Drummed into applied math students everywhere. It even has its own website, with this gem on how margarine consumption is correlated with divorce rates in Maine.
Should be true enough in markets as well. But in reality, at least in pockets, it isn’t always true. Stocks have always in part been driven by relative valuations. Stat-arb was a big thing some twenty years ago when computing power was starting to be applied to stocks. Pairs trading based on common risk factors makes some sense, Ford and GM operate in the same business after all, it makes sense they should be broadly be impacted by the same broad industry and economy trends. When computing power jumped later, factor investing came to dominate. Grouping stocks based on different attributes has some merit. At their heart, the old quants had valuation firmly in the mix of parameters. Many of the newer factors and machine learning quants have thrown out what ultimately matters. Price – or rather ‘value’. Low vol investing doesn’t care whether a stock is priced for perfection or not. Quality takes no account of what that pricing implies going forward, just that its metrics are stable. Momentum will push junk yields far below default rates and not even notice. As long as the quants see the property they like, regardless of valuation, away they go. They operate as if they are just observers, quietly taking a look from afar and being able to interact without impact. The Hawthorne effect is the phenomena where the behavior of subjects is altered due to the awareness of being observed. The quants in places are not observing any longer, and their impact is self-fulfilling, for a time anyways. There is plenty to be gained from applying stats and metrics to markets, but it is surely important to not take it too far.
You can see this today (September 9, 2019). ‘Value’ stocks are up a lot, not particularly based on the merits of the underlying businesses, but because other types of stocks are down. When stocks are held for their correlation properties, strange things happen. Like the butterfly that flaps its wings and causes a distant thunderstorm. It’s easier to make a case that at least today, retail stock Gap is up big because Boris Johnson chose to shut down parliament. Not often thought of as a butterfly, but bear with the logic here. Boris shut parliament…which catalyzed votes to stave off ‘no deal’ Brexit…which caused Gilts to fall…which drove global bonds to fall…which pushes growth stocks, utility stocks and REITS down…which makes value stocks jump. Seem strange? It should. But the stock market acts this way more and more. Factor investing and ETF baskets that segment stocks into groups are big drivers of prices, particularly when smaller names get larger weights in factors. We need to get back to a more fundamentally driven world.
Factor investing, particularly within the scope of risk premia strategies, has been a popular topic. Vanguard has convinced the investing community that beta can be achieved by buying passive indices and the cost of owning beta should be very low. Investors use risk premia strategies as a source of generating alpha. But …. are people looking carefully enough when evaluating these strategies? Much gets hidden in broad risk and return statistics. We thought we would take a deeper dive into how factors behave over market cycles. Continue reading
Searching Google for “Retail Apocalypse” returns 8.8 million results (in .45 seconds!). For the better part of a decade the sector has been beaten up in the press. The headlines are not unfounded. Former staples of American consumerism such as Toys-R-Us, Radio Shack, and Payless ShoeSource are no longer, while many others struggle to find stable ground. The negative hype surrounding the Retail Apocalypse has created a fog around the whole sector and retail stocks have not been a popular pick amongst active money managers in recent memory.
Behind the retail apocalypse headlines are companies who have adapted to new market conditions, have strong balance sheets, and forward-thinking management. Looking into the fog, we see a shunned sector, overly beaten down valuations, and good potential to seek out value. Our Mount Lucas Focused Large Cap Value currently holds 4 retail names amongst its 36 total holdings. Some may view this as a high concentration of an unpopular sector for a focused strategy which holds no more than 40 stocks. However, our quantitative stock picking algorithms have no such opinions, they are programmed to seek value.
Below are the 4 retail names currently being held in the strategy, each picked for the portfolio on Sept. 22, 2017. Presented are price charts with selection date indicated and resulting price move, as well as headlines from the time preceding selection. Even positive news is tinged with negatively worded headlines. We believe this illustrates the headline fear and peer pressures that all human stock pickers face, as well as the benefit of a non-biased quantitative approach to value investing.
Mount Lucas Focused Large Cap Value Strategy Information
In light of recent market performance, and the corresponding effect on changes in volatility on CTA returns we thought it important to give our views on the topic. Late last year, we were asked by a prospective client to see how one of our trend following models performed over several different stress environments. We highlight one particular stress that was given- a 20% stock market drop over 3 months, with 40% of move in month 1, 35% in month 2, and the last 25% of the move in month 3. A relatively straightforward exercise, but to really understand the nuances of different CTAs relative to our approach, you must look past just the change in level, but consider the potential price paths and volatility over that stress period. The difference boils down to whether one is viewing CTAs as a standalone investment, or as a piece of a larger portfolio, and the role of volatility targeting in position sizing.
In the great series of historical novels about the British Navy in the Napoleonic era by Patrick O’Brian, and the associated movie Master and Commander, there is a poignant scene where the ship is stuck in the Pacific doldrums, sails limp at the mast for days. The crew looks for a scapegoat, a Jonas, to blame for their misery, and they find one in a hapless midshipman. For the good of the ship he grabs a cannonball and jumps over the side. A prayer is said, the wind returns, and off they go. Complacency is the buzzword of the day, but the winds will return as they always do, and the catalyst will be as much a surprise to the market as to the midshipman who found himself carrying a cannonball.
I am habitually early for meetings. The other day I had some time to sit outside the offices of a major investment bank at the start of the business day. It’s an evergreen notion for me … a scene repeated at all the banks and funds all around the world, hundreds of well educated, motivated, energetic young people all chasing the same pool of alpha. And I wonder, is there enough to feed all these hungry strivers? Risk premium is durable, varied and growing, alpha is zero sum and rare. At Mount Lucas, our approach is to own that risk premium in many flavors through our quantitative trading. Just this month we added a new flavor, a momentum based multi asset credit basket. It fits nicely with the other risks in our capital markets allocation. We continue to search for alpha in the realm of long term behavioral biases. I have to admit it’s tough going, particularly in the doldrums, but the opportunities are there, and we await a stiff breeze to see them realized.
“Mount Lucas employs a number of different strategies each with their own investment objectives and risk profiles. Any reference to a strategy or strategies mentioned above may or may not be indicative of all of Mount Lucas’ products.”
There is a saying in our business,”the trend is your friend.” Given the recent downturn in trend following returns, I am reminded of another saying, “with friends like these, who needs enemies.” I was in a meeting recently with a consultant who asked a question we have heard many times over the years. How do you keep clients invested in managed futures in times like these? We know the history, the diversification benefits, the crisis protection, the long volatility profile…but, each time there is an extended period of challenging performance clients look to throw in the towel – because their equity investments are ripping up and managed futures is down. A good question, and the answer, I think, lies in the statement – it is a matter of faith.
Investors in the equity market have faith. They trust, and have been conditioned, that the market goes up over time, corrections are temporary. Participating in the capital formation of companies provides a risk premium to the investor and is an investment in the economic growth of a country.
Managed futures on the other hand is viewed as a trading strategy, it goes long and short, it is typically quantitative based. The perception is…I can’t have faith in a model, it can break. But this perception is misplaced, trend following measures a real economic risk premium in the market, just like equities (see older posts diving deeper on this topic, Portfolio Symmetry, Commodities are not Stocks, Benchmarking Alternative Beta). Investors in this market are rewarded by taking the price risk companies seek to shed. Faith should be in the durability of this premium, just like there is in the equity risk premium. Faith is gained by understanding this risk premium, when it works (periods of volatility and instability), and when it doesn’t (periods of low volatility and stability). Similar to equities, faith should be garnered from the underlying economics of the markets.
Whatever your politics, one can’t help but be fascinated by the Trump phenomenon. One interesting aspect is how pollsters got Trump wrong. We can get insight into that mistake from Nate Silver at fivethirtyeight. To his immense credit, Silver admits he got Trump all wrong, and went back to have a look at the why and the how…. it boils down to a case of algorithmic aversion (see here and here).
Silver’s approach to forecasting, which has been extremely successful across a wide range of subjects, is based on aggregating data from multiple polls to build as big a sample as possible. It is a mechanical, quantitative, model based, statistical method. The model spits out an answer, and that is forecast. But in the Trump case, he over-rode the model. He had a bias, and in this instance he applied it to his forecast, and voila, a bad outcome. Even Silver, a devoted algorithmist if there ever was one, fell into the trap. He did not trust the model, even though it had been right so many times before.