Get Shorty (again, research, not the movie…)

I’m running a high risk of running out of movies with “short” in the title. So this had better be the last blog post on the subject!

In my previous post (here), I looked at a short-sale signal where a stock was shorted after it averaged 3% gains each day over five days (in any distribution). At the end of five days, it had to be up 15%. Yes, I could have just looked at it that way, but whatever, it all works out the same.

The graphs looked pretty good. Seemed like the basis for a system, yeah?

Not so fast.

Continue reading Get Shorty (again, research, not the movie…)

Autocorrelation of SPY, and the Redneck Correlogram

I’ve been reading books by Michael Halls-Moore and my head hurts. Not having any formal training in statistics, I only understand about half of the material. None the less, I found his discussion of ‘correlograms’ interesting. I even installed R on my computer (even though I haven’t fully grasped Python yet!) and was able to make some correlograms with R. However not knowing anything about R (sensing a theme here?), I thought I’d come up with my own version of a correlogram using AmiBroker and Google Sheets. A ‘redneck correlogram’ if you will.

So what is a correlogram, you ask? Here’s a link to a wiki page on the subject. My interpretation: it’s a tool to see if a time series of data (i.e. stock prices) is autocorrelated (i.e. is there some connection between price movements down the line from the day in question).

Continue reading Autocorrelation of SPY, and the Redneck Correlogram

The Big Short (Research, Not the Movie…)

COLL bumpI seem to be in a ‘shorting’ mood lately. Can’t think why that might be… (oh yeah, the general state of the market perhaps?).

Above and below you can see some recent examples of stocks that went up a lot, and then either kept going up or dropped down again. Yes, that amazing analysis is given to you, free of charge.

WTW bump

Riffing on my previous ‘peak crash’ research, I thought I’d look into what happens to stocks that have a big bump up, either as a steady increase over a few days, or a more radical single-day increase. The results, at least from a shorting standpoint, were very pleasing.

Continue reading The Big Short (Research, Not the Movie…)

Peak Crashes: Are They a Shortable Opportunity?

AAPL 2008 exhausted3

In hindsight, it’s fun to look at stocks that have had a huge surge, only to collapse violently after they peak. And by “fun” I mean sitting on the sidelines watching, as opposed to pulling one’s hair out when you’re long that particular trade.

Above you can see Apple (AAPL) in 2008, where it hit its 52-week high, only to collapse a few days later, for a very significant loss.

Are these moments a shorting opportunity? I aim to find out.

So first, I’ll pick some somewhat arbitrary definitions of a ‘peak crash’ event.

• stock universe is the historical constituents of the Russell 3000 index.

• liquidity filter to eliminate the weird stuff: historical price >$10, with a median 19-day volume > 100,000 shares.

• The stock must have recently hit its 52-week highest close. I’ve defined this as the highest close of the last 10 trading days is equal to the highest close of the last 250 trading days. Which is close enough to being a year’s worth of trading days.

• The trigger is a drop of 8% or more from one close to the next, within that 10-day period of the highest close being reached.

• I then look at the forward gain/loss for the following 5, 10, 20 and 40 trading days.

• the period explored is 1/1/2000 through 12/22/2015. The end date represents the last ‘peak crash’ event that has the full 40 days of forward data to evaluate as of this writing.

Let’s take a look at the average gain or loss (expressed as a percentage) for each forward-looking period:

Screen Shot 2016-01-29 at 9.28.58 AM

Well now, that’s just boring as hell, isn’t it?

A slight mean-reversion tendency for the short term, and a slight downward continuation for the longer terms. The longer the holding period, the deeper the average loss, but I don’t know if these values are statistically significant.

Perhaps these peak-crashes are highly dependent on the overall market health to be a successful short? Let’s take a look at the yearly gain/loss averages for each forward looking period.

Screen Shot 2016-01-29 at 9.28.33 AMClearly there are some variations over the years!

The most striking takeaway from this chart is that if the year ends in “3”, go long. If the year ends in “1” or “8”, go short.

No, I’m kidding. That’s stupid. But what’s NOT stupid is taking a look at how the peak crashes behave under varying market conditions.

I’ve added another statistic to our data set: the percentage the S&P500 is below its highest close over the last year (ok, 250 trading days). So if the market is closing at a 250-day high, that percentage would be 0. From a code standpoint, I look at the peak-crash trigger, take the highest close of the S&P500 over the last 250 days, and divide it by the current close of the S&P 500. As of the close of 01/28/16, the S&P 500 is 12.9% under its 250-day closing high. Make sense?

This is a simple way to show if the market is doing well or not. There are of course other ways, such as my breadth diffusion indicator discussed a while back. But I wanted to keep this simple.

Here is a chart of all the peak-crash events, sorted by the S&P-percent-under-250-day-highest-close value. Then divided up into vigintiles. I’ve put the the ranges of some select vigintiles so you can get a sense of the values they encompass.

Screen Shot 2016-01-29 at 9.28.22 AMYou’ll note that there are many more instances where these peak-crash events happen and the market is very close to its 250-day closing high. In fact, about half of nearly 10,000 events occur when the market is 0-3.5% below the 250-day highest close.

From a shorting standpoint, the action doesn’t really start happening until the market is about 5.9% below its yearly closing high. And while the shorter periods show a pesky tendency to bounce back after such a drop over most market conditions, the 40-day forward period looks promising. Let’s look at that more closely.

Screen Shot 2016-01-29 at 9.28.10 AM

There might be a shorting system here. When the market is 6% down or more, these peak-crashes on average end up in a further two-month (40 day) loss. I’ll leave that to the reader to actually refine into a workable system.

And when markets are booming—remember those days?—perhaps a good shorting system might be found in the 10-day forward period (see below). The average loss when the market is less than 2.9% below the 250-day highest close is much smaller, but it’s pretty consistent. This might be worth checking out for when times are good again.

Screen Shot 2016-01-29 at 10.03.54 AM

And because charts can be a little boring, here’s a mountain sunset photo, taken on my phone.

Sunset at Green Valley Lake

Breadth Diffusion Predicts a Bounce?

SPY w dif30qtr

Recently I posted a number of articles on various breadth diffusion indicators and their relative effectiveness in predicting the health of the S&P 500. The big winner was the system that compared the number of stocks in the historical constituents of the Russell 3000 that were up 30% or more over the last quarter (60 trading days) vs those were 30% or down over the same period. You can read the whole series here.

The breadth diffusion value is computed as (up30 / (up30 + down30))*100. Suffice it to say, we have been seeing very low numbers recently. The current value is 8.2. So I thought I’d go back and see what those low numbers have foretold in the past.

Looking at the period of 1/1/2000 through 12/31/2015, we have had only twenty times that the diffusion value has dipped below 10. Below you can see all 20 of them, with the diffusion value, plus the 5, 10 and 20 day gain/loss for each instance:

low 30qtr dif
The average 5-day gain/loss of the S&P 500 is 2.9365%, while the average 10-day G/L is 5.4655% and the 20-day G/L average is 6.3975%. The 5-day period following such a low diffusion number has been positive 75% of the time, the 10-day period has been positive 90% of the time, and the 20-day period 80% of the time.

All that to say a bounce seems likely.