Posts filed under “Quantitative”

Single Variable Market Analysis is for Losers

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

When is ‘this time’ really different?

    My Sunday Washington Post Business Section column is out. This morning, we look at a famous aphorism from Sir John Templeton. The print version has the hedder When is ‘this time’ really different? while the online version used the fuller Investors must recognize what ‘this time it’s different’ really means. The column tries…Read More

Category: Apprenticed Investor, Psychology, Quantitative

Bull or Bubble?

Source: 361 Capital   This chart comes to us via Blaine Rollins of 361 Capital. I find it provides great context for the current markets, especially given the amount of bubble chatter we hear these days. How common are double-digit equity gains? How often do we see markets up 20 percent or more in a…Read More

Category: Investing, Markets, Quantitative

Benoît Mandelbrot, Father of Fractals

IBM and http://IBMblr.Tumblr.com celebrate the life of Benoit B. Mandelbrot, IBM Fellow Emeritus and Fractal Pioneer. In this final interview shot by filmmaker Erol Morris, Mandelbrot shares his love for mathematics and how it led him to his wondrous discovery of fractals. His work lives on today in many innovations in science, design, telecommunications, medicine, renewable energy, film (special effects), gaming (computer graphics) and more.

Category: Mathematics, Quantitative, Video

AQR Capital’s Cliff Asness

Leverage. Derivatives. Shorting. The three “dirty words” of finance, according to this week’s guest, became a regular part of our vocabulary after the 2008 financial crisis, but how can investors get back to “clean” investing principles? Our Financial Thought Leader this week is Cliff Asness, Managing and Founding Principal of AQR Capital Management, a global investment management firm which runs hedge funds, mutual funds, and a diversified collection of investment strategies. In this rare interview, he’ll discuss the three legs of his “investment stool” and the tools we can use to diversify our portfolios.

Category: Quantitative, Video

90% of Everything is Crap

Source: Mental Floss     As someone who has spent his fair share of time debunking nonsense, I love the elegant way Theodore Sturgeon trashed this anti-SciFi trope in the March 1958 issue of Venture: “I repeat Sturgeon’s Revelation, which was wrung out of me after twenty years of wearying defense of science fiction against…Read More

Category: Philosophy, Quantitative, Rules

US Quantitative Primer 2013

Last week, I mentioned Merrill Lynch’s Market Analysis Technical Handbook. I was somewhat smitten by the wire house attempt to explain the basics of technicals to a broader layperson audience. Several BP readers at Mother Merrill (as she used to be known) directed my attention to another annual release: US Quantitative Primer 2013. It is…Read More

Category: Analysts, Digital Media, Quantitative

Putting Investor Bearish Sentiment into Context

Individual Investors Are Not Buying It Click to enlarge   Lots of people have been discussing how negative investor sentiment is, showing the chart above. It shows markets making new all time highs as expectations that markets will be higher six months hence is at a mere 19% of AAII respondents. (See Individual Investors Are An…Read More

Category: Contrary Indicators, Investing, Quantitative, Sentiment

The Dangers of Non-Modeled Narrative Story Tellers

  “He can take a model and turn it into a narrative”   Right after the election, Felix wrote a post “When quants tell stories.” Clever as it was, I had issues with the underlying premise – namely, that the value of Nate Silver’s modeling lay more in the narrative tale as told by Silver…Read More

Category: Investing, Mathematics, Philosophy, Politics, Quantitative, Really, really bad calls

Michael Belkin 2:30

Category: Quantitative, Think Tank