Exploiting Behavioral Values
Mount Lucas U.S. Focused Equity Fund
Author: Ticker Magazine
Last Update: Apr 30, 9:24 AM EDT
|In the current age of real-time digital feeds, there is no such thing as an information advantage, according to Timothy Rudderow, portfolio manager of the Mount Lucas U.S. Focused Equity Fund. Instead of looking for an information edge, the fund team explores opportunities created by behavioral biases, relying on a structured process and a quantitative model to generate alpha.
ďWe believe that we need to vary from the benchmark to produce significant alpha over the long term. So, instead of recreating a portfolio thatís hugging the benchmark, we deviate from the benchmark Ė unconstrained alpha.Ē
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. Thatís 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 thatís hugging the benchmark, we deviate from the benchmark Ė 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 donít know. We believe that the value and the edge of information have dropped tremendously. Unlike many active managers, we donít 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 thatís 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 donít 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 thatís 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 donít 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 Ė 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 donít 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 donít talk to managements and we donít 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 thereís very little overlap between our top 20 holdings and the top 20 holdings of a typical large cap value fund. For example, we donít own any of the major banks, because we donít 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 donít see any value for producing a return stream that merely replicates the benchmark plus or minus 50 basis points. Thatís not our game. Our game is to trade portfolios that are truly different. Since we tend to deviate from the benchmark a lot, itís 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?