Last weekend, I visited New York City to have a partial knee replacement on my left leg to match last May’s version on the right. I sat for over an hour as the plane was prepared and de-iced in the middle of what seemed to be unending snowfall. As I watched the snowflakes dance about outside my window, I was struck by their quick and random movements. Although a part of the same storm, each individual snowflake seemed to have a mind of its own.
The great composer Tchaikovsky captured this sense of randomness well in the “Waltz of the Snowflakes,” part of his monumental score for “The Nutcracker.” The seemingly haphazard sounds of various instruments invoke a lightness and surreal quality, as runs of harps, flutes, bells, and violins dart in and out of the listener’s ears.
In the more modern world we live in, one of the most popular screen savers is a winter scene featuring a gentle snowfall. If one looks behind the scene to the coding of these visual masterpieces, the heart and soul of the imagery is a simple random-number generator. Each new number thrown out by the device sends the snowflakes darting in unpredictable directions, reinforcing the illusion.
For most of my 50-year career as a financial analyst, I have been told that stock prices follow this same random behavior. When I started in 1969, the die was already cast. Randomness was already an underlying rationale for Professor Harry Markowitz’s 17-year-old mean-variance passive-allocation methodology that still holds sway over most financial advisers. It held that since stock price direction was not determinable, the best result could be accomplished by mathematically achieved diversification between asset classes.
Backup for this way of thinking arrived from others in academia. Professor Paul Cootner released a book called “The Random Character of Stock Market Prices” in 1964, followed by Eugene F. Fama’s 1965 paper “Random Walks in Stock Market Prices” in 1965. Soon after I entered the field, Princeton economist Burton Malkiel authored his seminal work, “A Random Walk Down Wall Street.” The 1973 book is now in its 12th edition.
This formidable array of academics all reached the same conclusion. They all believed that the movement of stock market prices was, like the snowflake’s, random.
Despite its credentials, the theory has taken a lot of hits in the more than 50 years since it was first pronounced. While charts of daily returns over time do appear to approximate the single hump of a random curve, the devil has once again proven to be in the details. Many researchers following up on the early studies discovered on detailed examination that the so-called random curve of stock prices had two “fat tails.”
The existence of these “fat tails” meant that stock price data exhibited more negative and positive returns at the extremes of the curve than a curve would exhibit if the behavior was truly random. These fat tails provided both higher-than-expected opportunities and more devastating losses than index investors would anticipate if prices were truly random.
Then along came Warren Buffet. Following the work of Benjamin Graham and David Dodd in the 1930s, he demonstrated in real time that stocks could be chosen with results that consistently defied randomness. Then, of course, came the work of Professors Andrew W. Lo and Archie Craig MacKinlay, which questioned the very concept of randomness of stock prices and led to the inevitable counterpunch book, their 2002 “A Non-Random Walk Down Wall Street.”
Bringing the story up to date, the latest research of Eugene Fama (yes the same Eugene Fama that said stock prices were efficient and random) and Kenneth French demonstrated that certain factors could explain market returns. Soon there were three-factor, four-factor, and five-factor models. The pièce de résistance for active investors like me, however, came when French showed that momentum, or the price behavior of stock prices, might be the most significant “factor” determining stock market returns. (In the interest of full disclosure, he denied this reported mathematical conclusion when I talked with him personally a few years ago, citing the volatility of momentum strategies.)
The investment world had gone full circle, like a winter storm that just won’t advance. Stock prices were determinable, then they were random, and now it could be argued that they were the strongest factor in determining a stock’s return. Yet, while the theory had folded back on itself, the practice of most financial analysts remains locked in the 1950s world of Markowitz and its “random walk” assumptions.
As I sit recuperating from last Tuesday’s surgery, I have had plenty of time to consider my airport observations on the randomness of snowflakes. Today I’m home, and a blizzard has blown in. Snow is in the air and piling up. There is once again plenty of empirical evidence.
And I can’t help but notice that while the motion of individual snowflakes is random, masses of snow picked up by the wind are often blown (relentlessly and for long periods of time) in the same overall direction—much as individual stocks, once caught up in a rally or a correction, move together in the same direction, higher or lower.
I also notice that the weather forecasters nailed the commencement of today’s storm. Snow began to fall right in the middle of their projected window. A single snowflake’s movement was still random today, just as it was last weekend. But the path and timing of the storm that contained it was predictable.
Most major stock market indexes saw very moderate losses last week. In contrast, bonds and gold both rose in value.
Gold was the top performer of the three major asset classes last week. It was also the best performer since the market topped out on September 20 last year. It has clearly broken out from its sub-$1,200 doldrums and now stands atop the $1,300-per-ounce price line.
As is evident from the gains in bond prices last week, yields kept falling. If this keeps up, it could provide some support for stocks. It continues to provide evidence of a coup of sorts by the bond market against the Federal Reserve.
It has been evident for some time that the market and the Fed have been at odds on rates. As I have been saying for many months, the economy has been slowing and inflation has been flat. These are not the type of environments conducive to rate tightening by the Fed.
The falling yields have been a godsend for the housing industry, at least when it comes to mortgages. Refinancing applications have been at near highs, while bids for purchase mortgages have hit a new high for the last 12 months. Unfortunately, this has not yet shown up in housing stats, which once again tumbled last week. Stock market rallies have historically ridden stronger housing sales higher.
Other economic indicators have also not been particularly strong. Last week, those beating expectations edged out the underperformers by just one. Of course, the government shutdown has also meant that many economic reports have also been shut down.
To date, 19 different reports have been sidelined. With the agreement Friday to take a three-week hiatus from the shutdown, we will see many of these reports issued immediately, while others will await their next scheduled issuance date before we will be privy to their data.
Most of our intermediate-term strategies maintain their wait-and-see attitudes. While none are seeking to profit from further declines like we saw in the fourth quarter, most have moved to the sidelines while the market awaits a retest of the lows in December. The extent of that retest will signal whether the worst is in our past.
So far, the fourth-quarter earnings reports are in-line with last quarter’s. Revenues, on the other hand, have trailed, as has guidance for future quarters.
Still, earnings are rising, jobless claims are falling (at their lowest since 1969), and the stock market is more fairly (dare I say, under) valued relative to where it was last year at this time. Sentiment, while improved, is not overly bullish, and we are entering a seasonally bullish period.
Bespoke Investment Group had an interesting study last week. It showed that the last 12 months were one of the least correlated 12-month periods in market history. Only four 12-month periods in the past were more than 74% correlated—a historically low number.
Still, as they pointed out, most of these periods resulted in strong market gains over periods up to one year into the future, and they recorded very low drawdowns.
Because of my calendar seasonality work, I did worry about a different conclusion that also seemed based on their data. Only one of the 12-month periods that was very similar in daily price changes to the last 12-month period also began in January and ended in January. In other words, not only was the daily price pattern of the two patterns nearly identical, but the seasonal periods covered lined up too.
Unfortunately, this period was the worst performer of the four periods highlighted by Bespoke as most similar to the present. It was the January 1941 to January 1942 period. History tells us that this period was followed by losses for the next one-, three-, and six-month periods. It had a maximum drawdown of more than 16% over the next one-year period ending January 1943. While the other three comparison 12-month periods had one-year subsequent returns of more than 20%, the 1942–1943 period was far lower, although it did generate a respectable 11% return.
In last week’s President’s letter to clients, I pointed out that calendar periods are not the best times to judge active management strategies. Rather, the best way to evaluate an active manager is by accounting for the performance during a full market cycle. Of course, if you are looking to dynamic risk management only because it allows you to sleep at night, then a look at performance during just half a cycle, from the peak to bottom, may be all you need.
Last year was special in that it provided two full market cycles within one calendar year. To take advantage of this phenomenon, we provided our clients with the results of all of our more than 100 dynamic risk-management strategies on our Strategic Solutions service versus the S&P 500 during the last top to bottom (TB), bottom to top to bottom (BTB), and top to bottom to top to bottom (TBTB) cycles—all of which ended December 24, 2018.
Although most of our strategies are not designed to compete with the S&P 500 (they have much lower historical risk and are mostly suitability-based, including Conservative, Moderate, Balanced, Growth, and Aggressive risk profiles). I think you will see in the following chart that dynamic risk management did very well when compared to the growth-oriented S&P 500 during these periods.
As good as these results are, we have to remember that even though the stock market, like snowstorms, can be predictable, “predictable” does not imply “certainty.” When I flew in to NYC last weekend, the city was shutting down as it responded to a winter weather warning. Predictions were for a city shutdown the next day due to a huge snowstorm expected to arrive that Monday morning.
Needless to say, the snowstorm, while it enveloped much of the East, did not reach the city. Temperatures stayed up, and the worst bad weather we experienced was rain.
Stock market predictions and quantitative methods live and die by the same rules as weather forecasts. We listen to them because most of the time they are right, but we know that many times they are wrong.
It is important to remember that these methodologies are all about putting the odds on your side. Whether it’s about wearing your snow boots, gloves, and heavy winter coat or becoming defensive in a raging bull market, these systems use market history to discover rules to put the probabilities of success in your corner. As such, the results they can produce are anything but random.
All the best,
PAST PERFORMANCE DOES NOT GUARANTEE FUTURE RESULTS. Inherent in any investment is the potential for loss as well as profit. A list of all recommendations made within the immediately preceding twelve months is available upon written request. Please read Flexible Plan Investments’ Brochure Form ADV Part 2A carefully before investing. View full disclosures.