Q: How has the fund evolved? Could you give us some background information?

Mount Lucas Management was founded in 1986. We started a global macro hedge fund in the mid-1990s and we wanted to own the equity risk premium as part of that fund. After extensive research, we came to the conclusion that most of the alpha was generated by one of two methodologies. We found that deep value adds value and momentum tends to persist. In early 2000, we built a model, which has been running consistently along with our macro hedge fund. Since then, we have adopted only minor changes to our strategy.

It is interesting that when we transported the same model to Europe and Japan, it was running successfully in these places as well. We still run the macro hedge fund, but we also started the U.S. Focused Equity Fund in 2007. That’s the genesis of the idea and how it developed over time.

Q: How does your fund differ from other large-cap funds? What makes it unique?

A main differentiator is that we are benchmark agnostic. We believe that we need to vary from the benchmark to produce significant alpha over the long term. So, instead of recreating a portfolio that’s hugging the benchmark, we deviate from the benchmark – unconstrained alpha. Academic literature has shown that funds with high active share have significantly outperformed funds with low active share historically. We discovered that notion empirically back in 2000.

Q: What are the principles of your investment philosophy?

A core principle is the notion that there is nothing that I know that you don’t know. We believe that the value and the edge of information have dropped tremendously. Unlike many active managers, we don’t think that we can possess an information advantage, so our model is based entirely on published data.

There is a strong behavioral component to our philosophy. We believe that investors make tremendous mistakes in two major ways. The first one is panicking when stocks fall. Usually, the panic is the result of a view that circumstances in the world have changed dramatically. One example is retail, as there is a phobia that Amazon is going to put traditional retail out of business. Store closings further fuel that view. We can hear it everywhere, but the question is whether that’s correct.

An interesting anecdote is that in the five-year period ending in 2017, Best Buy outperformed Amazon on a total return basis, despite the fact that Amazon was supposed to effectively shut Best Buy at the time. The reason for the discrepancy is that the best companies in any sector can continue to perform, even in the face of extreme competition or change in circumstances. So, we strongly believe that investors have a behavioral bias when reading and following the news and often make incorrect judgments on particular companies.

The second mistake is that people tend to be bashful about holding stocks, which produce steady returns for a long time. Stocks with relatively good momentum, low volatility and good Sharpe Ratios tend to be overlooked, because investors tend to liquidate them prematurely. We believe that this mistake can be capitalized on with a more quantitative approach to stock selection.

To summarize, we don’t believe that we can capitalize on any information edge. Our approach is entirely behavioral, based on our view that people make behavioral mistakes regarding the markets, and we have to explore the opportunities in those circumstances.

Q: Could you give us some examples of such opportunities?

During the development of the ETF business, many stocks were combined in baskets. For example, U.S. steel and coal, which are in two radically different businesses, are pulled together in one basket because of particular factor characteristics. We believe that’s an opportunity.

In 2014, when oil prices surprisingly fell from $100 to about $30 a barrel over a 12-month period, everybody sold everything. In various oil-oriented ETFs, a big portion was allocated to oil refiners, who use oil as an input, not as an output. If their margins remain the same, their earnings will continue to do well despite low oil prices or exactly because of low oil prices. That is a perfect example of the investing public selling stocks, when actually the conditions for these stocks are improving.

Such mistakes happen all the time and the effect is repeatable. The difficult part is to go against the short-term conventional wisdom if you don’t have a quantitative approach.

In April 2009, one of our picks was Wyndham Hotel, which became the best-performing stock in the S&P 500 in the next 12 months. These are the value opportunities that we look for in stocks we believe are structurally sound and are trading at unbelievably cheap prices in our estimates. The other types of stocks are those that have already gone up a lot, but continue to rise. In these cases the main differentiators are the high Sharpe Ratio, the good momentum and the low volatility.

Q: How does your philosophy translate into the investment process?

We start with our universe, which is the S&P 500 Index, excluding utilities. Our goal is to develop an equity portfolio with high value added. We discard the stocks, whose earnings have fallen over the previous 12 months. The companies we look for may have negative earnings, but they need to exhibit growth.

So, we narrow down our investment universe to about 350 potential candidates. Then we create two screens – a value screen and a quality screen. The value screen ranks each security on five or six straightforward fundamental factors like dividend yield, price-to-book and price-to-sales ratios. These factors are problematic for many companies, so we assess them individually.

The next step is creating composites, which are based on the ranking of each of these individual characteristics. Our approach is to sum the rankings and look for composite scores over the broad range of rankings. We rank all the stocks in two curves, which are price momentum and volatility, looking for the stocks with good price momentum and relatively low volatility.

Once each individual company is ranked and scored, we buy the top 10 value stocks and the top 10 quality stocks, or a total of 20 stocks, and we hold them for one year. Six months later we screen again and we buy the top 10 value and the top 10 quality stocks and hold them for 12 months.

The process represents a laddered sequence, where we only trade twice a year and hold every stock that we own for a minimum of one year. Importantly, we don’t care about sector concentration, benchmark holdings or about being too long in a particular area, but we care about the kind of active share that we look for. We don’t talk to managements and we don’t do additional research. We take the published financial statement information and translate it into a ranking system to find the most inexpensive stocks.

It is interesting that there’s very little overlap between our top 20 holdings and the top 20 holdings of a typical large cap value fund.  For example, we don’t own any of the major banks, because we don’t think that they present unusual value. But the benchmark has a tremendous number of large banks, while investment managers are afraid to deviate from the benchmark.

The key feature of our fund is that we don’t see any value for producing a return stream that merely replicates the benchmark plus or minus 50 basis points. That’s not our game. Our game is to trade portfolios that are truly different. Since we tend to deviate from the benchmark a lot, it’s not unusual to be 500 or 1,000 basis points under or above the benchmark in a 12 month period.

Q: Have there been any refinements to your model?

We evaluate the model all the time to consider improvements or to see if we are missing something. Since 2000, the only major change has been to add volatility to the momentum side of our model. The original construct was comparing value with pure momentum, but we ended up with too many stocks that didn’t fit the profile that we wanted to establish. That’s why we added the volatility component to the momentum model. That change effectively moved that part of the portfolio more towards quality.

That’s the only major change we have made. The important thing to recognize is that the stocks we buy have a story behind them. Often that story is problematic and we know that. But if their financial metrics continue to perform well despite the problematic environment, we believe these stocks might represent true value.

Q: How would you define the type of value and quality that you look for?

I would call it behavioral value, where stocks have declined because of a story or a perception that has become popular with the broad market. I would call it “story value.” A great example of story value were the military contractors at the end of the first term under the U.S. President Barack Obama, when it was difficult to reach a budget agreement on government military spending. However, when we examined the actual earnings of these firms and what they were producing, it turned out that they continued to be quite profitable, because they were diverse businesses, which created tremendous value.

The managers running the companies read the newspapers as well. They know better than any analyst what’s happening on the political scene and its impact on their company. The good companies adapt to the environment and push their companies in a direction that could give a positive result. This type of story value is an example of an ideal situation for us.

On the quality side, it is the opposite. There are companies that nobody talks about as they continue to produce exceptional results with relatively low volatility. So, we do two different things that catch the behavioral influences, which come in two different ways.

Q: Do you always follow the model or do you question its results?

We check if any of the stocks are involved in an acquisition transaction. If they are, we discard that stock and move to number 11 on the list. We don’t have great liquidity or bankruptcy concerns, because all the stocks are components of the S&P 500. Another important feature is that we don’t get out of stocks halfway, because they might have gone down. We hold the stocks for at least one year. We also do the earnings screen to eliminate the stocks whose earnings have fallen.

In such a portfolio, we don’t pick individual companies, but a basket of companies with particular characteristics. I don’t have any prior knowledge which of those baskets is going to be the best or the worst performer. All I know is that these baskets they tend to outperform over one or two years. Overall, our model is very insensitive to execution.

All the data is derived directly from the investment financial statements. Interestingly, adjusting the data brings your own biases to the process. We all have various biases and we try to avoid them by relying on the published data and the ranking across standard variables with regard to valuation. There has been interesting research lately, which suggests that it is better to look at a composite of value figures independently, instead of at just one.

The notion is that each value calculation has some deficiencies and limitations. By creating a composite of these numbers together, we aim to overcome these limitations. For example, let’s take the book value of Macy’s Corporation. They own a block in New York City, which is worth billions of dollars but is not included in their book value. So every calculation or metric of value has some sort of profitability.

Q: When do you replace stocks in your portfolio?

We only do this twice a year. We usually buy 10 value and 10 quality stocks towards the end of the first quarter and we hold those 20 stocks for a year. Six months later we buy 20 more stocks in the same way. So, we hold a maximum of 40 stocks at any time. It is as if we buy portfolio A and hold it for 12 months. Six months later we buy portfolio B and hold that for 12 months. We hold a maximum of 40 names, but we can have less than 40 names, because we are not averse to having an overlap. If we like Boeing today and six months later we like Boeing again, we can buy it in both portfolios. So, we get a certain amount of concentration, but we are willing to accept that.

Q: What is your asset allocation process?

That process is simple as well. In each 20-stock portfolio, we allocate 80% of the capital to value and 20% to quality. There are 10 names in value and 10 names in quality, which are equally weighted within their respective basket. We rebalance the portfolio every year and we like to keep the equal weighting of stocks. That approach tends to push us down on the cap structure, because we are not asset weighting. That means that we tend to have a slightly lower average capitalization than the index. The average holding is about 3.5% and we can get as high as 8% or 9% or as low as 1% in each name.

Q: Do you have a particular sell discipline?

On the sell side, the discipline is “out with the old, in with the new.” If a stock is in both portfolios and we still want to hold it, then we readjust the allocation. Effectively, when the date comes a year later, we throw out the 20 stocks and buy new 20 stocks.

Q: How do you define and manage risk?

Investors usually target a particular level of volatility and adjust the risk in the portfolio based on the realized volatility. However, we believe that the whole process of targeting volatility injects a level of systemic risk in the portfolio. Investors have many ways to buy securities based on valuation, momentum, etc. But we see that they exit, deleverage or de-risk their portfolio based on one fact, volatility. That’s why there are episodes when volatility just peaks over the marketplace and de-risking portfolios takes over. As a firm, we believe that’s a real problem in the marketplace and we’ve thought and written a lot about it.

Second, we have definitive periods in which we hold a particular stock from a risk-management perspective. So, if the earnings of a company begin to decline over a period of 12 months and their metrics fall apart, it will exit our portfolio at the end of that period. Although we replace the companies on a quantitative basis, we don’t target specific levels of volatility or deviation from the benchmark, or specific information ratios or any other metric that the financial community has developed.

We spend our time thinking about the metrics that we can add to the model. We try to analyze whether there are new ones, with new ideas and research. Our field is pretty nimble in the sense that we restrict ourselves to published data that doesn’t have any complicated biases. We believe that the best way for us to add value is to create a distribution of outcomes through few stocks with tremendous performance. We see the value trap as one of the biggest risks for investors.

Overall, we believe that there are certain behavioral aspects that don’t change overnight. The key feature of quantitative investment is it’s not necessarily the model itself, but the conviction.