Q: What is the background of the fund?

The fund began in April 2005 because we believed that investors were going to become more income-oriented. We had just gone through the tech bubble and subsequent collapse, but everyone was still talking about growth and there wasn’t a lot of talk about the equity income area.

Even though we were looking at the income component, we were concerned about getting stuck in stocks that had high yields but little growth, such as utilities. And we also wanted to get representation across all 10 sectors, including themes such as technology, consumer discretionary and health care, where there was not a lot of investing by dividend-seeking investors.

Thus, our strategy had a dual mandate: first, total absolute risk-adjusted returns against the benchmark; and second, we valued the idea of a dividend yield. Our goal was to produce returns and a higher income stream through dividends.

The portfolio today is a fully active approach benchmarked against the Russell 1000 Value Index. Our goal is to outperform that benchmark and also target a forward dividend yield that is 50% higher than the benchmark index itself.

Over time, that spread has widened and narrowed depending on where we find relative value, but over the long haul we’ve been around that 50% yield advantage relative to the benchmark.

As of the end of June 2015, the total assets under management in the strategy were $670 million, and in the Nationwide Equity Income Fund were $500 million.

Q: How do you differ from your peers?

We started running this fund as an all-equity fully-invested fund long before people started concentrating on the yield components of stocks. We don’t take big cash bets; we want investors to be able to use this product to put into their asset allocations to U.S. equities, so measured against other full-equity funds, we hold little cash.

And because the fund is designed to hold dividend-paying stocks, it has a lower beta compared to the market in general and also compared to a lot of the other managers. We focus on total return in the large cap value space, but with an eye on yield.

Q: What core beliefs drive your investment philosophy?

We are very focused on dividends, which have been a major contributor to total returns over time, combined with an active stock selection process.

Our first step is to find stocks in each of the 10 sectors that have a yield above that of the average of the stocks in that sector. For example, if the average yield of stocks in the financial sector is 2%, we will at least get yields above that.

In addition to yield, we want capital appreciation. That goes back to our core discipline, which is, how do we find mispriced securities? We want to have stocks that are underpriced and stay away from those that are overpriced.

For that we turn to behavioral finance, and explore the anomalies of behavioral bias. That is at the core of our thinking. We use behavioral finance concepts such as anchoring, relativism, and framing to examine how investors’ over-reactions to the past and under-reaction to recent events can create mispriced securities.

Once we see where our universe of dividend-paying stocks line up, we follow a two-step process. We look at what has happened in the past versus the current trends to help us identify neglected stocks that represent good value and differentiate them from popular stocks that have become overpriced.

Many investors form their expectations for future returns by looking to the past. They latch onto whatever has been a winner in the past and, believing it will continue to do well, stay away from stocks that have been losers in the past. But we understand that winners can be priced for perfection, and we want to stay away from those.

There are also bargain stocks in the neglected area—stocks that have fallen out of favor, for company-specific reasons or industry reasons or macro reasons, but which we feel are now turning around. We look for the bargain stocks that have above-average yields relative to their sector.

In all of this, we recognize that we as investors are human too, and can fall into those same mistakes that investors make. This quantitative screening helps us avoid those mistakes and is a very strong tool to identify pockets of potential mispricing. We also dig deeper by performing fundamental analysis on the back end to confirm the mispricings.

Q: What market cap do you focus on?

We are a large-cap manager, so the weighted average market value of our portfolio is north of $100 billion. We will own names below $5 billion, but the weighted average market value will be plus or minus $10 billion to our benchmark, which is the Russell 1000 Value Index.

We try to avoid taking size bets in the construction of the total portfolio.

Q: Where do you get ideas and what investment criteria do you follow?

In the fundamental analysis process we identify names that we feel are mispriced by the market, because investors are extrapolating underperformance too far into the future, and missing out on opportunities.

While there are many variations, most of these stocks fall into a few buckets. The first category is an average name in an average industry, or a below-average name in an average industry, or an industry that’s growing slower than the broad economy and so is structurally shrinking.

The market recognizes that negativity and often overreacts, so we can buy that average business at a really low price.

The second category, our holy grail, is a structurally sound company—hopefully an above- average name in an industry that’s growing faster than the economy—that has gone through a lot of well-known, well-publicized negative issues. Maybe it’s a widely known name that portfolio managers have owned for three to five years, but it’s really underperformed, and now it’s difficult to continue to own.

We look past the cyclical issues to what the company will look like two or three years down the line, its earnings and mean reversion to the upside, and how we can benefit from that.

We try to understand and identify where our names fit in and what has to happen to make them look more attractive to the market.

Q: Would you share some details about your screening process?

At the beginning of the week, we distribute our buy/sell list to all the portfolio managers. Many of the same names show up from week to week; new names, as well as over- or under- represented industries are highlighted.

We hold a portfolio manager meeting every Wednesday. Because we are a large cap manager, at each meeting we discuss general macroeconomic trends and what’s happening in the market, and then why our particular industries or sectors are over- or under-represented.

Right now, for example, we are talking a lot about health care because the political candidates are talking about these issues and it’s in the news—and many of the stocks are being punished. We know that for one industry to be under-represented while another is over-represented, you have to have relative under-performance. In all our measures we’re tracking relative under-performance or over-performance, either on a fundamental basis or a price performance basis.

So we are asking ourselves, “What is the fundamental aspect that causes one industry to be over-represented, and another to be under-represented?” We try to understand what is happening and what is being done to get over the hump, and what the industry will look like once it gets past those cyclical issues.

Q: How do you deal with issues from a relative value perspective?

Our two-step process makes us unique. Like others, we look at cash flow to price, sales growth, earnings estimate revisions, and so forth, but the difference lies in how we use them.

In the first step, we identify stocks that have the potential for mispricing, and divide them into three buckets. At one end are the poor-value neglected stocks; at the other are the popular stocks; and about 60% of the universe is neutral and falls in between.

We focus on the two tails.

What makes us unique is how we apply the second step. We put momentum measures in the two buckets, the deep value neglected side and the popular growth side, and ask key questions: What is the market telling us via the price mechanism? Is it moving up or breaking down? What are the analysts telling us through their estimate revisions? Are they marking them up, down, or leaving them the same?

The third source of information is the company itself. Does it meet expectations, beat expectations, or disappoint? We weight these differently in the two buckets, to account for the very different characteristics of the investors in the value side and the growth side, and come up with a score.

So, on the deep value neglected side, we try to find bargain stocks and stay away from value traps. On the other side we want popular stocks that have good growth, but not overpriced stocks that are starting to disappoint.

Q: Can you give us some real-world examples?

A great example of how we use those quantitative screens is from 2013, when we were very underweight energy. Although a lot of energy stocks started to show up as value, we were staying away from them because the momentum was not coming through and the earnings still had to come down.

In late 2014, the momentum started to flatten out, and we believed the stocks were cheap. We started to see them turn around, and so it made sense to bring that large under-weight in energy up to at least a market neutral to control the risk of the portfolio

We still haven’t gone strongly positive in the area. We’ll have to see those momentum measures strengthen before we do that. But that’s an example of how we use the quantitative screens to help us with the macro composition and the risk characteristics of the entire portfolio.

Q: Would those momentum factors have helped you pick Apple, for example?

We actually did pick up Apple Inc as a bargain stock, not at the very bottom when it was around $7 or $8, but at around $12 or $14, once it started to turn the corner and begin beating earnings estimates. We got out of it when it hit the new highs at $700 or so, and came back into it once it corrected.

We rarely catch a stock on the bottom, and we rarely ride a stock to the very top, because we are waiting for those momentum indicators to start to turn around. We catch that sweet spot in between.

Q: What is your team composition?

Don Nesbitt and Mikhail Alkahzov have been on this portfolio since inception. We have added several ancillary members to the team, which has helped broaden the fundamental analysis. Even though the quantitative process gives us lists, we want to dig deeper and understand why a given stock became so cheap or so rich, and how the fundamentals change and corroborate the momentum.

We also have research teams that add their expertise. But the primary process is still in place, and Don Nesbitt and Mikhail Alkahzov are the ultimate decision-makers.

Q: What is your portfolio construction process?

Portfolio management is all about risk control. There will always be industry events and stock-specific events that no one can foresee, and you need to manage that risk.

We are benchmarked against the Russell 1000 Value Index, and so we like to have the risk characteristics of that benchmark. We have representation at all times across all 10 sectors, although they may be underweight or overweight relative to the benchmark.

A bottom-up approach determines the sector allocation, driven by where we’re seeing bargain stocks or attractively priced growth stocks. However, we will not have more than 4% or 5% in any sector long or short relative to the benchmark.

Our strategy is to always have 55 to 70 individual names in the portfolio, with no more than 5% in any one name. We also look at how these stocks interact. We use a portfolio management statistical tool to understand the interaction and the relationships among all these stocks, and to produce a predicted tracking error for the portfolio as a result of the risk model and the statistics.

You have to be able to measure risk to manage it, so we measure predicted tracking error. We have historically kept it between 2% and 5%, to understand how much volatility we want to take on for the incremental alpha that we expect to earn on the individual stock holdings.

Q: How do you look at factor bets?

In the portfolio management process we look at our recent performance and try to understand what drove the portfolio returns. We know we have some implicit factor bets in the portfolio—the dividend yield, cash flow yield, or value bias—that we want to tease out, and we want to make sure we are not making any unintended factor bets relative to our benchmark.

We also look at how much stock-specific risk we are taking, and if we are getting paid incrementally to take that risk. We want to make sure that our stock-specific risk is higher than our factor bets.

Q: How do you define and control risk?

Often an investor wants the attributes and the returns to be characteristic of the Russell 1000 Value Index, which is a large cap value product. That is why we built the portfolio acknowledging our exposure to the different risk factors. The portfolio will have a lower beta than the broad market, the idea being that it provides good value conservation, but with a growth component over time.

Our approach is to understand risk within the context of the benchmark, so we measure all the factor exposures and the individual stock exposures. That sets us apart from many other managers who do not look at it as closely. We look at the return we’re getting relative the risk we’re taking, and maximize that return per unit of risk taken.

Q: What drives your buy-and-sell discipline?

On the buy side, we start with our optimization process. Every security adds some tracking error into the portfolio relative to the expected returns that we’ll get from that security, and we try to understand and optimize that.

The second aspect of our buy discipline is what I’d call a common sense approach; we work with a model, making sure we fundamentally agree with what’s coming out of this optimizer. Often we don’t agree, and so we try to make some adjustments where we think risk is being inappropriately measured by the model.

In terms of the sell discipline, we go back to our investment principle that investors are always susceptible to behavioral biases. Since we are buy-side guys, we spend the vast majority of our time talking about the names we buy. But on our sell side also we’ve built in some quantitative measure to help protect us when a particular name disappoints.

Research shows that investors tend to fall in love with their names, so we’ve built in a number of triggers to essentially force us to make a sell decision on a name that we liked when we bought it, but has begun to disappoint in its fundamentals. These tools help prevent us from falling in love and keeping stocks that should be sold.

There are a few other scenarios as well. Sometimes you buy a stock when it is neglected by the market or facing some cyclical issues, and as it goes up in price you will trim it or liquidate it and buy something else.

Our process is a blend of science and art. We use quantitative measures to give us information on 70% of the issue, so we can focus on that last 30% percent with the fundamental analysis process. That’s the art, so to speak, of how we make our final decision. We have rules in place, but you have to have a very strong argument to override those rules.