Investing In Uncertain Times And The Significance Of Numbers

Summary

• Stock prices follow rhythms or patterns based on time frame.
• A significant market move, one way or the other, might be extremely rare in any single week but common in long term time frames.
• Stock prices are more volatile than the performance of the underlying businesses they represent. That offers opportunity.
• Some investors don’t need the numbers. Some have great intuition. I don’t.
• Most investors have more confidence in their intuition than is justified by their performance. Investors tend to confuse a bull market with great intuition.

Events that are quite improbable on any given day or week, are actually extremely probable in any fifty or one hundred year period. It is crucial that an investor be clear about which time frame he or she is focused on, or seeking to profit from, and have a thorough understanding of the characteristics of that time frame.

Some charts of the S&P 500 illustrate the point. The first is the chart of primary importance to the long-term investor who wants to take ten to twenty years positions. It is a yearly chart plotted against its three-year simple moving average. The chart indicates two advantageous times to invest: halfway through 2002 and halfway through 2008. Sell points would be determined by periods when the price line traded at a unusually high premium to the red line. There were at least four of those: 1999, 2014, 2018 and 2019.

Source for all stock graps: TD Ameritrade Thinkorswim

If an investor instead wanted to capitalize on quarterly stock price performance, this is what the chart would look like.

That’s the time frame I’ve chosen to focus on because my research concentrates on financial statement trend analysis, and public companies file quarterly. Using this chart, an investor would buy when the price line was at an unusually low level relative to the red, smoothed three quarter average price, and sell when the stock traded at unusually high levels relative to that line.

Finally, here is the weekly chart, a chart a short-term trader might use or at least consider.

Again, the objective would be to buy when the price fell to an unusually low level relative to the smoothed moving average, and sell when the price rose to an unusually high level. An investor could also short based on this analysis, although there are probably lots better instruments to short than the S&P 500 given its inherent bias toward quality companies.

That’s what is sometimes called a mean reversion strategy. There’s also a trend following strategy where an investor buys whenever the price crosses the moving average, and sells whenever it falls below. And there are derivations where the two are combined, but that’s a complicated subject perhaps suitable for an article all its own.

My point is this: time frame matters and once set and clearly understood, the strategy part, the tactics become relatively easy, at least in principle.

Since quarterly is my chosen time frame, I can tell you the maximum, median and mean quarterly deviation over the last thirteen years between price and that smoothed moving average to within three decimal points. I can tell you that for the one hundred most profitable, low-debt public companies – the top one percent -- traded in the US, and for the one hundred with the worst financial statement performance, at least those that have not been liquidated in bankruptcy. And I can do the same for the major banks, the sector perhaps most vulnerable in an economic contraction. I can tell you how much you would have made in percentage terms buying the best or shorting the worst of these companies over the last thirteen years, or one complete market cycle, including the 2007/2008 downturn and the subsequent recovery, and how long on average you would have had to hold that security to earn that return. It takes me a minute or two to generate this data. I do it daily using computer programs I’ve developed over a number of years. That’s my edge. That’s how I make my living.

What Could Possibly Go Wrong?

A primary risk is the assumption that the landscape through which our investments travel is relatively stable. If you assume that the last twelve years can be extrapolated into the future, buy the dips. However, a study of any twenty year time frame over the last hundred years would suggest that assumption is risky. Another twelve years of bull market is not impossible, just improbable.

In my early years as a stock broker, I think I was about 24 years old, I had a client by the name of Pat Sheridan who owned mines all over the world. One of his many great aphorisms was, "The classic mistake in business is expansion at the top of the cycle with borrowed money." Pat was right, but even more important in business, is a clear objective. A clear time frame.

There is no risk-free route through the investment landscape in a state of perpetual bliss. The risks in this strategy lie mostly in the assumption that history will repeat. Extreme events – war, a virus that kills tens of millions of people, extreme climate change, a total collapse of the banking system – the Black Swan events that tend to be dismissed as improbable, once in a lifetime events, or once in a hundred or a thousand-year events, defeat the system. Strategies based on one hundred year pricing will perform better in such a scenario than strategies based on fifty year events. Strategies based on fifty year events will outperform systems based on ten year events in Black Swan scenarios, etc.

There's the risk of losing money, but there’s also the risk of missing out. The longer the time frame that an investor focuses on, the greater the opportunity risk. The less responsive you are to opportunities because your system is looking for the fifty year maximum (or even median or mean) deviation from moving average, the fewer the opportunities you will take advantage of.

If you want to avoid all risk, invest in two-year treasuries.

That’s why I say that in investing the difficult decision is time frame. One of my favorite all time quotes is by George Marshall, Chairman of the Joint Chiefs of Staff during World War II.

If you get the objectives right, a lieutenant can write the strategy.

In investing, substitute the word "strategy," for the phrase "time frame." Once time frame is decided, strategy and tactics that have proven themselves over time, whether those of Buffett or any of a slew of other successful investors, become easy to identify by reading a few books or Seth Klarman annual letters. Of course, the execution is another matter. Execution is never easy, and the greatest challenge to execution is emotion.

The one common characteristic of all great investors, no matter the specifics of their strategy, whether it is Buffett, Tepper, Simons or Soros, generate a substantial part of their return by benefiting from, rather than being the victim of, market volatility. The market is more volatile than the underlying businesses it represents. Money can be made from the difference or spread between the stock price and the performance of the underlying business because that pendulum is constantly swinging back and forth.

Volatility is a fear gauge. A collapsing market is a market where investors sense inordinate risk. A volatile market, a market with directionless up and down movements, which rarely last for long, indicate that investors don’t know how to assess risk.

The Role Of Quality (Compounders)

When I started designing investment software, the purpose was to scan all public companies looking for signs, for emerging trends, that indicated that the underlying business was improving or deteriorating. Gradually, over a period of years, I’ve placed greater and greater emphasis on the ability of a company to compound free cash flow. In recent weeks, with the market turmoil, my focus has turned exclusively to compounders. Why? In an uncertain, unpredictable world, cling to any certainty you can find. Compounders are the most predictable of all public companies.

What is a compounder? A company that, year after year, through thick and thin, increases its free cash flow per share with minimal debt and minimal capital expenditures. There are about a hundred public companies with these characteristics. They have protectable competitive advantages, or moats. They are survivors. Mastercard and Visa. Monster Beverage and Simulations Plus. The cream of the cream of US public companies. But even those companies have a one-year stock cycle marked by wide fluctuations in relation to their moving averages.

And of course, even these companies, even the exceptional companies, don’t increase free cash flow every year. They are human enterprises, not machines. But they come close. Visa, one of the best-known compounders, for instance, generated less free cash flow in 2016 than it had in 2015. In 2017 it jumped right back on trend. Which, by the way, illustrates another investment characteristic of these companies. They are best purchased during periods of adversity or substandard growth. In these times, the crucial risk factor is whether fluctuation is based on temporary blips that all companies experience, or a basic change of character. That is too complicated a subject to discuss here, but the software utilizes thousands of formulas that weigh the constituent components of return on assets by free cash flow and total liabilities trend to reduce that risk.

My pricing algorithms assume history will repeat. They work best with companies that don’t change character, or suffer serious reversals. Minor reversals or bumps in the road are fine, good even, for the patient opportunist. Perhaps that is true of all investment strategies. Of those consistent, exceptionally profitable companies, the software will take the largest positions in the most volatile. The more the financial statement trend can be counted on to return to trend, and the more volatile the stock of that company, the better the pricing algos work.

As for short sales, the software looks for the opposite. It is constantly searching for companies with lots of debt, high average capital expenditures and no real earnings. The software is ambivalent on revenue growth which does create some drama in my overall returns. The Shopifys of the world, the companies with stocks that keep going up despite never making any money, are a fly in the ointment so to speak. But every dog has his day. (I call my short research the Dirty Dog report, and from time to time Shopify has featured prominently).

Implications In The Current Market

The economic and market recession of 2007-2009, often described as the worst recession since the Great Depression, offers useful numbers in determining the maximum, maximum and mean deviation from three quarter simple moving average. After running hundreds of backtests against those numbers, the software arrived at the relationship that would have generated the highest returns.

Here’s a portion of the computer printout of the results:

The results:

• An average purchase price of $178.53.
• An average sale price of $254.94.
• An annualized return of 19.4%.
• Average months held per position 26.5 months.

The objective of the model is to buy, on average, once per year, and to sell once per year, over hundreds of different stocks. It just so happens that with the S&P 500, the average position was held for 28 months.

If, for instance, the parameters are adjusted to focus on the worst periods, the periods of extreme weakness in the 2007-2009 market, the annualized returns would be lower:

• An average purchase price of $178.53.
• An average sale price of $254.94.
• An annualized return of 15.7%.
• Average months held per position 51 months.

The profit per position was actually higher under the more stringent criteria, but because fewer positions were taken, and the average position held longer, the annualized return was actually 24% lower.

The lesson – more stringent criteria can translate into lower returns because of opportunity cost. Fewer positions taken, held longer, mean lower returns. However, should we enter an actual depression, the numbers are likely to be very different.

The implications of the historical record of the S&P applied to current simple moving average: buy under $277 and sell over $327. For complicated reasons, each position the software takes is 0.5% of the total portfolio, although if for reasons of volatility a stock trades for several quarters at the low end of its historical relationship to mean (or to three quarter simple moving average) a position in any one security can reach several percent.

I should note that this is all theoretical. We don’t take positions in the S&P unless we don’t see any company with strong and improving financials, attractively priced, and we have short positions that need to be hedged. We maintain a 50% long, 50% short portfolio. In those few instances where the S&P is attractively priced and we can’t find suitable individual securities, we take positions in nine-month to one-year, slightly out-of-the money, calls. A couple of times, we’ve also written puts. That’s a whole different discussion.

Analyst’s Disclosure: I am/we are long MNST, V. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

We are constantly entering and exiting positions based on this analysis, both long and short. We maintain a balanced long/short portfolio. Do your own research. Our research can help you narrow the focus of those efforts but unless you are establishing highly diversified, long/short portfolio, that includes a substantial number of positions not mentioned in this article, it should not be your sole source of information.