Cockroach Theory with a Positive Consequence. $scss $tpx

Written by Michael Bigger. Follow me on Twitter.

 

 

On April 20, 2010, Tempur-Pedic (TPX) reported great earnings, and the stock went up 12 percent in after hours trading. Select Comfort, another bed manufacturer, went up to $9.05 (+5 percent) in reaction. I tweeted at that times that the earning report for TPX was an indication that the earning for SCSS would be good.

 

The cockraoch theory states that If you find one roach in the cupboard, there is usually more than one crawling in the same location.

 

On April 21, SCSS reported great earnings, and on April 22, the stock traded up to $11. If you bought SCSS on the TPX news, you generated a 21.5 percent return on your investment in a few days. This is the cockroach theory with a positive consequence.

 

This theory is a useful leading indicator for cyclical companies because a company's performance in the short run is more sensitive to the economy than to management skills. Companies in the same sector will tend to track one another.

 

The reverse is also true. In 2006, SCSS announced mediocre results. This should have been a warning sign for TPX investors. From mid-2006 to late 2007, TPX stock doubled. Eventually, TPX followed SCSS's lead and dropped from about 38 to 5. This is the version of the cockroach theory with a negative consequence. 

 

I can think of many ways to incorporate roaches' behavior in my trading algorithm. What about you?

  

 

The Fallacy of Vividness in a Levy Flight News Cluster $SHW

Written by Michael Bigger. Follow me on Twitter.

As the stock price of the Sherwin-Williams Company (SHW) reaches all-time highs, it is interesting to look back at how the stock reacted when Rhode Island dropped its lead paint lawsuit on the company in 2006 (see the chart below). We believe the "fallacy of vividness in a Levy flight news cluster" is the model that represents this situation.

A trader who has the ability to view similar situations in their proper contexts can create algorithms to take advantage of the deer caught in the headlights. 

How our Algorithm Traded $GS this Morning

Written by Michael Bigger. Follow me on Twitter.

Yesterday, we said in this post that It might be of interest to short the large dealers if they rally.

So we set up our algorithm to lean short on Goldman Sachs (GS) and establish our short as a fade a quick move up type of trade. We were fortunate to get a strong upward move in $GS after it announced earnings. Our algorithm sold the stock short at $164.45 and covered this position a few dollars lower. We were very doubtful that shorting GS was a great thing to do. The beauty of algorithmic trading is that it takes the emotion out of the equation. If the mental model makes sense, follow it.  We are just learning about how to trade in a Levy Flight news cluster. This is an experiment, and we could be completely off the mark. Our intuition tells us that until the truffle diggers are done digging, the move and fade strategy is the way to go. Our algorithm will trade GS from a long bias when the news frequency on the company decreases.

biggercapital Twitter stream:

Levy Flight, Truffle Diggers, and Goldman Sachs $GS

Written by Michael Bigger. Follow me on Twitter.

It did not take long after I wrote the post Levy Flight Path in Algorithmic Trading to get a fraud scandal erupting that was worthy of a Levy analysis. The Goldman Sachs (GS) allegations have already attracted  many truffle diggers (journalists and politicians) sending the frequency of Internet news messages to the moon.

Here is what two of the diggers are saying:

• Paul Krugman
• Gordon Brown

This is how we think things will play out using the Levy Flight model:

• GS entered a dense news cluster.
• The cluster will persist for months.
• The cluster will attract more truffle diggers. For that reason, more negative news about GS and other financial companies will surface.
• We expect GS stock to remain under pressure and volatile until the tension is released.
• It might be of interest to short the large dealers if they rally. The Citigroup stock price gap up, which is up after today's earning release, is a good example of this trade. 
• The diggers will eventually get bored and move on to the next story. "This too shall pass". 
• The decrease in the frequency of news messages will be a good indication of when we are about to exit the cluster.
• A trader might want to initiate a long position at that time.

We have decided to add GS to one of our algorithmic trading strategies. We believe GS will be a great trading vehicle for agile traders. We can't wait to see how things play out and whether the Levy Flight model holds its own.

How do you think this will play out? 

Levy Flight Path in Algorithmic Trading

Written by Michael Bigger. Follow me on Twitter.

  

  

Seth Godin wrote a fabulous blog post about a cool mathematical concept called the Levy Flight that shows up in nature (Wikipedia: Levy Flight Description).

 

Here's how a Levy Flight might play out in the media. A journalist finds an interesting story to writeSource: Wikipedia about. For example, think about Merck's scandal involving the drug Vioxx. That was big business news, and it stirred up emotions. Many people took a stance on both sides of the issues related to this event. Writing about Vioxx generated good readership and sold advertising. Eventually, readers got bored with the story and moved on. Our journalist had to find other news. The journalist’s path follows a Levy Flight as depicted in the image, from one random walk to a cluster, followed by the same process over and over again.

 

The Levy Flight also shows up in finance. Just think about how Merck (MRK) reacted on the Vioxx news and how it stabilized after journalists migrated to a different story.

 

For algorithmic traders or investors, a few things are of interest here: 

 

1. What is the relationship between a cluster and volatility?

2. As journalists migrate from one story to the next, is tension being released on the system? Is that a form of catalyst (value trading and volatility trading)?

3. What are the roles of traders or investors in these situations? Do they also become news amplifiers within the cluster? Do they contribute to the catalyst after the cluster disintegrates? 

4. When a news cluster starts forming, could monitoring social media, finance groups, or other venues help predict entry into a cluster or exit from it?

5. If so, could we analyze Internet messages to incorporate the jump function into a Brownian process?

The Levy Flight is worthy of further analysis. I can see a few ways to incorporate this concept into our algorithms right now. 

 

When the next Vioxx crisis erupts, I will remember that journalists will eventually walk away and let it go. The news will subside as it always does. Benjamin Graham once said this: “This too shall pass.” 

 

How to Start an Algorithmic Trading Business within a Week

You are a successful trader or an aspiring trader. You use technical or fundamental analysis to generate profit. You spend most of the day watching the ticker screen.

 

If these statements describe who you are as a trader, you should consider generating more profit by building an algorithmic trading platform. Let technology work for you by leveraging your trading knowledge.

 

There are many ways to start algorithmic trading, but this is the method I recommend:

 

• Find an online broker with strong technology that can enable you to deploy your own algorithm. I wrote the following post on how to choose the right online broker for your algorithmic trading business: http://bit.ly/8Qli9Y.

 

• I cannot emphasize this point enough: Your broker must have pre-built algorithms you can use right from the get-go and a sandbox to test these algorithms with your trading methods.

 

• Go test your ideas in the sandbox using these pre-built algorithms.

 

• Once you are comfortable with how your algorithm performs, deploy it in the market.

 

• Keep track of your results.

I use Interactive Brokers (IB) for my algorithmic trading business. There are plenty of good online brokers out there, and IB is one of the best. Check out this IB video entitled “How to Capture Profit using the Scale Trader.” I can think of many ways to trade using this algo. What about you?

Information for Algorithmic Trading: Perspective from Jeff Bezos and Wilbur Ross

Written by Michael Bigger. Follow me on Twitter.

 

 

Recently, Jeff Bezos and renowned investor Wilbur Ross discussed information. Their views about information made us think about how to incorporate information into our algorithmic trading strategy. Let’s examine what they said and then examine the meaning of this for algorithmic traders. 

 

In this blog post, I discuss "Innovate the Amazon.com's Way". In the Harvard interview, Jeff Bezos made the following observations about information: 

 

1. Information perfection is on the rise.

2. Information costs are going down.

 

In the March Issue of Fortune Magazine, Wilbur Ross, in addition to reinforcing the points made previously, adds his own observations:

 

1. Market information is timely.

2. Market information is abundant and overwhelming.

3. Market participants have not demonstrated more ability to gain meaning from more information.

4. Therefore, the value of the expertise and ability to interpret this mountain of information goes to infinity.

 

The implications these statements have for algorithmic trading are profound. Embedding the expertise and ability to input and interpret a vast amount of information on a timely basis into an algorithm will be a significant source of value creation for algorithmic trading. It has never been cheaper and easier to access the internet information reservoir. The big value will be derived from developing some astute ways to interpret it. We are working on this. Are you?

 

Algorithmic Trading: Innovate the Amazon.com Way

Written by Michael Bigger. Follow me on Twitter.

 

 

Some recently published articles and a video have shed some light on what fosters creativity and innovation at Amazon.com. The lessons learned from Amazon.com’s strategy can help all of us in our algorithmic trading activities. The articles and the video can be found at the following links:

 

1. Harvard’s interview of Jeff Bezos 

2. Jeff Bezos discussing Amazon.com’s acquisition of Zappos

3. How Amazon Innovates: Lessons in Strategy for Microsoft and Others

 

This is what I have learned from Jeff Bezos:

 

1. Be patient; think long-term. Start early and compound knowledge.

2. Learn from your mistakes. Iterate!

3. Learn and invent.

4. “It is always day 1 for innovation.” The same is true of algorithmic trading innovation.

5. “We are willing to plant seeds and wait a long time for them to turn into trees.” 

6. “Is it big enough to be meaningful?”

7. “Important to focus on what’s not going to change in the next five to ten years." What's not going to change with the market you are trading? You might want to build around that.

8. “Information perfection is on the rise.” Can you exploit that?

9. Information costs are going down. Can you exploit that?

10. “Close following doesn’t work as well in a fast-changing environment.” Come up with your own algorithm recipes.

11. “Ask ‘Why not?’” Why not develop your own algorithm? Why not develop a second one?

12. “Maximize the number of experiments you can do per given unit of time.” You never know what you will discover.

13. “Reduce the cost of the experiments.” Therefore, you can do many more experiments.

 

We have applied all these concepts in our trading activities. As an example, items 8 and 9 explain why we have spent so much effort on Twitter and its application programming interface (API). We want to mine information and mine it cheaply.

 

What say you?

Break Down Your Algorithm Plan into Smaller Parts

Written by Michael Bigger. Follow me on Twitter.

 

 

Google La Tuque, Qc, Canada, and you will know where I come from. When I was a teenager, my goal was to move to the United States. Coming from a small town in Quebec, this was a big goal. Although I did not have the wisdom of how to accomplish my goal, I knew how to improve my position. I broke down the big task into small chunks. I climbed one step at a time. When I met insurmountable resistance, I backed off, iterated, and went at it again. I arrived in New York in 1994 and began working for Citibank, where I ran the single stock derivatives book.

What does that have to do with algorithm, finance, and trading? When I work on my trading algorithm, I have a sense of where I want to go, but the path to success is never clear. I come up with an initial concept and test it in the sandbox. Then I simplify, taking one small step at a time, and I iterate if things don’t go my way. In short, I use the same model I used to get to New York, and it works for me.

What is your strategy for achieving your big trading goals? Mine is to simplify, break it down, and iterate.

Whys of Algorithm Trading

Written by Michael Bigger. Follow me on Twitter.

 

 

If you are a trader considering trading your own algorithm (algo), I can think of many reasons why you should start creating your own algo today:

  

• If you already have a trading strategy, coding it will turn it into a process.

• Well-designed algos are efficient and effective.

• Algos can process much more information in real time than you can.

• Technology is cheap, so why not make it work for you?

• Algos improve consistency.

• Algos remove emotion from the process.

• Algos capture your knowledge about the market and compound it. 

• You will learn a ton doing it.

• Developing an algo is a creative process. It is fun.

• There is no downside to trying it.

    

Just do it. I post about algos on Twitter using the hashtag #tradingalgo. Join the discussion and tell me what you think.