Stocks & Commodities V. 30:4 (42-48): Interview Market Kinematics With Stan Harley by Bruce Faber and Jayanthi Gopalakrishnan

Stocks & Commodities V. 30:4 (42-48): Interview Market Kinematics With Stan Harley by Bruce Faber and Jayanthi Gopalakrishnan
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Interview Market Kinematics With Stan Harley by Bruce Faber and Jayanthi Gopalakrishnan

Stan Harley’s career has spanned investment advisory services, defense/aerospace, and military service. A former naval aviator in various fighter aircraft, Harley attended the US Navy’s fighter weapons school better known as “Top Gun.” An investment advisor since 1991, he has developed mathematical algorithms for trend assessment, determination of cyclical functions, statistical analysis, regression modeling, and time-series forecasting. The chief investment officer for a Dallas, TX–based hedge fund, Harley provides support to institutional managers, individual investors, and traders, and since 1995 he has published The Harley Market Letter, which analyzes the stock market, bonds, precious metals, crude oil, housing, unemployment, and casino gaming, with a focus on the analysis of market kinematics, the study of market movements. Further information can be found at

Stocks & Commodities Jayanthi Gopalakrishnan and Staff Writer Bruce Faber spoke with Stan Harley via telephone on January 31, 2012.

Stan, how did you get interested in analyzing cycles?

My background is in engineering. I was also a naval aviator. My core areas of engineering focused on harmonic oscillations, solid materials, and fluid mechanics. I applied my expertise in solving complex engineering problems to the study of financial markets. When I started getting involved with the markets and looking at charts, I noticed that the oscillations were sometimes regular in their recurrences and sometimes not. I found it both baffling as well as a challenge, so I had to try to solve the puzzle. That is how I got into it.

What do you look for first when you try to identify these cycles?

I always thought there had to be some kind of mathematical approach to solving the cyclical business we see in the markets, so I look at a whole host of things. I look at a data series. I look at the ups and downs. I start by doing a visual inspection and a detailed statistical analysis. I do some extensive regression modeling and from that I extrapolate my time series forecasting of the recurring series. It is both simple and complex.

That said, too many people try to make the approach to market analysis overly simplistic. I think that starting out we should be aware that cycles are the essential algorithm in predicting how long a trend should run, when to expect reversals, and ultimately, how to exploit this information to make a profit. I think it is a top-down approach.

First, I look at a long-term chart and try to get as much data as I can. I try to identify the longest, dominant cycles and then work down to shorter and shorter time periods. I have found that almost all cycles have subcycles embedded within them, usually two or three, and I call these subcomponents “alpha,” “bravo,” and “charlie.” I keep a detailed database of cyclical functions that I have under study.

I think that cycles have their grounding in Fibonacci numerology. I have found that 99.9% of cycles can be derived from Fibonacci numbers. That doesn’t mean just the straight Fibonacci number itself, but some mathematical multiple or derivative of that number. I see these same recurring rhythms outside of the financial markets. I see them in such economic statistical databases as unemployment and I see them in commodities. I even see these cycles in the casino games. Actually, I have made a detailed study of the games.

How did you find these Fibonacci correlations? Was it just by observation and trial and error or something else?

First it was just by inspection, and then it was by doing statistical analysis. Then I would think there had to be some math that could define these things more accurately. So I went through thousands of attempts, trying to find formulas that would fit cyclical functions. I did this until I finally found an approach that seemed to work.

What compounded all of this is the fact that cycles are not always a fixed beat. For example, we can look at a price chart and we can see two, three, maybe four iterations of a fixed-beat cyclical function. Then it seems to disappear. That is not to say the cycle will evaporate altogether, but it may skip a beat or two, only to recur later on, or more often it will either expand or contract and then resume its regular beat, or it may just synthesize into a new cycle. Cycles are not simple. It really is complex, but not so much that mortal man can’t figure it out.

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