If you work in finance, you will invariably come across an example of single-variable analysis. Almost daily, we see terrible examples of this sort of analytic error, rife with logical weakness, yet offered with the highest degree of certainty.

The way this works is as follows: Some ominous data point will be shown, along with a chart that foretells some sort of outcome, often horrific. It usually is accompanied by a chart or table, along with text that warns “IF X OCCURS, THEN Y MUST FOLLOW.” Welcome to the mathematically ignorant, conceptually foolish, money-losing world of single-variable analysis.

A closer look reveals this to be the worst sort of intellectual foppery.

The behavior of markets or economies simply can’t be explained by looking at just one thing. If single-variable analysis were remotely possible, the world’s forecasters would have a much, much better track record. They could simply pick the data point with the highest correlation to the target variable, and make much better predictions than we currently see. (Earnings versus Standard & Poor’s 500 Index performance, gross domestic product versus economic activity, interest rates versus bond returns.) Even a moderately accurate set of directional predictions should be easy.

But that doesn’t happen. Economies and markets are extremely complex. They have a variety of different inputs, including earnings, interest rates, psychology, economic activity, fund flows, taxes, sentiment, momentum, geopolitics, etc. The relevant significance of all of these inputs varies over time. There are periods when fund flow doesn’t seem to matter, like most of the past five years. Sometimes interest rate increases are really important to equity prices, other times they aren’t. Indeed, markets seem to embrace the idea of rotating memes. These different forces have different weights at different times.

In a lot of ways, markets exhibit behaviors quite similar to those predicted by chaos theory — dynamic, nonlinear, sensitive to initial conditions, etc. (For those who may be interested, James Gleick’s “Chaos: Making a New Science,” is a marvelous primer on the subject.). Some years ago, I noted that “quantum physics aside, I think we can all agree that the market is a terrifically complex mechanism for digesting an unholy spectrum of all too many data points. Merely controlling for one single variable over time is a surefire long-term money loser.”

What are a few examples of the single factors that have been making the rounds these days?

GDP: “We have never had a negative 2.96 percent GDP report and not gone into recession…”

Rising Rates: “The U.S. stock market doesn’t do well when interest rates are rising.”

Earnings Surprises: “Earnings are good this quarter, better than expected, and therefore, the market’s going higher.”

New Financial Products: “These new products are being adopted, therefore it means the bull market is coming to its peak.”

Death Cross/Golden Cross: “When the 50 and 200 day moving average cross to the upside (downside), it bodes well (poorly) for any trading vehicle.”

You probably have read one or more of these things recently. They all share the same analytical flaw: Predicting the future actions of a complex system by tracking a single variable. Even when they end up identifying a correlation, we don’t know if it is anything more than a random coincidence.

Let’s just take one example of how foolish it is to reach a definitive conclusion based on a single data input. Look at the chart below: It shows a powerful relationship between per-capita consumption of sour cream with U.S. motorcycle riders killed in noncollision transport accidents. Correlation: 0.916391.

Source: Tyler Vigen

Given that Wall Street’s computing horsepower rivals that of NASA, if that single variable existed, we have to believe it would have already been found, and whatever informational advantages it offered would have been whittled away already. For some fun with ridiculous correlations, have a look at Tyler Vigen’s website.

This is before we even get to George Soros’s theory of reflexivity. Stated simply, the interaction in any complex system of unfolding events and participants’ reactions to those events creates a dynamic disequilibrium that leads to unpredictable, unexpected future results. In other words, our collective reaction to ever-changing data points ruins any predictability that might have otherwise come about from that data.

Theory aside, real world observations lead me to conclude that investors, analysts and economists aren’t going to successfully forecast a stock market or economy by simply looking at a single variable.


Originally published here

Category: Analysts, Data Analysis, Investing, Philosophy, Psychology, Quantitative, Really, really bad calls

Please use the comments to demonstrate your own ignorance, unfamiliarity with empirical data and lack of respect for scientific knowledge. Be sure to create straw men and argue against things I have neither said nor implied. If you could repeat previously discredited memes or steer the conversation into irrelevant, off topic discussions, it would be appreciated. Lastly, kindly forgo all civility in your discourse . . . you are, after all, anonymous.

5 Responses to “Single Variable Market Analysis is for Losers”

  1. rd says:

    “Theory aside, real world observations lead me to conclude that investors, analysts and economists aren’t going to successfully forecast a stock market or economy by simply looking at a single variable.”

    You probably have a different definition of “successfully forecast” than the analysts and economists have. It appears that “successfully forecast” as defined by the forecasters is to get their name and their forecast together in a headline. If they can do that then they have successfully forecast whatever it is. If they have not accomplished that, then their forecast is not successful.

  2. BetaDist says:

    I think you are confusing two different things: univariate analysis versus correlation/causation.

    There’s nothing necessarily wrong with univariate regression. For example, CAPM (Capital Asset Pricing Model) is a univariate model, which shows that asset returns can be modeled as a linear relationship with the market. That’s where we get the “equity beta” coefficient.

    However, confusing correlation and causation is a much bigger issue on Wall Street — and which I wholeheartedly agree with you on this point. The old adage we learn in basic statistics is: “Correlation does not imply causation.” In other words, just because A and B occur at the same time does not mean A caused B. Of course, plenty of Wall Street experts say just that! In fact, in one of your previous posts, you cited someone who claimed that the decrease in stock trading volume was caused by the decrease in the Fed Funds rate! (Of course, he didn’t explain why an increase in the Fed Funds rate was also correlated with a decline in volume too…)


    ADMIN: Thats a measure of valuation, and certainly cannot be used to predict market movements . . .

  3. bear_in_mind says:

    What you’re describing is the business model for CNBC and the myriad offshoot endeavors they’ve spawned. It’s also the formula very frequently employed by websites such as Business Insider, Zero Hedge, and other similar click-bait emporiums. You can only scream “FIRE” so many times before you lose credibility and audience. As it should be.

  4. faulkner says:

    “It’s all there in black and white, but it’s more in the white than in the black.”

    The very complexity of market phenomena – which you well describe – compells many people to seek a simple(r) picture. Called by Piaget a “centration” of attention – a focus on limited information – it is often accompanied by a reliance on authority, it is a child’s attempt at some certainty in the world in which any association will do.

    Only now we know that without rigor school education – which most people are unwilling to engage in – these patterns of “thinking” persist into adulthood. It turns out the higher (Piagetian) stages of mental development must be earned. In short, lacking hard-won developed System 2 meta-cognitive capabilities, most people, most of time, are run by their System 1. And the rest of the educated class often fall prey to them when they are tired, hungry, emotional or distracted.

    So, everyone is sometimes vulnerable to some form of single-variable analysis. “Why did s/he do that?” Why did the stock, bond, ETF, etc. _______________?” “What (single thing) do I need to do to accomplish ____________?” All share in the same perisistent illusion.

    “For every complex problem there is an answer that is clear, simple, and wrong.” – H. L. Mencken

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