Q: What is the history of the fund?

Paul Kleinaitis and I launched the PNC Multi-Factor Small Cap Growth Fund in September 2005. 

Q: What is your investment philosophy?

We use quantitative tools to cover three basic concepts. First, we believe that a stock should be valued relative to its peer groups, its history, and its current environment. Second, we include all the fundamentals that matter within the small-cap growth space. Revenue growth, earnings momentum, and profitability are key to our fund. Third, we believe that a catalyst is required, so investor interest and price momentum play an essential role in our buy and sell discipline.

We have a strong focus on discipline and quantitative methods that we systematically apply to the three-legged stool concept. The goal is to find consistent quality growth companies with catalysts that would trigger buy and sell opportunities. 

Our process reveals signals that the team analyzes for data accuracy and for any fundamental trends that the data cannot include. With that solid background, our objective has always been to win with our winners, but also to mitigate the losses and minimize the downside risk. 

Q: How do you define the small-cap space? What is the size of your investment universe?

We reconstitute our universe and its definition every quarter. As of July 2016, the market cap range for small cap is from $200 million to $3.8 billion. Our goal is to set a market cap range that incorporates all U.S. traded securities, including ADRs, MLPs and limited partnerships. The securities have to be publicly traded with at least six months of trading history. 

That universe is then filtered for volatility, liquidity, stability and quality of the balance sheet. We eliminate stocks with prices of less than $3.50. The securities we invest in should have trading volume of more than $1 million per day. Then we eliminate the companies with weak balance sheets. 

At the beginning of every quarter, we start with about 8,500 names that are filtered down to about 1,900 names, our investment pool.

Q: In terms of your investment philosophy, why do you believe in the three-legged stool concept?

Our philosophy is well supported by academic research, macroeconomic behavioral research, sell-side commentary and technical analysis. Plenty of academic research done in the last 50 years strongly points to companies with biases towards value and fundamentals, created by anomalies in sensitivities to different aspects of the market. 

The market by itself creates anomalies in short-term fundamental factors, such as top-line growth or profitability. In the past seven years, for example, earnings momentum played an essential role in the success of all growth companies, although valuations can be defined differently in the small cap space.

We believe that our multi-factor model is the right approach to the selection process. It is what differentiates us from our peers. Although the three tenets are our basic concepts, various underlying factors make a big difference. 

Q: Could you explain the architecture of your quantitative investment process?

Once we have defined the universe and the pool of securities, we apply daily systematic ranking to all of them. Each security receives a score based on our multi-factor model. We monitor about 85 factors daily, but only 10 factors, selected especially for small-cap growth, are used in the layout. 

The small-cap growth factors include earnings momentum, which we approach from two sides, the momentum itself and the risk premium. We follow the revisions of earnings estimates, but we also care about the risk uncertainty in the consensus. Other factors include changes in top-line growth and profitability. 

In terms of valuation, we look for simple factors, such as price-to-earnings ratios, but we also focus on specific factors for industries like technology or biotech. When we combine these factors with price momentum, we create a weighting scheme that is sensitive to the market. When the market focuses on four out of these 10 factors on a given day, we make sure that we have those four factors. At the same time, the 10 small-cap growth factors are uniform and work in the long run. 

The selection of these factors and their weights is what separates us from the other funds in the space. Quantitative tools can be used differently and, in our case, we rank the securities and we use the ranking to construct the portfolio. 

We use the best factor overlay, which focuses on the best factors for the last three months, and uses them to filter out the buy universe. In that way the universe gets narrowed down to about 400 names. 

Q: How do you approach the portfolio construction process?

At the portfolio construction level, we use a quadratic optimizer, which is about balance between picking the best securities and focusing on the type of risk we undertake. The optimizer diversifies our portfolio into 62 industries with limited exposure to each of them. That diversification helps to avoid being blindsided by a market spike or by a market trough. 

The goal of the optimizer is to always provide a bandwidth for each risk metric. So, we don’t have a targeted point, but a bandwidth that we have robustly tested. The tracking errors of our portfolio are an outcome of this process. 

At the time of purchase, we have limits of between 0.25% and 1.5% on position sizes. We let these positions grow up to 3% in absolute terms or 2.5% relative to the benchmark. That’s how growth is allowed when we are winning. 

At the industry level, we have a limit of +/-3% as a risk constraint. If an industry is hot, like biotech today, or energy or materials last year, or financials the year before, we need to limit our exposure.

Overall, we have 110 securities in the portfolio, narrowed down from 1,800 in the universe and from 400 securities in the buy list. This is a long-only portfolio and our focus is the long term. Through the long-term focus, we avoid any whiplash and negative reversal due to short-term factors. 

Q: How important is the role of the analysts in the fund?
 
The final step of the process is the fundamental oversight of the team. Through this analysis, we establish data accuracy, so we look for anomalies and perform a qualitative check on the data. Even when accurate, the data can be intuitively wrong from the investor perspective. For example, if a car rental company sells some of its assets, there will be cash recognition on its books. Any unusual trend in cash flow can be misleading about the efficiency of the company. That can also be reflected in the profitability, so we analyze the signal before we make a judgment. 

We also don’t like to step in ahead of announcements, because we would rather win through the process of fundamental and valuation analysis than through outguessing the news by meetings. In that way we avoid installing our own biases and the market biases into the portfolio. The goal is to be systematic, disciplined, and to create a diversified portfolio with good quality names based on the multi factor approach.

Overall, there is a significant contribution from us to analyze the data at the individual security level. But although we examine signals daily, we only rebalance the portfolio once a month to achieve efficiency with minimal transaction cost to our clients. 

Q: Do macro views and developments play a role in your model?

Yes, the macro view plays a big role. Price momentum and the type of price momentum you use, have direct relationship with macro events, such as changes in the fiscal policy or global events. 

There are two factors that we attribute to our success. One of them takes price behavior and considers the path it takes to the current price, while the other is the actual volatility. In any risk related to price, we believe there are two impacts - the volatility and the path it takes. Our two price momentum factors, which we have utilized since 2006, have helped us to find a direct correlation to macro events. 

Additionally, in the risk attribution in portfolio construction, we have factors that are directly related to the yield curve as well as to macro economic events, like the commodity impact on risk attribution. 

Q: Could you give us examples of specific holdings that illustrate your process?

Matthews International would be a good example, because it is not a conventional growth company. It works on high-end memorialization and has been silently building a great business with revenue and cash flow growth. It has been growing at a good pace through mergers and acquisitions. 

Our process identified it through the revenue growth and the cash flow metrics. We found it when the price momentum catalysts kicked in and we bought it in the middle of last year. Since then, we have seen growth at a fast pace. It was one of our top winners for the fourth quarter in 2016. This is a typical example of a company we found through our model. Everything supported a buy decision.

Often we wouldn’t buy a stock because of these metrics. For example, we found a stock connected to a gun company that was ranking high in our models, but the outcome was purely based on one leg, price momentum. We decided not to invest, because we need the cash flow and the fundamentals to also support the decision. That was a good call because after the elections the stock crashed about 36%. That’s why our process is based on all three legs, not just on the leg of momentum. 

Another example is Cantel Medical, which provides equipment and tools for biotech healthcare departments. Biotech stocks are difficult to invest in for a quant process, but Cantel is a supplier. It contributes to the success of biotech companies, without being directly involved. That successful investment was also been picked through our three-legged stool.

Q: When would you sell a stock?

We have a strict discipline and we would sell a stock for three reasons. First, we would sell when the ranking deteriorates. We buy stocks if fundamentals are strong, valuation is reasonable, and when price momentum kicks in. When one of the three aspects deteriorates, we start trimming the position and adding to securities with better rankings. Overall, stock deterioration is a direct reason for selling.

The second reason is when we are winning at the security level and the position grows to 3% of the portfolio. Then we would start trimming it. When an industry reaches the limit of 3% overweight, we would also start selling securities in that industry.

The final reason is if there is an event that contradicts our thesis or if the company disappoints. We call such stocks torpedoes. When that happens, we gather as a team, we review the event, look at the fundamentals, and then make a decision.

This discipline results in a constant turnover. At the end of last year, our turnover was about 76%, but we are focused on being very cost efficient when we exit. We look for liquidity in the right spots and we are careful not to give up liquidity to start buying illiquidity. Also, we aim to be perfectly silent when we go into the market. So we are very conscious of how we deal and we are very tax efficient in our process. 

Q: How do you incorporate what you’ve learned over a decade? Have you improved your systematic process? 

We constantly monitor the performance of the factors on a monthly, quarterly, and annual basis to evaluate their effectiveness in security selection. Ultimately, when we are successful, the market focuses on five or six different definitions at a time. It is difficult to win when the market focuses only on one or two factors, like in 2009. 

We look at the factors and their performance with regard to sectors and we evolve based on this information. For example, our definitions of price momentum have evolved since 1998, when they were quite simple. Now we have a more sophisticated way of calculating the price, the volatility, and the way we capture it. 

Regarding the estimate revisions, we have found that risk premium can be time sensitive. Initially, in the small-cap space there were many securities covered by less than three analysts, while today the number of stocks with low coverage has significantly dropped. We get many benefits from following the number of analysts covering a stock, when they change their estimates, and how often they change them. This type of analysis helps us quantify key trends. 

But although we evolve in the way we calculate the factors, our concepts do not change. The idea of top line growth and earnings momentum will always be functional. What we may change is how to achieve better extraction. We also try to improve the way we use specific industry data and accounting. 

Q: How do you define and manage risk?

We believe that risk should be addressed from the very beginning. In our case, risk management starts with factor selection and measuring stability for different timeframes. The stability of the securities in the different factor buckets is critical.

At the portfolio construction stage, we strive to consider every type of risk, including the relationship of the factors, the relationship of heuristic constraints, like transaction costs, the industry and the security risk. At the industry allocation level, we don’t try to outguess the market, because that’s a risk we are not very good at. So we keep our sector and industry allocation within certain parameters. 

We also consider the risk of human bias to be a critical contributor. Our fundamental work is about measuring that risk. Our goal is to always minimize our human bias rather than using it to add value. 

Ultimately, there is no such thing as a risk-free asset. So, if we are going to take a risk, we need to understand, measure, monitor, and minimize it. The only way to win is through minimizing the downside risk and maximizing the upside capture. 

One of the biggest challenges for quant funds is that the models always look backwards, which doesn’t necessarily mean that you will be successful in an event that will happen tomorrow. You cannot control or outsmart that event, so you need to manage the risk and learn from the system what can and what cannot be managed.