The Big Pairs Debate

Michael and Jennifer have different views on a critical issue regarding statistical pairs/spreads.  The question is whether the two companies need to be similar (in the same sector or industry) or not.

The basic pillar of spread trading is that the two stocks are tied together with some sort of force, and a deviation from historical levels is an opportunity to bet on a return to those levels.  Without such a force, the spread may not ever return to past levels.

Michael’s view is that all stocks are subject to the Law of One Price (LOP), this is the force uniting all securities.  “Ingersoll (1987) defines the LOP as the “proposition…that two investments with the same payoff in every state of nature must have the same current value””(Gatev, 2006), regardless of whether the two companies are in the same or very different businesses. The profit generated by the arbitrageur is a compensation for enforcing the LOP.

Jennifer’s view is that current price is expectation of future cash flows which are impacted by many outside forces.  Companies that have similar businesses will be impacted similarly by small changes to inputs like interest rates or commodities prices or consumer sentiment. A deviation in the prices of a bank versus an oil company may be an anomaly, or it may be due to fluctuations in inputs which could take a long time to correct; for similar companies it is more like to be an anomaly we can exploit in the short- to medium-term.  Given a relatively short time horizon, it is essential that the two companies have similar businesses.

This is an important issue because requiring the pair to be similar companies means fewer potential trading candidates and therefore less diversification across positions.  However, if we open up to pairs of different companies we may introduce more data mining errors.

What do you think?  Do you think statistical spreads need to be pairs of similar companies, or can you make money trading statistical spreads with companies in different industries?

Written by Jennifer Galperin. Follow me on Twitter and StockTwits.

In collaboration with Michael Bigger. Follow me on Twitter and StockTwits.

You can learn about trading pairs here.

Day Trading Green Mountain Coffee and Diamond Foods Spread

I have been day trading (scalping this spread) for the past few days. Everytime it goes close to about 20.00, I would initiate a short position.  That is short 3 * $GMCR / Long 5 * $DMND. I would then cover below 15.00 giving us +5 pts profit per spread.

Overall the spread is in overbought territory as it is trading over +4 z-score above the 5 year average.

Stats and charts can be viewed here. Learn to daytrade spikes and gaps here.

Written by Aris David. Follow me on Twitter and StockTwits.

Source: Interactive Brokers

More on Starting Over

My most popular post on StockTwits has been Starting Over. That post crushed it.

Before I forget, there are a few more things I would like to share with you:

• Not all tools have been created. Imagine the ultimate trading tool you would love to have. Now go build it.
• Make that tool algorithmic, in some way, if possible.
• Start building a business around it.
• Anchor it to the Internet.
• Now go and do some amazing things.

Written by Michael Bigger. Follow me on Twitter and StockTwits.

Introducing SpreadTraderPro

When I traded single-stock derivatives at D. E. Shaw, my boss traded S&P options spreads, and he made money consistently though he took very little risk. He was a trading magician. He knew his options market, especially the S&P, and he knew how to trade spreads. He constantly traded in and out, squeezing juice out of the lemon. The lemon never ran out of juice! It was a wonderful thing—a winning trading method.

Most of the best traders I have met trade spreads. They spread different options, stocks versus stocks, indices, stocks against indices, etc. They even trade cash against stock. Think of it as trading a stock against its intrinsic value. This is taking advantage of a spread between two values. The number of combinations is endless.

These traders are good at identifying pockets of value (be it statistical, against value, momentum, and so on) among the securities they trade, and they rotate their inventory to take advantage of these discrepancies with no net increase in market exposure.

On the statistical front, the biggest challenge is dealing with data; you have to narrow down the universe of stocks and spread candidates (millions) into the small subset of spreads that are cointegrated, meaning they tend to revert to the mean. This alone has made it very difficult for individual investors to trade statistical spreads. You may have encountered this problem if you have used our free Spread Analyzer, only to find out that very few of the spreads you can think of are in fact cointegrated at any significance level. It’s very frustrating!

We faced the same issue. We dealt with it by developing our proprietary market scanner to identify cointegrated spreads. Now, for the first time ever, we are making this tool available to the broader trading community. It is called SpreadTraderPro.

Say goodbye to the frustration of searching manually for profitable spreads. Using SpreadTraderPro, you can trade statistical spreads just like the pros do. No programming is required. It really is that simple!

Are you ready to complement your current trading strategies with statistical spreads for more profits and diversification? Click here to find out more about SpreadTraderPro.

Written by Michael Bigger. Follow me on Twitter and StockTwits.

Join The One Percent

I recently went to dinner with several friends who are traders at large investment banks. All of them are smart successful guys with many years of trading experience. I was explaining to them some of the statistical techniques we use at Bigger Capital. None of them were familiar with any of the basic statistical measures used in statistical arbitrage. I’ve encountered the same thing talking to other traders on different occasions. Ninety nine percent don’t have any idea of the basic concepts of statistical trading. And yet none of these measures are new. Statistical Arbitrage has been used as a successful strategy since the 1980’s. So what are the basic measures used? We give brief definitions of the primary ones below. They are the same statistical values we provide subscribers to our SpreadTraderPro. These measures can be used as a standalone strategy, or combined with other methods to enhance returns. They are:

1. Cointegration – measures the degree of confidence that a stock pair which has diverged from its mean value will revert to that mean.

2. Z-Score – measures the distance the spread is from its mean value in standard deviations.

3. Half-Life – is the expected time it will take for the spread to revert half way to its mean.

4. Zero Crossing Rate – is the number of times the spread can be expected to cross the zero value for the defined time period. A higher number implies a shorter holding period and a greater likelihood that the spread will not continue to trend.

5. Sum of Least Square - is the squared distance over the selected period. The lower this number the tighter the spread is, and the better the chance that the price of leg one will not wander far from the price of the leg two.

Written by Norm Winer. Follow me on Twitter and StockTwits.

Green Mountain Coffee and Peet’s Coffee Spread

The $GMCR and $PEET spread is going to be a fun spread to trade during the next few days. The spread went from overvaluation a few months ago and it should trade in undervaluation territory today. You can play around with our Spread Analyzer right here.

- Michael

Harnessing Energy Using Frictionless Platforms

I met Aris David on the StockTwits platform. I don’t remember when and how the interaction started, but our trading relationship was born within the chaos of the Twitter ecosystem. He is known on Twitter as @fledglingtrader.

Although Aris operates out of London, we collaborate seamlessly (or easily) on trading technologies for the Bigger Capital ecosystem. To date, our symbiotic relationship has produced satisfactory trading results.

All our achievement has been made possible by available and free collaboration tools and technology platforms such as:

• Cloud IT and virtual private servers IT infrastructure
• Gmail
• Skype
• Twitter and StockTwits
• Google+
• Google Docs
• Oracle Virtual Box hosting several operating systems, including Linux
• Screenr
• SquareSpace
• Interactive Brokers
• And so forth…

We use the same frictionless platform to create trading tools such as our Spread Analyzer, which you can find right here.

Anyone located anywhere in the world who has access to a browser can use this free tool.

@AsymmetricRisk, a trader residing in Panama, tweeted a few days ago:

@biggercapital pls take a look when u get a chance, bit.ly/sHtbrJ vs bit.ly/vtenzW only change is switch 1 and 2 leg

I got the tweet on my Droid Bionic and was able to open the exact same spread using the embedded links. I responded:

@AsymmetricRisk let me think about it. First thought is rounding impact right at the boundary condition

After talking to Aris about this tweet, I replied:

@AsymmetricRisk After talking to @fledglingtrader, $MSFT (at cointegration theshold) drives $INTC not the other way around

Because @AsymmetricRisk permutated the legs of the spread in the analyzer, he asked a good question, and we all benefited from it. Microsoft drives Intel changes at a cointegration level, but it is less than ideal the other way around.

If you think about the value we all have derived since my first interaction with Aris, it is just amazing. And now, we interacted with @AsymmetricRisk, and the energy from that interaction will follow its own course, hopefully in a value-added type of way.

This is how I think traders and investors will increase their value toward infinity in the future.

I am in awe by what you can accomplish on the chaotic frictionless platforms. Aren’t you?

Written by Michael Bigger and Aris David. 

How to Use Our Spread Analyzer

The Spread Analyzer can be found right here.

Produced by Jennifer Galperin. Follow me on Twitter and StockTwits.

How to Use Matlab for Trading

This video is an introduction to Matlab for trading purposes.

The resolution isn't the best, but you can get the source codes from here.

Guest post by Jev Kuznetsov. Kuznetsov runs a quantitative volatility strategy for Bigger Capital. You can find his blog right here.

Why should I learn a programming language?

Guest post written by Jev Kuznetsov. Kuznetsov runs a quantitative volatility strategy for Bigger Capital. You can find his blog right here.

 

People are bad at math. Some are better than others, but who can calculate ln(sqrt(345)*34)^3.4 in less than 1/1000th of a second? Even the simplest computer can calculate at a rate million times faster than a human, in fact this is the main reason computers have been invented. To use the full capacity of a computer the user should learn how to progam it.

 

People are quickly bored. Imagine you'd have to download around 1500 excel sheets of stock data and then combine them together? Unless you have an autistic disorder you would probably get bored to death. A computer would not mind doing the task, but you'd have to program it.

 

Another example is reaction speed.  It takes ages measured in computer time, for a human to see a price change in a stock and then just press a button.Curious about how fast you can react? Take a test here. Your reaction speed should be around 0.2s,  while even a simple microcontroller (the ones you’ll find almost in any electronic gadget)  will do the same task in 0.0000001s.  Want to compete? If you know what action should be taken in a certain sitiuation, a computer can do the job for you a couple of million times faster, but still you need to tell it what to do by means of programming.

 

Still, even with all that raw computer power nowadays, measured in gigahertz and terabytes, the 'heart' of computers haven't changed much since their invention: they are still not much more than 'calculators'. Yes, we've come a long way in terms of size factor, power consumption, user friendliness and price, but still, computers are incapable of creative thinking, adaptation to new environments and unpredictable situations. A cockroach is more intelligent measured by these standards than the most advanced computer. By combining the human power of creative thinking with the raw processing power of a computer a trader can become supertrader, achieving a consistent return.Financial markets are just like nature, the smartest ones with the best skills for the current (and ever changing) environment survive. The ones that do not adapt get extinct at some point.

Using computers for trading today is a fact of life, and a trader has got no choice but to adapt. Who does not use some form of charting tool to find entry and exit points or an excel sheet to keep track of performance? Not much algorithm here, but it is already a form of computer-assisted trading. Another level is to purchase special purpose software, like spread trading tools. Often it includes some form of 'black box' logic, so you can only hope that it works and continues to work in the future. But why not move to the top of the food chain and learn how to process incredible amounts of data, design own algorithms, backtest strategies, automatically place hundreds of orders and analyze trading performance? By writing your own tools and programs, you'll be able to quickly adapt to every new market environment.

 

Your time invested in learning programming techniques will have a ten-fold return in time saved from doing boring daily trading tasks.

 

Coming next: which programming language to choose?

 

Jev