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Every ZZAlpha® 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. 

ZZAlpha LTD. focuses on stocks with sustained trading volume – it does not recommend penny

stocks, micro-capitalization stocks, IPOs, or stocks listed outside NYSE, Amex and NASDAQ.

ZZAlpha LTD. does not handle client funds, does not provide individualized investment advice and does not buy or sell securities.  We receive no compensation from anyone

based on which stock we recommend.

Performance measured against benchmarks

The ZZAlpha® machine learning engine has been applied to over 20 market segments, and results evaluated against benchmarks. As the tables below show, in many instances over 5  year, 3 year and single year periods, the ZZAlpha® recommendations substantially exceeded their benchmarks.  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, 5 and 2 equities, recommended daily (i.e. 50,  25 and 10 recommendations a week for each market segment).

Annualized returns for ZZAlpha® recommendation portfolios.

In 2010, we tuned the original algorithm based on the history 2006 to 2010 (which included the great recession).  In early 2021, we evaluated the algorithm with variations of the parameter, and found there would have been better results for the past 5 years with slightly different parameters in some cases.  We have installed the new parameters in the production system and hope to find that they perform better over the coming 5 years.

Find returns for Q1 2021 here.

Find returns for Q2 2021 here.

Find returns for Q3 2021 here.

Find returns for Q1 2022 here.

Find returns for Q2 2022 here.

"Results 2021"

Notes to tables:
1) Returns are annualized returns for multi-year periods, NOT average annual returns. Quarter, YTD and single year are returns for those periods, not annualized.
2) Benchmarks include dividend reinvestment but portfolios omit dividends.
3) We never use leverage or margin or options in evaluating results.
4) Due to a change in Sector definitions and memberships in 2018, we do not have sufficient data for some Sectors before that date.

Assumptions in performance metrics
The results shown here are produced by a mechanistic trading results simulator used to evaluate all  the ZZAlpha® 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.)

Real market testing
In testing by ZZAlpha  by actual market trading of various portfolio recommendations for various periods, the actual net returns track the evaluator model  estimates.

Returns of professional traders
The returns shown assume mechanistic purchase at a price equal to the opening price on the day of recommendation and sale at a price equal to the opening price 5 trading days later.  We expect that professional traders watching market movement at open can improve on these returns, or that speculators choosing to sell in a 3 to 20 trading day window after purchase can often sell above the price at open that we assume for closing the position.

Better returns vs larger scale
In general, smaller portfolios have higher (but more volatile) results than larger portfolios.    Please contact us for detailed information and statistics on portfolios of interest.

* To be conservative, we often use both the selected index and equal weighted population means as benchmarks in our analysis of returns.