Q: Would you give a brief overview of the fund?
I started with PNC in 2002 with a plan to help develop a process for their Advantage line of funds and we subsequently took funds over in 2009 after the National City merger.
PNC Large Cap Growth Fund seeks long-term capital appreciation by investing primarily in a diversified portfolio of growth-oriented domestic large-cap companies. The current assets in the fund stand at a little less than $100 million. However, overall we manage just under $500 million, in institutional and high net worth accounts under this strategy.
Q: What is your investment philosophy?
We believe that earnings drive stock prices, and that it is critical to have a strict, disciplined process that focuses on earnings, one that does not permit subjective or behavioral factors to influence our stock picking. Our primary investment goal is to find stocks that outperform the index and exceed analyst expectations.
We are more interested in a stock that is expected to grow earnings at 2% but in fact grows at 4%, rather than a stock that is expected to grow earnings at 25% and instead grows at 24%. Our stock picks are designed to grow faster than the broad market, but we take a long-term approach and our primary focus is on exceeding expectations rather than having a flat-out faster-growing stock.
Q: How would you define your investment strategy?
Our focus, again, is on outperformance—despite the fact that it is difficult to outperform in the large-cap space. So, to do this, we employ a disciplined quantitative process. Simply knowing more about Apple Inc or General Electric than everybody else is not enough to ensure we accomplish our goal to outperform.
What we do is create a portfolio that has distinct characteristics, one with a more stable earnings pattern and lower downside capture ratio. We are intent on having less earnings disappointments than the broad market, and there have been only two quarters since 2003 in which we experienced more disappointments than our benchmark, the Russell 1000 Growth Index, did.
This is because we have a strict framework, a distinct set of rules, that each stock must adhere to. They must display specific characteristics in order to be added and maintain their position within the portfolio, so it’s not just our buy but our sell discipline as well that is crystalized. If an investment violates these disciplines, then the stock is not for us.
It is hard to make a compelling valuation argument on a stock with deteriorating fundamentals. We have each of our analysts set both a high and low price target for each stock they consider. If a stock goes below the low price target, it indicates to us that there was a risk associated with this investment that we did not fully anticipate.
Q: How is your investment process distinct from peers?
Our process is such that we don’t anchor or fall in love with a stock and turn a blind eye to its performance. There are no debates. We are vigilant in having a strict process—we have rules, guidelines, and tools that we follow and employ time and time again. As a result, we generate a subgroup of stocks with distinct characteristics, and it is these characteristics that provide an opportunity to outperform.
Quarterly earnings comprise our key variable—they are predictable as a variable and the relationship with price is well defined. Top-down macro calls are difficult to make, so, while sometimes top-down data can supersede records of bottom-up data, we are willing to give up outperforming in all environments in order to have that desired subgroup of stocks with the characteristics that tend to outperform in most environments.
Q: How does that work?
To accomplish this, we start off with a quantitative model. A stock simply will not be added to the portfolio if it fails to score well within our quantitative model. Neither will it go into the portfolio if an analyst does not approve of it on a fundamental basis.
We find that using such a quantitative model makes our analysts more objective and therefore more productive, and that those stocks that fall into the top quintile of our model outperform annually by 3% on average. If a stock scores well within the quantitative model, if the analyst has an estimate set higher than that of Wall Street, and if the reward/risk ratio falls above one, we will try to find a place for it in one of our portfolios.
Our quantitative process does feature a quality component, akin to S&P’s financial strength rating: we include volatility of the earnings stream; we look at quarterly earnings and estimates for the next four quarters; and we look at the growth rate and the slope, the standard deviation. If we compare two companies that are growing at the same level, the company with the stabler earnings pattern will score higher. This particular factor has helped lower our downside capture ratio, something that might go unnoticed if we focused more on momentum.
We want to outperform not just on an absolute basis, but also on a risk-adjusted basis. For the growth product, we place a greater weight on our momentum characteristics, looking at relative strength, earnings surprises, and earnings revisions for the next two financial years.
We also include valuation in our model. We look at price-to-earnings, price-to-book, and price-to-cash-flow, but we’ve found that, on a sector-relative basis, we almost double the predicted nature of the results.
We like to have a process that is as methodical as possible. Anytime we can replace a subjective decision with an objective decision, we do. We have fundamental analysts who are involved in the process, but we provide a great level of focus on what we ask them to do.
Ultimately, we apply a strict framework in which to identify stocks that outperform, stocks that do not disappoint analysts and do not disappoint the market, while having attractive relative valuations and a quality bias.
Q: How do you approach your research to identify opportunities?
There is a temptation in the industry to favor companies in which one possesses the most knowledge, yet there is no correlation between knowing a great deal about a company and the performance of its particular stock.
In contrast, our model is wholly objective. It does not know what stocks one might favor over others. It simply provides stocks that possess a certain combination of characteristics that lead to outperformance. The parameters of that model forces an analyst out of their comfort zone to consider and evaluate companies that they may know little or nothing about.
Our analysts utilize their Street connections and their expertise to dig down and come up with key merits and risks that reveal precisely what the Street has missed, the very features that will lead to such a stock exceeding expectations, and analyze existing risks and downside using this measure.
Our analysts are responsible for their own sectors, and they share their findings with a senior analyst who pushes back on any analyst assumptions and listens to their narrative as to why they like certain stocks and why they think these will exceed expectations. The analyst determines whether the stock scores well according to our model and, if they find one that should exceed expectations, they put a price on it. The senior analyst then tests the assumptions and validity of the decision.
In keeping with our strict set of rules in terms of gauging potential performance, while the senior analyst listens to the narrative, I do not. As the portfolio manager, yes, I like to know what the analysts like and why a stock meets our criteria, but I do not necessarily need to go into depth about the rationale behind their recommendations. I have no intention of falling in love with a stock. My job is to make sure it meets our objective criteria.
Once the senior analyst agrees that a stock meets our criteria, they discuss it with me. I determine whether it is the best opportunity out there and verify that it scores well within the model. I am there to establish whether our process was followed, that the stock does meet our criteria, and that our decision is predicated on the model rather than street research.
Additionally, we hold weekly meetings where we assess our current holdings to make sure they still meet our criteria, particularly after an earnings report.
We also look at where our sectors are vs. the benchmark—we have guidelines for our growth portfolio as it compares to the Russell 1000 Growth Index. We might weight the major sectors as high comparatively as 120% and as low as 80%.
Q: What is your portfolio construction process?
We’re sector-neutral and primarily based on model-driven stock selection. Our initial purchase of a position generally runs between 1% and 2%, and we have a maximum active weight of 3%, particularly if a stock is not in the Russell 1000 Growth Index. However, if it’s something like Apple, which represents a substantial weight in the index, we permit ourselves to go as high as 3% above the index weight, but, again, only if the stock scores well on our model.
In terms of sector overweighting or underweighting, we look at the top quintile in our multi-factor model, and if a sector represents a bigger than index percentage of the top 20%, we will overweight it. Conversely, if a sector is under-represented, we will underweight it, but we make sure we maintain the 120 to 80-weight spread to adhere to our mandate.
And we keep a perpetual eye on risk measurement to determine our tracking error, as we like to see a tracking error of four or less.
In terms of the number of positions we maintain, it’s about 60 to 65 right now; we maintain just under 70 as our maximum holdings. Our overall goal is to have 40% turnover, but we’ve had turnover at just over 50% in this particular portfolio, some of which has to do with our making adjustments for the rebalancing of the Russell 1000 Growth Index, because we want to maintain the tracking error and our guidelines in terms of sector weights.
Q: What drives your buy and sell discipline?
In terms of why and when we sell, we have sell decisions above and beyond our hard sell disciplines. For example, a lot of times a stock may not necessarily violate our sell discipline, but we choose to sell it anyway because we have identified a higher-scoring alternative. It might be fully valued in terms of reward/risk ratio, where there is not much more to its upside, or it may have had an earnings disappointment and revisions are going down.
We have a special tool, distinct from the multi-factor model I explained earlier, which looks at SUE, the Standard Unexpected Earnings surprise, and the earnings revisions going into the next quarter. If a stock is in the top decile of both of those factors, it has a greater than 80% probability of exceeding expectations.
So, if we have a stock that looks attractive, meets all the criteria, and looks like it will beat expectations, we will try to move into it prior to the earnings announcement in order to benefit from that new information and the positive effect it should have on the stock price.
Where this tool really helps us though is on the downside. If the stock falls into the bottom quintile in those two factors, near-term revisions and standard unexpected earnings surprise, the stock has a 70% probability of missing the analyst’s expectations, compared to 25% for the broad market.
We have three qualifications in determining when to sell; we don’t just sell when a stock falls below our price target low. If the stocks go below our low and this internal assessment is below that of the Street’s, then we sell the stock. If it goes below the low and near-term revisions are in the bottom 20%, indicating the stock has a greater deterioration than the broad market, then we sell the stock. If it goes below the low and lies in the bottom 20% of our multi-factor model, we sell the stock.
When we apply this strict discipline to our buying and selling, it keeps us from falling into a value trap. It helps avoid rationalizing as to why we should otherwise maintain a holding, and it avoids the temptation to ignore the negatives by analysts who fall in love with stocks and their story or who are intrigued and impressed by management. By the same token, if a negative issue arises and a stock’s price drops faster than the earnings estimate declines, it helps us to avoid making the assumption that the bad news is in the stock itself and selling prematurely.
Q: How do you classify and mitigate risk?
Our clientele is typically concerned about the portfolio falling more than the broad market. We are very conscious of downside capture and standard deviation as measures of risk. In those particular characteristics, we tend to come up as below average, even in the bottom quartile.
As I mentioned earlier, we use third party risk model as the measure of tracking error, not from an optimization standpoint but instead to determine the source of any tracking error and ensure that we are comfortable with it.
For example, a couple of years ago, the biggest component of our tracking error was that we had overweight in aerospace, yet, according to our model, a lot of those companies were scoring well. Because we felt that sector presented a better opportunity for outperformance, we were happy for that to be the biggest tracking error component—we felt it was a good source of risk.
What we do not want to see is inadvertently having a big risk exposure to, for example, interest rates or mortgages. We are willing to have risk in the portfolio; we want to deviate from the benchmark to outperform, but we insist on being conscious of where those risk exposures lie.
Our quality bias leads to lower downside capture and lower standard deviation, as does our valuation incorporation into the growth process. The fact that we have fewer disappointments than the broad market also helps lower our standard deviation and gives us better downside capture ratio.
Because outperforming the index is our primary focus, we are less susceptible to market volatility in many respects. When the market does well, that is good for us, but if the market falls because the Federal Reserve raises rates or because of inflation, creating less of a growth environment, our focus on better fundamentals and avoiding disappointments means that we can still meet our objective and outperform.
It is arguably the single variable top-down factor that impacts the market that stands to make our job more difficult.
