Investors Likely to Repeat Astonishing Losses

People are likely to repeat what they did in the past.

Do you believe it?

Ok. Let’s see.

Get hired as a Surgeon without the schooling, licensing, and experience and tell them:

Don’t worry about my background – I can do it!

Hire a Surgeon for an operation on you who didn’t earn a medical degree, or complete a residency program, and has no real experience.

Fly to a professional baseball team and tell them you’re going to play this season with no track record of hitting, running, fielding, or pitching.

Tell them you’ve played baseball online – hypothetically!

Is that enough “experience”?

Fly the plane yourself with no flight experience.

Fly a plane yourself after just some simulated experience.

Race a car on XBox, then attempt to the same in a real racecar. I don’t care how many times you’ve driven a Stringray Z06 around Sebring on XBox over 150MPH. The game may be accurate enough that it helps to get familiar with the track, but when you drive it for real, it’s an entirely different experience. In real driving, the shift between gears and turns provide real feedback and the risk is real. You don’t get to press “Rewind” when you crash.

It’s unlikely you’ll survive any of that.

Don’t do it!

Our track records matter. A track record is the past achievements or performance of a person or organization.

Marriam-Webster says:

Track Record: A record of past performance often taken as an indicator of likely future performance.

If you’ve lost a lot of money during bear markets, you’ll probably find some way to do it again. You’ll likely make the same kind of decisions – so will your advisor or investment manager. Our track record of the past is all we have to determine what to expect about what we’ll do in the future. We can say we’ll do this or that next time, but what is, is.

When the markets started declining in October 2007 through March 2009 everyone made decisions along the way. Buying and selling are decisions. Holding on and doing nothing is a decision. You are probably going to respond the same way you did then the next time it happens. It’s what we do. It’s why our resume, background, and track record matters in everything we do. It is what it is.

We can’t get into medical school without the right academic credentials showing we likely have what it takes to make it through.

We can’t become a professional athlete without a proven track record.

We can’t fly a plane without flight experience.

In the investment industry, we say that “Past performance is no guarantee of future results” and that is true. Past performance of a security, strategy, or index is no guarantee of future results or investment success. The market’s past performance isn’t guaranteed to repeat nor is any investment strategy.

An experienced pilot isn’t guaranteed to land safely, but is likely to, and far more likely than one without a track record of safe landings.

A Surgeon can’t guarantee every procedure will have the desired outcome, but one with a track record of success is more likely.

It isn’t certain a great college baseball player become a star professional, but it’s more likely.

Whatever your past results, you are likely to repeat it.

It was your actions and decisions that created it. The same goes for investment managers and advisors. You better know what they actually did because they are likely to do it again.

Market trends are like snowflakes – they aren’t exactly the same. That’s why past performance is no guarantee of future results. But, bear markets have historically unfolded in similar patterns – downward sloping swings of lower highs and lower lows. The magnitude of the swings will be different and vary in length.

Some will try to hold through the losses and tap out when the losses get too large.

Others may exit sooner in the decline, but the fear of missing out will urge them back in, just in time for the next big downswing.

There are many ways of losing a lot of money in a bear market trend of downward sloping swings of lower highs, and lower lows on its way to -50% or more.

I thought of this when I came across my performance chart from 2005 to 2010. The stock index had declined -56%.

I can’t guarantee I’ll execute through another one the same, but I do know I am applying the same risk management methods I did then. Having successfully operated through bear markets, I believe I’m better now than I was then. I know what it feels like as the markets are swinging up and down as it all unfolds. I know how the emotions play out, no matter how mechanical or discretionary the methods used. I know how investors tend to respond to changing trends. I know when mistakes are likely. I know it even better now than I did then.

I know of investment managers who didn’t execute so well through those periods and though they have “new models” now, they won’t know if they can execute them until after it’s over. Some of them say their models are so systematic and mechanical that they remove the emotion. That is far from reality.They may instead discover a whole new kind of emotion they haven’t yet experienced. When they do, they don’t yet know how they will respond.  In fact, the required disclosure for backtested models  and hypotheticals is essential to read and understand:

“HYPOTHETICAL PERFORMANCE RESULTS HAVE MANY INHERENT LIMITATIONS, SOME OF WHICH ARE DESCRIBED BELOW. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE SHOWN; IN FACT, THERE ARE FREQUENTLY SHARP DIFFERENCES BETWEEN HYPOTHETICAL PERFORMANCE RESULTS AND THE ACTUAL RESULTS SUBSEQUENTLY ACHIEVED BY ANY PARTICULAR TRADING PROGRAM. ONE OF THE LIMITATIONS OF HYPOTHETICAL PERFORMANCE RESULTS IS THAT THEY ARE GENERALLY PREPARED WITH THE BENEFIT OF HINDSIGHT. IN ADDITION, HYPOTHETICAL TRADING DOES NOT INVOLVE FINANCIAL RISK, AND NO HYPOTHETICAL TRADING RECORD CAN COMPLETELY ACCOUNT FOR THE IMPACT OF FINANCIAL RISK OF ACTUAL TRADING. FOR EXAMPLE, THE ABILITY TO WITHSTAND LOSSES OR TO ADHERE TO A PARTICULAR TRADING PROGRAM IN SPITE OF TRADING LOSSES ARE MATERIAL POINTS WHICH CAN ALSO ADVERSELY AFFECT ACTUAL TRADING RESULTS. THERE ARE NUMEROUS OTHER FACTORS RELATED TO THE MARKETS IN GENERAL OR TO THE IMPLEMENTATION OF ANY SPECIFIC TRADING PROGRAM WHICH CANNOT BE FULLY ACCOUNTED FOR IN THE PREPARATION OF HYPOTHETICAL PERFORMANCE RESULTS AND ALL WHICH CAN ADVERSELY AFFECT TRADING RESULTS.”

Investment results are probabilistic, not a sure thing, but backtested models introduce a whole new element of risk.

I’d rather bet on someone who has done it and has done it well.

It isn’t a sure thing, but it is stacking the odds in your favor and that’s all anyone can do.

Using the Month of September to Understand Probability and Expectation

probabilty-coin-flip

September is the month when the U.S. stock market’s three most popular indexes usually perform the poorest. So say the headlines every September.

I first wrote this in September 2013 after many commentators had published information about the seasonality of the month of September. Seasonality is the historical tendency for certain calendar periods to gain or lose value. However, when commentators speak of such probabilities, they rarely provide a clear probability and almost never the full mathematical expectation.  Without the mathematical expectation, probability alone is of little value or no value. I’ll explain why.

For those of us focused on actual directional price trends it may seem a little silly to discuss the historical probability of gain or loss for a single month. However, even though I wouldn’t make decisions based on it, we can use the seasonal theme to explain the critical importance of both probability and mathematical expectation.

“From 1928-2012 the S&P 500 was up 39 months and down 46 months in September. It is down 55% of the time in September…”

“Dow Jones Industrial Average 1886-2004 (116 years) 49 years the Dow was up in September, in 67 years the Dow was down in September. It’s down 58% of the time in September…”

Those are probability statements. But they say nothing about how much it was up or down.

First, let’s define probability.

Probability is likelihood. It is a measure or estimation of how likely it is that something will happen or that a statement is true. Probabilities are given a range of value between 0% chance (it will not happen) and 100% chance (it will happen). There are few things so certain as 0% and 100%, so most probabilities fall in between. The higher the degree of probability, the more likely the event is to happen, or, in a longer series of samples, the greater the number of times such event is expected to happen.

But that says nothing about how to calculate probability and apply it. One thing to realize about probability is that it is the math for dealing with uncertainty. When we don’t know an outcome, it is uncertain. It is probabilistic, not a sure thing. Probability provides us our best estimation of the outcome.

As I see it, there are two ways to calculate probability: subjectively and objectively.

Subjective Probability: assigns a likelihood based on opinions and confidence (degree of belief) in those opinions. It may include “expert” knowledge as well as experimental data. For example, the majority of the research and news is based on “expert opinion”. They may state their belief and then assign a probability: “I believe the stock market has a X% chance of going down.” They may go on to add a good sounding story to support their hypothesis. You may see how that is subjective.

Objective Probability: assigns a likelihood based on numbers. Objective probability is data-driven. The popular method is frequentist probability: the probability of a random event means the relative frequency of occurrence of an experiment’s outcome when the experiment is repeated. This method believes probability is the relative frequency of outcomes over the long run. We can think of it as the historical tendency of the outcome. For example, if we flip a fair coin, its probability of landing on heads is 50% and tails is 50%. If we flip it 10 times, it could land on heads 7 and tails 3. That outcome implies 70%/30%. To prove the coin is “fair” (balanced on both sides), we would need to flip it more times to get a large enough sample size to realize the full probability. If we flip it 30 times or more it is likely to get closer and closer to 50%/50%. The more frequency, the closer it gets to its probability. You may see see why I say this is more objective: it’s based on actual historical data.

If you are a math person and logical thinker, you may get this. I have a hunch many people don’t like math, so they’d rather hear a good story. Rather than checking the stats on a game, they’d rather hear some guru’s opinion about who will win.

Which has more predictive power? An expert opinion or the fact that historically the month of September has been down more often than it’s up? Predictive ability needs to be quantified by math to determine if it exists and opinions are often far too subjective to do that. We can do the math based on historical data and determine if it is probable, or not.

As I said in September is statistically the worst month for the stock market the data shows it is indeed statistically significant and does indeed have predictive ability, but not necessarily enough to act on it. Instead, I suggest it be used to set expectations of what may happen: the month of September has historically been the worst performance month for the stock indexes. So, we shouldn’t be surprised if it ends in the red. It’s that simple.

Theory-driven researchers want a cause and effect story to go with their beliefs. If they can’t figure out a good reason behind the phenomenon, they may reject it even though the data is what it is. One person commented to me that he didn’t believe the September data has predictive value, even though it does, and he provided nothing to disprove it. Probabilities do need to make sense. Correlations can occur randomly, so logical reasoning behind the numbers may be useful. For example, one theory for a losing September is it is the fiscal year end of many mutual funds and fund managers typically sell losing positions before year end to realize losses to offset gains.

I previously stated a few different probabilities about September: what percentage of time the month is down. In September is statistically the worst month for the stock market I didn’t mention the percent of time the month is negative, only that on average it’s down X% since Y. It occurred to me that most people don’t seem to understand probability and more importantly, the more complete equation of expectation.

Expectation

There are many different ways to define expectation. We may initially think of it as “what we expect to happen”. In many ways, it’s best not to have expectations about the future. Our expectations may not play out as we’d hoped. If we base our investment decisions on opinion and expectations don’t pan out, we may stick with our opinion anyway and eventually lose money. The expectation I’m talking about is the kind that I apply: mathematical expectation.

So far, we have determined probability of September based on how many months it’s down or up. However, probability alone isn’t enough information to make a logical decision. First of all, going back to 1950 using the S&P 500 stock index, the month of September is down about 53% of the time and ends the month positive about 47% of the time. That alone isn’t a huge difference, but what makes it more meaningful is the expectation. When it’s down 53% of the time, it’s down -3.8% and when it’s up 47% of the time it’s up an average of 3.3%. That results in an expected value of -0.50% for the month of September. If we go back further to 1928, which includes the Great Depression, it’s about  -1.12%.

The bottom line is the math says “based on historical data, September has been the worst month for the stock market”. We could then say “it can be expected to be”. But as I said before, it may not be! And, another point I have made is the use of multiple time frames for looking at the data, which is a reminder that by intention: probability is not exact. It can’t be, it’s not supposed to be, and doesn’t need to be! Probability and expectation are the maths of uncertainty. We don’t know in advance many outcomes in life, but we can estimate them mathematically and that provides a sound logic and a mathematical basis for believing what we do.

We’ve made a whole lot of the month of September, but I think it made for a good opportunity to explain probability and expectation that are the essence of portfolio management. It doesn’t matter so much how often we are right or wrong, but instead the probability and the magnitude. Asymmetric returns are created by more profit, less loss. Mathematical expectation provides us a mathematical basis for believing a method works, or not. Not knowing the future; it’s the best we have.

Rather than seasonal tendencies, I prefer to focus on the actual direction of global price trends and directly manage the risk in individual my positions.

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