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).
            
          
          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.)
            
          
          
        