Screen Shot 2015-11-18 at 4.55.47 PM

There is a set of trading rules that only fails [true positive] 1/1000 times. However, when entering the trade, there is a 5% false positive rate in screening trades, which can take you out of the trade based on your risk management rules, thereby missing the return. You are only authorised by your partner/manager/firm to take a maximum of 10 trades each year. Trades are screened through this rule set randomly from all manner of securities, [stocks, futures, options]. A trade is screened and it is positive. What is the probability that the trade will fail? Will you take the trade?

Screen Shot 2015-11-18 at 4.55.47 PM

Back in the days of printed newspapers, magazines, and newsletters the acquisition of news and information was easier, or so it seemed.  The reason it seemed easier is that there was much less of it.  Today, with the internet, 24-hour financial media, blogs, and every conceivable method of acquisition, information is overwhelming.  Once I realized that some information was actionable and most of the rest was categorized as observable, then things became greatly simplified.  Hopefully this article will shed some light on how to separate actionable information from the much larger observable information.  As you can see from the Webster definitions below they initially do not seem that different.

Actionable – able to be used as a basis or reason for doing something or capable of being acted on.

Observable – possible to see or notice or deserving of attention; noteworthy.

However, when real money gets involved the difference can be significant.  Let me give you my definition and then follow up with some scenarios.  The world is full of observable information being dispensed as if it is actionable.  All the experts, television pundits, talking heads, economists (especially them), most newsletter writers, most blog authors, in fact most of the stuff you hear in regard to the markets is rarely actionable.  Actionable means that you, upon seeing it, can make a decision to buy, sell, or do nothing – period.

I’ll start by mentioning Japanese candle patterns, a subject I beat to death in this blog over the past few months.  I have never stated anything other than the fact that Japanese candle patterns should never be used in isolation; you should always use them in concert with other technical tools.  Hence, Japanese candle patterns for me are observable information; not actionable.  Only when backed up by Western technical tools can they become actionable.  I demonstrated with in my article Candlestick Analysis – Putting It All Together.

Too often I hear the financial media discussing economic indicators and how they affect the stock market.  Initially it seems they forget the stock market is one of the components of the index of LEADING indicators; in other words, the stock market is better at predicting the economy.  First of all, economics can never be proved right or wrong since it is an art, just like technical analysis.  Economic data is primarily monthly, often quarterly, and occasionally weekly.  It gets rebased periodically and often gets adjusted for seasonal affects and everything else you can think of.  It just cannot reliably provide any valuable information to a trader or investor.  However, boy does it sound really good when someone creates a great story around it and how at one time in the past it occurred at a market top; it is truly difficult to ignore.  Ignore you should!  The beauty of the data generated by the stock market, mainly price, is that it is an instantaneous view of supply and demand.  I have said this a lot on these pages, but it needs to be fully understood.  The action of buyers and sellers making decisions and taking action is determined by price, and price alone.  The analysis of price at least is a first step to obtaining actionable information.  Using technical tools that help you reduce price into information that you can rely upon is where the actionable part surfaces.

I also seriously doubt anyone relies totally upon one technical tool or indicator.  If they do, then probably not for long.  I managed a lot of money using a weight of the evidence approach which means I used a bunch of indicators from price, breadth, and relative strength (called it PBR – see graphic).  Each individual indicator could be classified as observable, but when used in concert with others, THEY became actionable.

I think the point of this entire article is to alert or remind you that there is a giant amount of information out there and that most of it is not actionable; it is only observable.  Sometimes it is difficult to tell the difference so just think about putting real money into a trade based upon what you hear or read.  Real money separates a lot of people from making decisions based upon observable information, no matter how convincing it is.

I am really looking forward to speaking at ChartCon 2016.  The schedule shows me on at 10:30am PT where I’ll talk about the marketing of Wall Street disguised as research and show a couple of things about Technical Analysis that annoy me.

Dance with the Actionable Information,

Greg Morris


Screen Shot 2015-11-18 at 4.55.47 PM

Stats in law has been one of my bug-bears for quite some time. Lawyers, being very language based [as a rule] don’t seem to have any mathematical skills. My own law class, struggle to work out a basic % calculation.

Statistical significance, particularly when looking at ‘causation’ in law, has been a killing field for accurate legal argument.

As the American Statistical Association said,

Researchers often wish to turn a p-value into a statement about the truth of a null hypothesis, or about the probability that random chance produced the observed data. The p-value is neither. It is a statement about data in relation to a specified hypothetical explanation, and is not a statement about the explanation itself. …

Smaller p-values do not necessarily imply the presence of larger or more important effects, and larger p-values do not imply a lack of importance or even lack of effect. Any effect, no matter how tiny, can produce a small p-value if the sample size or measurement precision is high enough, and large effects may produce unimpressive p-values if the sample size is small or measurements are imprecise.


The Court in the In re Chantix litigation got it exactly right:

While the defendant repeatedly harps on the importance of statistically significant data, the United States Supreme Court recently stated that “[a] lack of statistically significant data does not mean that medical experts have no reliable basis for inferring a causal link between a drug and adverse events …. medical experts rely on other evidence to establish an inference of causation.” Matrixx Initiatives, Inc. v. Siracusano,––– U.S. ––––, 131 S.Ct. 1309, 1319, 179 L.Ed.2d 398 (2011). The Court further recognised that courts “frequently permit expert testimony on causation based on evidence other than statistical significance.” Id.; citing Wells v. Ortho Pharmaceutical Corp., 788 F.2d 741, 744–745 (11th Cir.1986). Hence, the court does not find the defendant’s argument that Dr. Furberg “cannot establish a valid statistical association between Chantix and serious neuropsychiatric events” to be a persuasive reason to exclude his opinion, even if the court found the same to be true.


IN THE SEARCH FOR THE NEW and different indexes that will power a new and different ETF, back-testing plays a critical role. Index providers, including S&P, Dow Jones, MSCI, Russell, Zacks and others can index just about anything. You want to rank the companies in the S&P 500 by earnings growth, then take the 50 top firms and weight them equally? Weight them by market capitalization? Go long the top 50 and short the bottom 50? They can build it. And then they back-test it. Indexes that look good in hindsight have a shot to become ETFs. Those that don’t, don’t.

Given our quantitative roots, we are sympathetic to the fact that backtests are often used as an input into making investment decisions. But past returns, as we all know, do not predict the future. And we think backtested results may be particularly problematic today. Very little fundamental data for US equities extends back more than 30 years, but the last 30 years were a period generally accompanied by two related phenomena: increasingly easy monetary policy and falling interest rates. In particular, the wave of liquidity and stimulus provided in the wake of the Tech Bubble coincided with unprecedented levels of credit expansion, rising asset correlations and record earnings volatility.




I’ve been in Court for the last few days sitting on a jury. Yesterday, Counsel for the defense initiated their open, and called an expert witness: he was an image analyst. His CV, which he read out in Court was very impressive, and I was looking forward to an interesting and technical discussion: to this point the Crown and Police evidence had been a shambles of inepeptitude.

Essentially we were told the following: [i] there are 3 tests performed in an image analysis [ii] the methodology is specific in each test [iii] a conclusion is reached that rates from 1-6: 1 = no correlation, 6 = high correlation

The images were of an aggravated robbery of a petrol station by two armed robbers, and the analysis was designed to ascertain whether the defendent was in point of fact one of the robbers pictured.

Test 1 is the measurement of anatomical landmarks of the face: it is a quantitative test. Test 2 is an analyst’s visual subjective analysis of facial features. Test 3 is manipulation of the images: the images taken from the robbery, plus control images taken from the defendent. The two images are mixed and matched via technology and computers.

Due to hats and bandana’s worn by the robbers, only partial facial features were visible, thus, Test 1 and Test 3 could not be performed. This left only Test 2 the subjective assessment.

The Crown Prosecutor had a chance to cross examine after the presentation of the defense evidence. Did the Crown ever drop the ball. The problem was that the Crown had no idea of statistical investigation and probabilities.

The defense evidence resulted in a conclusion of 1 on the 1-6 scale, based on the following 4 significant differences. [i] the sideburns were different [ii] the robber had some undefined mark/depression on his right cheek [iii] the bridge of the nose appeared farther forward [iv] differences in the shape of various points of the ear.

This analysis could have been attacked immediately on the following basis: what is your confidence level in your conclusion? Now anyone who is vaguely versed in statistics knows that this is a basic, basic question.

The confidence level is calculated from your margin of error, or Z-score, your population or sample size, the distribution, which will provide you with your confidence level expressed as a +/-

The ideal outcome being a high confidence level at least 95%+ and a small margin of error, which you generate from your sample size and selected Z-score.

When you want to estimate a probability, a quick and dirty formula for the required sample size you need to get within the margin of error you need is n = 1/[MOE]*

The problem that the Crown sensed, but couldn’t quite get to, was the sample size of 1. Obviously a sample size this small is going to, particularly in light of the utter subjectiveness of the only Test applied, is going to provide a very low confidence level, and thus a very low probability of the conclusions drawn being statistically significant, and thus the testimony of the expert being taken with a large pinch-of-salt. Certainly his conclusion of 1 on his scale I feel was untenable.

Nonetheless, an interesting couple of days to observe the State in action. If I was concerned before, I have to say that now I am absolutely terrified. The level of ignorance, incompetence and total arrogance has to be witnessed first hand to appreciate just how bad things really are.

Wood has crunched the numbers on the distribution of closing prices. Guess what? They’re not a Gaussian distribution…what a shocker!

Read Mandelbrot for the research and mathematics into “Power laws” that can explain the distributions that occur on a fairly regular basis on Wall St.

Next Page »