Think Clearly

Highlighting the most important topic of the week.

“You just keep pushing. You just keep pushing.
 I made every mistake that could be made.
 But I just kept pushing.” ~  Rene Descartes

AAPL-GOOG: Is the pair ready for a contrarian strategy?

The action in AAPL’s performance for the past few weeks has been charged with energy as the stock has fallen off the tree for the momentum players. Roughly, 706 highs in September 2012.  Now we are two months into the new year and AAPL is trading at 430 price levels. That’s -39% from the highs.

One way to take advantage of AAPL’s demise is to trade the AAPL – GOOG pair as a spread. Let’s examine if the pairs ready for a change. This “pairs” strategy would mean a long side entry in AAPL and short side entry in GOOG as a dollar neutral position. This technique has been around wall street for over 20 years and is a form of statistical arbitrage. There’s no real “market” hedge. It is really dollar neutral in terms of the number of AAPL shares long compared to GOOG shares short. We are simply taking advantage of an anomaly in the spread between AAPL and GOOG as the price differences between these two highly correlated stocks diverge to the point that the spread widens to levels not seen historically. But when I say historically, it’s not just time that is important, but the length of time two “similar” stocks have been under a high correlation. In other words, AAPL and GOOG would move in the same direction at least 86% of the time for a very long time.  Then it changes. We want to take advantage of this change in correlation. We can measure this change as a chart of the spread then overlay a simple linear plot.

Below is a weekly bar chart of AAPL. Notice the distance from the 50 period MA and price range.  Roughly an 18% move from the 50 MA.

AAPL weekly chart by: MBNavPro
AAPL weekly chart by: MBNavPro

Below is the weekly chart of GOOG and the relationship of the price to 50 period MA.

Weekly Chart of GOOG by: MBNavPro
Weekly Chart of GOOG by: MBNavPro

Below is the weekly chart with both AAPL-GOOG’s divergence in correlation. We can see the time line I plotted on the chart when AAPL and GOOG divorced and went their separate ways. The stocks accelerated away from each other as GOOG trends higher and AAPL trends lower to historically wide levels.

AAPL-GOOG Plot by : MBNavPro
AAPL-GOOG Plot by : MBNavPro

The Trade defined as a spread between the “pair”.

 Since we are setting up the trade based on the spread being wider than historic levels (Selling short the winner and buying the loser), we only care about the movement of the spread. In this model, we are looking for the spread to narrow. If history repeats itself, the prices will converge and the spread will narrow and we make money. We do not care which end of the trade creates the narrowing of the spread. We only care about the spread. How does this happen? Well, there are several scenarios and I’ll go through a couple examples leaving out any dividends or splits that would have a direct impact on our profit/loss. Scenario 1)  is GOOG falls at a faster pace than AAPL. 2) AAPL rises at a faster pace than GOOG or 3) AAPL rises and GOOG falls from their current price levels.  These scenarios would bring that spread tighter. We won’t try to guess what will happen or when, we just want a few scenarios too potentially work in the favor of the spread to narrow based on historical prices between the two stocks. Remember, this is a contrarian strategy and we are betting the ratio moves back to the mean.

We would want to keep the same dollar amount. Using the weekly close data of AAPL and GOOG, our set up shows a ratio of .53.  Using AAPL as a base long of 100 shares makes this really easy to comprehend. For example, we might want to buy 100 of AAPL and short the same dollar amount of GOOG. So to find this, we plot the spread and see that 100 AAPL equals the same dollar amount of a short position of 53 shares of GOOG.

Sheet of a very basic ratio plot to see how it looks over a few week periods. Look at the last line item that represents the current spread data point we would use if we wanted to put this trade on now.

SPREAD WEEKLY DATA.

Below is a chart of the spread:  I plotted a simple linear regression over the spread and we can see it is at the lower band of the linear regression line. We want the spread to eventually migrate back to the mean or narrow from here.

AAPL-GOOG spread.
AAPL-GOOG spread.

What’s the risk?

There is risk in several areas. The spread gets narrow to a point of margin pressures and time is a risk.  Remember, this is technically a contrarian strategy. There’s potential bankruptcy. If you trade pairs in other companies that might not be so fundamentally sound. (Very doubtful this occurs in AAPL or GOOG). Also, execution costs, margin costs and changes.  Low liquidity or carry costs associated with short selling will alter the performance. All these factors can have an impact on your net profit/loss that’s not adjusted in the spread. Profit targets and stop loss come in many forms for pairs trades. One method is the levels need to coincide with your max dollar loss and back that into the spread levels if they continue to move against you. Profits or trailing the profits should be taken at a favorable spread levels based on costs associated with the spread and a ratio above your stop loss dollar amount by at least 1.5 times the risk.

Advanced models:

I can take this strategy a few steps further by altering the ratio. The ratio can be altered through factors in a proprietary model that many professionals have in their statistical arbitrage models. For example, a weighting of volatility, dividends, liquidity or volume. But this example is the as basic as you can possible get and with the right risk and discipline, can work out if you want to take advantage of a contrarian bet in AAPL or GOOG. Be aware there are several other methods to plotting the spreads and ratios between two pairs which can alter your trading time frame dramatically. Take the time to do the research that fits your risk.

Nick Pirraglia

TradersThinkTank

Follow me on Twitter and StockTwits: @Liquid_Trader

New Home Sales Shine & Housing Stocks Power Higher

New Home Sales rise nearly 16% in January. This number is the highest in 4 ½ years according to AP and Case-Shiller. The trend continues as housing rebounds.

All eyes were focused on the new Home Sales data released on February 26th as key points were being made all week long in the debate centered on interest rates, economic growth and the fed’s continued bond buying program. This has been a hot topic for many months but a renewed interest has migrated into the New Home sales as the debt markets have seen the past few weeks of selling and the equity markets have been on a steady – state move higher.

As the markets stretch higher, expectations must be met.  Now the housing data moves to center stage. Justification to continue the bond buying program is deeply rooted in economic growth and jobs data. Take this one step further with the Fed and many economic analysts believing that the housing markets are the catalyst to future economic expansion. The near snail-like growth in housing has kept many facets of the economy in check. Growth is seen rooted in housing sales and rising home prices. The need for steady increases in home sales should to lead the consumers towards increased confidence, jobs and the feeling of wealth.  The fed is betting that a strong housing sector and increasing demand will create jobs relating to construction and also builds the confidence of large and small business to ramp up hiring across the board.

Where’s the growth coming from?

According to the Case-Shiller data, 19 of 20 Cities increased in new home sales. The Composite data increased and moved into positive territory as New York fell slightly. Let’s think about for a moment. The national average and the 20 City composite that Case-Shiller tracks has moved in tandem for two years now. The data is also broken down into a 10 city composite which will show a diversion at times but not enough to cause concern the past year. Basically, what we are experiencing is the major cities that were hit the hardest have also re-bounded the fastest over the past year in both sales and prices. The major cities that did not experience such a dramatic peak and drop, cities like Dallas, Houston, New York and Charlotte have held steady. Cities like DC, Phoenix, Denver, San Diego and Atlanta has seen a nice up-tick in data as housing gained a footing to these battered cities.

20-City Composite

  We clearly see the peak and drop in data with a near flat line the past 2.5 years.

20-city

10-City Composite

  A much volatile move from the 2006-7 peak to 2009 trough.

Charts By: Case/Shiller
Charts By: Case/Shiller

National Average

It looks a lot like the 10-City. This is because those top major metropolitan areas have skewed the national average.

Case/Shiller
Case/Shiller

Look at the data for Phoenix and the volatility in the data as it comes back towards the mean of the past 2-3 years.

Case/Shiller
Case/Shiller

 Look at the steady data in Dallas where the peak and valley was nothing compared to other parts of the country. Some major cities, like Dallas and New York have kept steady. This has not offset the volatile effects of the cities that experienced the fastest growth and ultimately hit the hardest.

Dallas Data Chart ~Case/Shiller
Dallas Data Chart ~Case/Shiller

 Taking advantage of this data

 We can trade or invest in this data several ways. Think how housing growth can affect the many branches of the economy? For example, construction, housing construction, banking and retail. Use your imagination.  Sometimes, simply looking around those home construction sites gives you all the information you need to know. A simple and direct approach is to look into the housing stocks like, TOL up 3.68%, BZH + 3%, LEN +3.7% and KBH + 6.7%. Also retail support like HD + 5.7%, LOW + 1.84%, ITW +.5 %. There are two Exchange Traded Funds to watch also if you don’t want to trade one or two stocks, ETF’s like XHB + 2.89%  and ITB + 3.56 %.

Nick Pirraglia

TradersThinkTank

2 Responses to Think Clearly

  1. Pingback: Think Clearly | TradersThinkTank

  2. Nick “Heebner” Pirraglia
    well done!

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