About us


ZZAlpha serves knowledgeable investors
ZZAlpha LTD. serves institutional, professional and knowledgeable investors. We provide portfolios of recommendations exhibiting consistent, high returns with well behaved risk, liquidity and transparency. All recommendations come from the objective and thoroughly tested ZZAlpha proprietary machine learning techniques.  We supply daily equity long and short recommendations by subscription for products spanning about 40 liquidity, capitalization, index, sector and industry segments groups.

Ten years of scientific research
ZZAlpha LTD. was formed in July 2010, after 10 years of scientific research on how equities might be successfully selected by an appropriate machine learning technique.  There are many ways to poorly apply machine learning, parameters, algorithms, and models that produce inconsistent and brittle results.  We investigated (and discarded) many over the years.

Machine learning successfully responds to the unexpected
Machine learning techniques have the ability to learn in dynamic environments and quickly fashion an appropriate response, much as “black-box” auto-pilots on defense and commercial aircraft do when unexpected weather threatens safe landing or critical maneuvers.  Unlike traditional static models, machine learning techniques can respond to extreme and unforeseen events.  The machine learning technique learns every night from the day's dynamics.

Our machine learning technique evaluates the interconnections, patterns, and changes in the market on a daily basis and places those in the context of historic market behavior, using over half a billion pieces of data and 1.8 trillion calculations every night.

Dominance from scientific methodology
ZZAlpha expects to dominate the field of stock recommendations and stock analysis from machine learning techniques, supplementing and occasionally displacing conventional equity analysis methods for large investment enterprises because of its robust scientific foundation, its breadth across market segments, its consistent, high returns, and its well-behaved risk profiles.

ZZAlpha anticipates extending its effective learning techniques to seven other major international markets.

Founder

The founder, CEO and Chief Scientist, Kevin B. Pratt, 67, is a computer scientist who developed advanced analytics, machine learning, and high-performance computing methods for critical activities of the US Intelligence Community for ten years before founding ZZAlpha. He was trusted at the highest security levels.

At age 52, he earned a Masters in computer science, artificial intelligence and pattern recognition with a 4.0 gpa. He has over 100 publications. He was 7th in an international data mining competition in 2008, competing against IBM Research, Microsoft Research and groups from major research universities in the US, India and China.

Prior to 1998 he was a commercial trial and appellate lawyer in his own firm. Before that he was a partner at Fairfield and Woods, PC in Denver, Colorado, and earlier was a trial attorney at the US Justice Dept. Antitrust Division. Along his 22 year legal career, he handled securities cases for brokers, financial advisors and professional investors. He is a member of the Bar of the United States Supreme Court.

He was an Eagle Scout, and worked his way through college and law school at the University of Texas at Austin. He is a Rotary International Paul Harris Fellow.

He has been an active investor for 20 years. He is past president of the American Association of Individual Investors’ Tampa-St. Petersburg Chapter. He invests all his own funds using ZZAlpha recommendations and has hands-on experience in market dynamics.

Until June 2016, he was also  Senior Analytics Scientist at Teradata Corp.  There is NO affiliation whatsoever between ZZAlpha and Teradata or Teradata's Aster Big Data Group.

More details of his background can be found at:

LinkedIn   https://www.linkedin.com/in/kevin-pratt-709b033a

Also, see his blog:  kevinprattanalytics.com

Reminder

PAST PERFORMANCE does NOT indicate the probability of similar performance in future market conditions.

Investment in equities involves SUBSTANTIAL RISK and has the potential for partial or complete LOSS of funds invested.