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Certified

Every ZZAlphaŽ stock recommendation is notarized by a trusted third party at the time we e-mail it our subscribers, and is then permanently archived.  That helps assure that our performance and risk information can pass stringent audits. 

ZZAlpha is data driven: we work with objective facts to forecast the price changes and make recommendations, and we work with objective facts to evaluate performance and risk.  We provide transparency of recommendations and results that is seldom found in the investment advice arena. 

Performance measured against benchmarks

The ZZAlphaŽ machine learning engine has been applied to over 3 market segments, and results evaluated against the IWB benchmark. As the table below shows, in many instances over 5  year, 3 year and single year periods, the ZZAlphaŽ recommendations substantially exceeded their benchmark.  The results come from recommendations that were certified each morning before market open, to assure they support stringent audit.

We show here results for portfolios of  10 equities, recommended daily (i.e. 50, recommendations a week for each market segment).


Annualized returns for ZZAlphaŽ recommendation portfolios.


Portfolio 5yr 3yr 1yr Max YoY drawdown%
BigCap100 size 10 16.9 7.3 8.5 34
VeryHighLiquidity size 10 21.7 13.3 38.7 56
HighLiquidity size 10 33.3 26.9 46.6 59
BigCap100 Sector size100 12.7 5.1 19.7 32
IWB Benchmark size1000 18.8 7.2 24.2 _

Notes to tables:
1) Returns are annualized returns for multi-year periods, NOT average annual returns.
2) Benchmark includes dividend reinvestment but portfolios omit dividends.
3) We never use leverage or margin or options in evaluating results.


Assumptions in performance metrics
The results shown here are produced by a mechanistic trading results simulator used to evaluate all  the ZZAlphaŽ machine learning engine recommendations.  (The evaluation is entirely distinct from the machine learning technique.) The results shown assume no trading commissions, no spread, no slippage and no dividends.  These results assume purchase at a price equal to the reported opening price on the day of recommendation and sale at the reported opening price five trading days later. (Of course, traders often can and do improve on this pricing assumption by scrutinizing the market flow and news at the open or during the day.)