparing businesses with similarly high ROTAs, we invest in those with the highest ROME, because they are trading at lower prices. 

Our quantitative systems allow us to cull through millions of pieces of historic financial data and efficiently execute on ROTA/ROME®. Unlike many in the quantitative space, we do not rely on technical analysis or indicators like stock price momentum. 

Though we do quantitative analysis of historical fundamental financial information, we do not look at historical stock prices in our value-based and market neutral strategies — only at current prices. The implication of this is that we do not forecast; we never try to predict what a company will make next quarter or next year and then put a multiple on that. 

Historically, our fundamental quantitative research demonstrates that companies with high ROTA today tend to have persistently high ROTA tomorrow. This is a generalization that the companies that were the best businesses in the past are most likely to continue to be the best businesses in the future, and further, the companies that were the worst businesses are not likely to turn themselves into great businesses.

We rely upon these generalizations; ROTA is a long-term measure that is not looked at over the past quarter or year, but instead over a longer period of time, helping us capture the earnings power of a company regardless of where it may be in its cycle. 

Q: Could you describe your investment strategy and process?

Our process is straightforward, quantitative, and systematic, and for the Cognios Market Neutral Large Cap fund, it starts with the universe of the stocks in the S&P 500 Index.

ROTA and ROME score every S&P 500 stock. Though for research purposes we run models daily, on a monthly basis the highest-scoring stocks are selected for the long book, and the lowest scoring stocks for the short book. Then we re-run the models and compare the model portfolio to the actual portfolio, and our computer programs tell us what trades need to be executed to align them. 

On average there are 45 names in the long book and 110 in the short book. In a given month, three to five names may come in and out of the long book, and five to seven in and out of the short book. The remaining holdings might need to be rebalanced back to their target weights.

So, in our process, there is no manager intervention, no short-term tactical tilt, no biases or judgments based on what is going on in the market or in the news. Stock picking, portfolio construction, and trades are based purely on the scoring models developed by our portfolio management team, which in turn are based on historical financial performance, ROTA, and the most recent stock price, ROME.

One of our key risk measures, and something our model adjusts for, is how we achieve market neutrality. We believe the best approach to achieving market neutrality is by being Beta-neutral; we also never use options, swaps, futures, or any other types of derivatives. From a portfolio construction perspective, our goal is to factor out all the market impact — global or U.S., credit or equity — to generate returns that are independent of any market movements. 

Q: What is your research process and how do you look for opportunities?

Investing is a lot easier than most people make it; ROTA and ROME tell us what to own and when to own it. Though there are some proprietary statistical and quantitative ways these scores work over time, we try to keep it as simple as possible. 

ROTAs tend to persist over time, but ROMEs change every day. On our list of the highest ROTA companies, there may be one trading at 3% ROME — and that is a company we wouldn’t own while it is that expensive. 

A stock’s current market price ultimately dictates whether it goes in the long book or the short book. Because some industries and sectors are more likely to have companies with high ROTAs or low ROTAs, it might seem that some would be more highly represented in one book instead of the other. 

In practice, pricing evens out much of this, so we have a broad representation of all sectors and industries in both books over time. 

Financials are one exception. Though some — like insurance companies and brokerage businesses — end up in the long book, banks are unlikely ever to because they tend to have ROTAs in the 1% range, some of the worst performing ROTAs in the S&P 500. It is generally not possible to get a stock price cheap enough to offset this. 

As a result of their low ROTAs, banks show up too often in the model’s short book, and because of this we have limited financial exposure in the short book to the total financial exposure in the S&P 500. Currently in the S&P, there are around 80 financial companies representing 16% of the total market cap, so today financials are limited to 16% of the size of the short book. 

Because our strategy is systematic, we can go back 15 years to test models and formulas. In that time, no one sector has ever become so dominant in either the long or short book that it makes the portfolio uncomfortable to us. 

Sometimes a sector might represent 30% of the long book, but in almost every case there is also some representation of that sector in the short book. On a net basis, it is an exception if a sector ends up representing more than 25% of the portfolio. Because of this, today, there are no restrictions on an individual sector’s concentration level in the portfolio, other than the Financial sector in the short book as mentioned above 

Q: What is your portfolio construction process?

As I spoke about earlier, our portfolio construction process is based strictly on historical quantitative fundamental research, computer models manage the three steps of our portfolio construction process: picking which stocks will be in the long and short books, mechanically determining the weight of each, and figuring out how large the short book needs to be to hedge out the Beta in the long book. 

The fund’s benchmarks are the HFRX Market Neutral Index and the S&P 500 Index. We also think it is important to benchmark ourselves against the Morningstar Market Neutral Category although that is not an official benchmark of the Fund. Also, because we are a market neutral fund, third-party data providers typically benchmark us against the risk-free rate. But right now, the risk-free rate of return is so low that we think that this is an inappropriate benchmark. 

Over time, the fund should have lower volatility than the S&P 500 index. In general, when the S&P 500 is up, investors should expect the fund to underperform it on a net basis — because though the S&P 500 is in our long book, it is also in the short book. However, when the S&P 500 Index is down, the fund has historically outperformed the Index. 

We operate under a philosophy of continuous improvement, and while our models are already working well, our goal is to always make them better. They are constantly studied and periodically fine-tuned, and any changes are tested before implementation. 

Q: How do you define and manage risk?

In the broadest terms, we define risk as the chance of losing money. 

Although standard deviation and volatility are interesting measures, we do not consider them risk factors. These metrics are much more important to investors using high levels of leverage and investing over shorter periods of time. We use only 20% leverage, a very modest amount for a hedge-fund-like strategy. 

Our focus is on getting an acceptable return with a modest standard deviation rather than focusing on having the lowest possible standard deviation. 
We do not view our long portfolio as being very risky over long periods of time because the companies we invest in are large, stable companies; however, over shorter periods of time the stock market can be much more volatile. 

Unlike its long counterpart, our short book by definition introduces true risk, because any time investors are short anything they can lose more money than invested. This risk is managed primarily by diversifying the short book with more names — on average 110 — than are in the long book. We could make more money if the short book had fewer names, but we find the corresponding level of risk that concentration introduces to be unacceptable in the Fund. 

As mentioned, another risk mitigation technique we employ is limiting the portfolio to companies in the S&P 500. This is not so much because we worry about buying, or being long, any mid-cap or small-cap stocks, but rather reflects our belief that only large-cap stocks should ever be sold short in this Fund.

We believe this for two reasons. First, smaller stocks are harder to borrow from a bank, and therefore two things can happen: a bank can charge a lot of money when borrowing a stock to short it, which makes it difficult to make money; and second, because smaller companies tend to be less liquid, investors could be subject to a short squeeze. These risks are much lower in the large-cap stocks of the S&P 500. 

Beta neutrality helps us factor out the risks inherent to market movements. Many market-neutral portfolios are dollar weighted: $100 long and $100 short. The problem with this is that it fails to hedge out all the portfolio’s Beta, because the Beta of the long book is generally different from that of the short. 

Our short book’s Beta is about one and a half, while the long book’s Beta is about one. This means for every 1% swing in the S&P 500, our long book, before the Alpha, will want to swing plus or minus 1%, while the short book moves more, swinging plus or minus 
1-1/2%. 

To hedge out all the Beta in the long book, we determine relative Beta. Today, because the Beta of our short book is higher, we would divide the long book’s Beta by the Beta of the short book, or 1 divided by 1-1/2, which results in 0.667. This tells us we need only $67 of shorts for every $100 of long. 

Because of this Beta-neutral structure, any returns of our portfolio should be completely independent of what the overall market is doing meaning that its returns are non-correlated to traditional stock and bond markets.