Can a global resources fund be right for some of your clients? While the mere mention of digging for oil, copper, coal, or gold makes some investors want to run for the hills, others may want to embrace these cyclical, tangible, infrastructure building blocks, and include them in a diverse portfolio.
Frank Holmes knows this firsthand. Chairman, CEO, and CIO of U.S. Global Investors, Inc., Holmes directs the investment team's activities at the firm's Global Resources Fund. "I look upon myself as a music conductor, and I know the musical score for Beethoven's Fifth. My job is to bring out the best of each of the different players on the team, and make sure the tempo and the passion [going] into playing those instruments is of the utmost. So, I am involved with it every day." That attention from the maestro has helped this no-load fund earn Standard & Poor's five-star overall category ranking, with a four-star style ranking for the one-, three-, and five-year periods.
Morningstar rates this fund five-stars for the three- and five-year performance periods and four-stars overall. Both services agree that the fund's performance is exceptional, with Morningstar saying it has "an outstanding record."
According to S&P, the fund had an annualized five-year total return of 25.51% versus 9.15% for its peers in the S&P 500 Energy Sector Index. S&P ranks the fund 17th of 4346 funds in the domestic equity fund category over five-years.
I've noticed that your U.S. Global funds are team managed. Why? Because we have people on the road all the time. If you have someone on the road and not watching all the holdings during the day they can miss opportunities because different stocks, different sectors in different countries have so many moving parts [that] can have an event that can change the success of the portfolio.
So someone can watch the store? Did you see our report that shows [a map of] where we are around the world? The quarterly shareholder report shows you all the countries. Right now we have someone at a coal conference in Vancouver, two weeks ago I was in China, the week before that, one of our other analysts was in Mongolia, and China, two weeks from now one of the other guys is going to Kazakhstan, someone is in Mexico, Argentina, and Chile. What you want to do is be able to communicate back and forth with someone who knows our models. Someone knows what the buy/sells are for how we value stocks statistically and with fundamental analysis. That way someone is always [watching over] the portfolio.
In terms of Global Resources Fund, you've got three on the team that you list on your Web site (Ralph P. Aldis, Brian K. Hicks, and Evan W. Smith). How hands on are you? Daily. What's important here is that we create these models-it's a matrix of models, and that allows me to be able to question.
Can you tell me a little bit about the investment process for Global Resources? It starts off with Mondays, [which are] always top down, macroeconomic analysis. We look at the G7 and what we call the E7 countries. The G7 everyone knows about; the E7 are for us the seven most populated countries-over 100 million-on which we can get economic data [including] China, and India. We look at their economic development industrial production numbers, and GDP numbers, and we measure how they concert with the G7 countries.
Other than China and India, who else do you have in your E7? Brazil, Indonesia, Mexico, Pakistan, and Russia. They've made Russia part of what they call the G8, but that was over politics-when it is not even as big as China-so if anyone's supposed to be part of the G7 it should be China. They've been invited but they're not part of it. We take the G7 numbers-the industrialized world-and compare them [with our E7]. We do this analysis and on Mondays we look at the three significant factors that drive our alpha. One is time management. Mondays are always top down macroeconomic days; the other four days are stock picking days. Then there are very defined hours in the day when we do analysis on a section of the world, geographically, and a sector from an industry.
I think that there is a time management process, so we don't respond to every broker that's calling. We call brokers on different sectors based on our thoughts. When an analyst comes to visit us from Citigroup, etc., we don't just listen to their recommendations; we do analysis of their recommendations. We're very brutal that way. We hold them accountable. We'll say "Let's take a look at oil holdings you cover, over the past five days, 20 days, and 60 trading days. Are your recommendations above or below the index you cover? We want to know why your stocks are below-what did you miss? What's your process for missing them? What's your process that they'll turn around?" We're really consumed with the investment process-that there is consistency in every analyst we talk to. When you go to these meetings quite often around the table people just take notes on what the analyst is recommending and take it at face value; we turn it back on them.
You're questioning the face value and making your own analysis of what they said? Correct. We look at the best-to-worst of all our holdings every day, for the week, for the month, and for the quarter. We try to assess leadership patterns. If I come back to some basic knowledge about people with high IQs-it's not because of a better grasp of history, it's because they have better pattern recognition. We have created systems that try to identify patterns, which are in concert or in great contrast.
Every week in our Investor Alert at www.usfunds.com, we rank all the indexes and all the Global countries best-to-worst for the week; best-to-worst for 20-days; and best-to-worst for 60 trading days, which is a 90-day moving average. We will then take a subset and look at the sectors, the industries, and our stocks. We can take a glance at our holdings at any time and say: that's beaten the index; that's not beaten the index, for the week, for the month, and for the quarter. We ask questions. We want to know where our leadership is coming from, where our laggards are coming from, and ask, should we add more to our laggards or is there a fundamental flaw here, should we be getting out? Should we be selling our leaders, or should we be adding to them? And the stuff in the middle, we see how it migrates-does it float to the top or does it start to sink to the bottom?
Because there are so many events taking place in the marketplace we want to apply Pareto's Law (the 80/20 rule). We apply that concept and another concept called "analytical hierarchy processing," which is really fascinating because four years ago when I was building this multiple process, there was hardly anything using analytical hierarchy processing. [It has] you pair everything–hot and cold–and prioritize, biggest to smallest, fastest to slowest. That process allows you to see pattern changes. It's almost like music; anywhere in the world if you know how to read music, and play the piano or violin, you can all read the same music sheet. You may be Mandarin, and I'm Italian, we can't talk to each other, but we can both play the same musical score. That's what I was trying to evolve. That's how we operate and think.
We use time management, and we use cycles. We're very, very caught up in managing expectations, and there's a wonderful line from Warren Buffett–you know everything is about earnings expectations–if they didn't meet expectations the stocks fall. Right, even if they do [meet expectations].
If they do [meet expectations], they may fall, and if earnings were exceeded [more] dramatically than expectations from First Call, they may rise. Warren Buffett said if you want to have a long-lasting marriage have low expectations. We said-how do you manage expectations best? In cycles. We can do probability analysis with cycles, and we use "mean reversion" which is a classic concept that everything reverts to a mean. We apply seasonal cycles to commodities in particular. You will find that energy prices, gas prices start to rise from March to June, and oil prices fall, because the east coast heats up; more people are driving, and fewer people are heating up their houses because of warm weather. Happens every year. You can have exogenous factors that come in and distort it but that has a high correlation. We also find that gas prices pick up in September because quite often hurricanes hit the Gulf and they shut down the refineries. So you have to understand seasonal weather patterns when looking at various commodities like energy.
You go back how long in your historical cycles? We have up to 30 one-year snapshots overlapped. Right now we're looking at energy prices. One of the charts we're looking at is 15 years of data overlapped every year, and you can see a real pattern. We look at that to try to manage the expectations. The patterns replicate themselves 60%-70% of the time; that helps us manage our expectations.
When we step out of the seasonal expectation cycle, we go to the four-year presidential election cycle. America is one-third of the world's GDP. Very important. Studies show that 70% of the time, the stock market is down or sloppy in the first two years of any president. And, 70%-80% of the time it's up in the third and fourth year of any presidential term. Doesn't matter of it's a Republican or Democrat; that's the pattern and it has close to an 80% probability. We ask ourselves "where are we in the presidential election cycle for the largest economy in the world?" Well, first year, guess what, markets are usually sloppy. Rising interest rates are slowing down the economy. That's part of our managing expectations.
Third, in cycles, we look at what's important in those E7, emerging countries. Is there a policy to build infrastructure? The most populated countries that are building infrastructure will absorb commodities at a rapid pace. We follow our E7 countries and look for what's called a Kuznet cycle. A Harvard professor [who] died in '85, he was the father of the GNP number. He created that factor. He also noticed that when an economy was emerging, they started building highways, railways, shipping tanks, and skyscrapers. They all used a lot of steel, copper, they used basic commodities, and for those particular infrastructure builders the cycle lasts for 20 years. That's what gives you a secular bull market. When you have the largest economies in the world with policies to build infrastructure, then you have a basic sponge on commodities, a demand cycle. We look at those factors.
When we look at individual stocks, we look at the life cycle of a resource stock. They go through a very defined three stages: exploration, development and production. There are three different stages of risk associated with those three stages-very defined. You look at the life cycle of a product-a piece of software-it's usually good for three years, then it's gone, worth nothing. PCs are good for three years, [then] buy a new one. Cell phones are now almost a year, [then] get rid of it, get a new one. You have to look at the product life cycle, and we look at the resource cycle for an individual company. That's part of our theme: it's time management, it's looking at these cycles, and then it's quantitative analysis.
You mean quantitative analysis of the type you were just describing? Quantitative analysis meaning we use a lot of statistics. We look at metrics. An ounce of gold in the ground in Nevada is worth more than an ounce of gold in the ground in Venezuela because Chavez has got political risk. [It's the] same gold sold in the marketplace, has the same dollar value, but [the cost of] an ounce of the gold, because of political risk, changes. We look at the whole world, all countries, and we have a dynamic model. We apply a [qualitative] SWOT (strengths, weaknesses, opportunities, threats) model and look at management, culture, strategy and talent-intellectual capital. We've used this qualitative, quantitative modeling and then we use price-risk modeling. It's called "volatility timing." It's not market timing, but volatility timing.
Then it's volatility of the actual commodity itself? The commodity, the commodities sector index, and the individual stocks. They all go to overbought/oversold. When we see things mathematically overbought over a 60 day rate-of-change (90 calendar days/60 trading days), above [their trading range] 68% of the time, we raise our cash levels to 10%: when they go two standard deviations, we raise our cash levels to 20%. If we have a stock that goes way above its average price over five years of data, or 10 years of data, that goes up two standard deviations over a 60 day rate-of-change, we will sell some of those holdings. What happens is that actually our overall turnover of the portfolio is less than our peer group.
I noticed that.
That's because we take core positions that are undervalued relative to all the other stocks in the universe and then use mathematical trading to realize times to sell and to buy. What do we find? Money flows always come into a fund when it is up two standard deviations-huge money comes in; it's the worst time to invest, mathematically. It's when we build our cash positions.
When you say that you use mathematical trading to decide when to sell or buy, are you keeping a position of so many thousands of shares and edging it up and down, according to the timing? Yes, to stress levels and to patterns of the market-so it's another way of looking at patterns. Type into Google "volatility timing" and you will see so many pages of PhD dissertations on markets and volatility timing. You'll see very little on market timing compared to studies on volatility timing. The whole premise of The Chicago Board of Trade is volatility timing. That means that if stocks go up one or two standard deviations, you sell the premium-that's the whole basis of it. What it says is that trying to guess when something is going to go up one sigma or one standard deviation or two standard deviations over 60 days or over 12 months is next to impossible. It's very, very difficult to say it will go up–with 100 [%] confidence-it's going up over the next 60 days. But if it's up one standard deviation or it's up two standard deviations over 60 days, there's an 80%-90% probability it's going back to the mean.
Again, that regression or reversion to the mean? Correct. But you have to wait until it's expanded itself or fallen below, way below, its mean. When do most of the redemptions happen? When the commodities are down one or two standard deviations from the mean. What happens is that our cash levels start to shrink because people are redeeming; that's fine-we give them back their money; we charge them for it but our long-term investors are not hurt. That helps buffer some down days. We may be up to 20% cash, but then as the market corrects, that cash buffers our holdings.
That's why you keep that much cash? Yes, but we'll go down to two percent
And that would be in an opportunistic way? Yes, that would be because it's down two standard deviations.
You'd have been buying? We're investing. Because mathematically we have an 80%-90% confidence factor that things would revert back to the mean, which could be 12%-20% over the next 60 days. That's the opportunistic mechanism of how we use volatility timing models, and we use many of them. We use weekly, monthly, quarterly models and we created a dynamic process for our algorithm that allows us to look at sector stocks and manage cash flow for the investor. That, roughly speaking, is how we take a look at the big picture.
The other thing we do, [and] why we believe we're in a secular bull market for commodities, is that almost all commodities are priced in U.S. dollars. In the latter part of the '90s, the strongest currency in the world was U.S. dollars. That means that commodities prices were at an all-time low so no one was exploring or developing for commodities-for oil, for gas, for coal, for any of them-because prices were so cheap you couldn't get a return on capital. During that period of almost a boycott on exploration and development, environmental rulemaking which is very important and which we're big believers in was growing at double-digit rates. Clean air, clean water, [the] Kyoto Agreement. All these things came in during a strong-dollar, weak-commodity-price environment. All of a sudden now, the dollar falls, commodity prices go up.
The inverse effect? People say yes, well the cost now for exploring has gone up 300%; environmental insurance is up 300 to 400%. Who is our best case study to confirm this thought process? The greatest investor of all time is Warren Buffett. Warren Buffett used to say that he'd never invest in natural resource stocks because it was too easy to get into the game and it was too volatile. Well, when Enron went bankrupt and all the pipelines were having great financial difficulty he came in and bought them-the Williams Companies-he became the third largest pipeline owner in America. But he said 10 years ago that he would never invest in resources. Why is that? One of his critical drivers is that barriers to entry have to be high. That's why he bought Coca-Cola. He didn't try to compete with Coca-Cola's brand; [with] their marketing dollars, basically it's very difficult to compete with them. The barriers to entry are high for Gillette, the barriers to entry are high for McDonald's, and the barriers to entry are high for Coca-Cola because their brand is so strong.
