The numbers don’t lie

Matt Vergesten, a UW-Whitewater finance and accounting major, tells Whitewater Rotarians the success of a computerized stocks and commodities algorithm test during the club’s Aug. 12 meeting. (Tom Ganser photo)
Matt Vergesten, a UW-Whitewater finance and accounting major, tells Whitewater Rotarians the success of a computerized stocks and commodities algorithm test during the club’s Aug. 12 meeting. (Tom Ganser photo)

Test proves there are more efficient methods of investing than buy-and-hold

By Tom Ganser


On Aug. 12, University of Wisconsin-Whitewater finance and accounting major Matthew Verstegen and management professor William Dougan effectively transformed their speaking engagement with the Whitewater Rotary Club into an energy-charged seminar on the development of an innovative strategy for investing money in a variety of markets, including stocks and commodities.

Dougan provided the context for Verstegen’s cutting-edge, computerized trading algorithm for making money over a long-term as the product of an eight-week UW-W/Blackthorne summer institute, an initiative of Blackthorne Capital Management, LLC, the first private business tenant of the Innovation Center at the Whitewater University Technology Park.

Blackthorne has teamed up with UW-Whitewater faculty and students to conduct research and develop software systems for the purpose of managing financial investment portfolios – a partnership that has allowed Blackthorne to make significant innovative strides and enable them to be a trendsetter in their industry.

An algorithm is a step-by-step procedure or formula for solving a problem.  The word is derived from the name of the mathematician, Mohammed ibn-Musa al-Khwarizmi, who was part of the royal court in Baghdad and lived from about 780 to 850. A computer program can be viewed as an elaborate algorithm and in mathematics and computer science, an algorithm usually means a procedure that solves a recurrent problem. Simply put, an algorithm is like a cooking recipe for mathematics or a computer program.

The objective of the institute, Dougan told Rotarians, was to give “first class students” like Verstegen and his team members, Hugh McMahon from Dublin, Ireland and Jenna McMillian from Eagle, “background and information on financial markets, on statistics and on trading methods to give them some background and theory, in terms of algorithm creation and development. Let them use what really is a state-of-the-art tool to get to some state-of-the-art perceptions about what’s going on in financial markets that frankly nobody else knows about.”

Dougan likened the students’ experience to that of test pilots “set loose on something that’s like an F-22 airplane compared to a bi-plane.”

Verstegen described the purpose of his team’s project as turning “the experience and knowledge of the Blackthorne Institute … into a tool that was greater than the sum of its parts. By taking bits and pieces from each member of the institute we were able to produce a valuable product that will continuously improve as its members gain knowledge and experience.”

The project, Verstegen said, “is proof that there are more efficient methods of investing rather than buy-and-hold using advanced computer applications.”

“I wanted to find the market bottom,” Verstegen said.  “When you’ve got a stock or an equity that’s trading, it will bottom out after time.  I wanted to capture that trend and then trade it to the top.  I wanted it to be easy to adjust for anyone that was coming in to use it and I didn’t want anything else interfering with it.  I just wanted it to run, as an algorithm should, by itself.”

In developing their trading algorithm, Verstegen’s team included nine quantitative indicators commonly used to describe financial market patterns, including the Relative Strength Index (RSI) that Verstegen said looks at how much a stock in overbought and oversold. “It gives you a  number and then you can use that in the algorithm to determine how you want to trade it or if you don’t want to trade it.”

At the start of the project, the team assigned each of the nine indicators the same amount of weight, or importance, in determining whether to buy or sell.

But by the end of the project, each indicator was assigned a different “performance based weighting” based on the “win-loss ratio, how many times it was winning to losing … and some other things such as drawdown which is how far you are losing before you start making money,” he explained.

It was stressed during the talk that the “drag and drop” capacity of the Blackthrone Toolbox in creating the trading algorithm was critical to the success of the project.

“If I had tried to do this with another piece of software,” Verstegen said, “it could have taken six months, maybe more, because I would have had to write the code to do all this. Having this drag and drop ability allowed me to do it in a matter of an hour.”

As with the development of many innovations, the proof is in the pudding.  For Verstegen, McMahon and McMillian this meant “back-testing” their trading algorithm based on the history of the nine indicators upon which it is built. The test began with “buying” $100,000 in each of eight different market in July 1998 and allowing the algorithm automatically to place sell or buy orders until July 2014.

Verstegen said 321 trades were made and the algorithm “won 290 times.”

Looking at the bottom line, the algorithm “made $8.7 million over the course of 16 years,” or a gain of $7.9 million on an investment of $800,000.

Dougan pointed out the team’s trading algorithm is a good example of “time series analysis” involving the analysis of data collected over time – weekly values, monthly values, quarterly values, yearly values, etc. – with the intent to discern whether there is some pattern in the values that can be used in forecasting future patterns.

According to Dougan, the process Verstegen’s team used in building their trading algorithm can be applied to many other fields, including cardiology in medicine, automated production management in energy, basin aquifer management in water, fuel management in air transport, theater fire control in military and genomics in biotechnology.

Verstegen’s greatest takeaway from the project was “the strength of knowledge in numbers (teamwork) and the advantage of having a statistical background, or at the very least taking a few stats classes in high school or college.”

“When you are able to get multiple minds with different backgrounds and different perspectives on the same team, ideas are going to flow faster and be stronger than anything any individual could come up with on their own in the same amount of time,” Verstegen said. “And as much as everyone struggles to see the applicability of upper-level math in everyday life, statistics are everywhere. There is no escaping it; there really is a form of beauty in seeing how different things interact with math.”

Aiming to graduate in May 2016, Verstegen plans “to immediately sit for the CPA exam and pick up some financial certifications before I graduate. It is still too early for me to tell, but I have always dreamt of working on Wall Street or for a Big 4 accounting firm.”

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