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AI Model Portfolio Index
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Portfolio OverviewAI Growth - Asset Class Weights: 80% Stocks, 20% Bonds The MAI Bull-Rider Bear-Fighter Index employed by this portfolio begins with an 80:20 allocation split typical of a classic Growth portfolio. Each of eight underlying momentum strategies selects one ETF (its momentum leader) for the portfolio to hold. Advanced signal processing and AI genetic algorithms identify the selections. The names and allocation weights of the eight underlying strategies are:
Merlyn.AI Improves and Simplifies Investing
• Merlyn.AI
intelligently analyzes and automatically selects from
hundreds of ETFs on a monthly basis.
Model Hypothetical Performance![]() www.MAIindexes.com info@SumGrowth.com SumGrowth Strategies, LLC Seattle, WA |
Active Risk ReductionRisk is Not a One-Dimensional Problem Cured by a Single Dose of Diversification. The passive risk reduction provided by Modern Portfolio Theory's 1952 buy-and-hold diversification method was only just the beginning. Today, momentum in market data is well-established, and the cross-disciplinary sciences of differential signal processing and matched filter theory facilitate multiple forms of active risk reduction that forever change the game. They include; (1) risk avoidance as a byproduct of its tactical momentum algorithms designed to identify the trend leader from among a set of candidate ETFs, thus inherently avoiding laggards; and (2) market crash risk avoidance through determining the market is no longer safe (three-factor test of StormGuard-Armor) and automatically switching to an integrated Bear Market Strategy that selects the best-performing ETF from among multiple safe harbor investment candidates.
Break Through the Efficient Frontier
It's only by owning the trend leader and avoiding the laggards that
one can simultaneously improve returns and reduce risk. While
buy-and-hold performance under Modern
Portfolio Theory is bounded by the Efficient
Frontier (gray curve), the MAI Bull-Rider Bear-Fighter Portfolio
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Performance Drivers
Polymorphic Momentum: Our Performance Pedigree
MPT reliably achieves average returns with reduced risk
-- but doing
better requires changing the game. Momentum in market data
supplements MPT's long-term statistical measures by indicating how
to allocate more assets toward trend leaders and away from trend
laggards. Extracting the momentum signal from noisy market data is
the whole game. We credit these astute agents of change:
These signal processing innovations have become the technology
backbone that has enabled Ethernet, Wi-Fi, and smartphones to
perform so well. AlphaDroid's algorithms incorporate this same
technology to better extract the momentum signal from noisy market
data to improve the probability of making a better investment
decision. Additionally, our award-winning
Automated Polymorphic Momentum algorithm was
developed
to automatically adapt the momentum filter's shape and duration to (1)
the character of the ever-changing market, and (2) the character of
the strategy's candidate funds -- bonds, treasuries, sectors,
countries, and commodities are clearly quite different from one
another.
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Underlying Strategies
Merlyn - Sectors BRBF-a Investment Summary
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Merlyn - Sectors BRBF-b Investment Summary
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Merlyn - Countries BRBF Investment Summary
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Merlyn - Regions BRBF Investment Summary
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Merlyn -
Factors BRBF Investment Summary
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Merlyn -
StyleMix BRBF Investment Summary
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Merlyn -
Bonds BRBF-b Investment Summary
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Merlyn -
Bonds BRBF-a Investment Summary
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Modeling InformationForward-Walk, Backtesting, and Inception
AlphaDroid employs Forward-Walk Progressive Tuning — the
industry gold standard for performance modeling. A first period of
time is used to tune the parameters of the algorithm for use in
making decisions during a subsequent period of time. The first
period is called backtesting and is inherently flawed because tuning
is done in hindsight of the data. The subsequent period is called
forward-walk testing because it applies the tuning to an
"out-of-sample" set of data as the algorithm walks forward in time,
thus denying random events of any special tuning advantage. As the algorithm moves forward through the
out-of-sample data it periodically pauses to re-tune itself using
only past data so that it can adapt to changes in market's character
or adjust to the character of new funds participating in the strategy. Each
performance chart is thus a composite of performance estimates,
including (a) an initial tuning period determined by backtesting,
(b) a subsequent period of Forward-Walk Progressive Tuning, and (c)
a final period starting at the strategy's Inception Date (when its
design
was complete) and then walking forward in real time from there. Trade Signals, Data Source, and Performance Modeling
Underlying strategies
typically produce 3 to 5 month-end trades per year, as new trend
leaders emerge. Trade signals are
generated following the close of the last trading day of the month.
Performance modeling assumes trades are executed at the close
of the subsequent market day. End-of-day market data is provided by FastTrack and is back-adjusted for splits,
dividends, and capital gains distributions. The charts in this
document are updated daily.
Portfolio
Benchmarks are derived from
Vanguard and Fidelity mutual funds representing the basic asset
classes and are allocated accordingly. Hypothetical model performance does not include the costs of trading fees,
portfolio management fees, account management fees, or financial advisor fees.
About UsWe Believe: High-performance investment software should be for everyone, not just big Wall Street firms. SumGrowth Strategies, LLC was founded in 2009 to make simplified high-performance momentum algorithms available to independent investors through SectorSurfer and later to independent advisors through AlphaDroid. It began in 1992 when founder Scott Juds first performed a few spreadsheet experiments to determine if trends existed in market data as he was searching for a better way to invest his IRA funds. As implied by the Performance Drivers section of this document, creating a comprehensive solution turned out to be a complex, multi-faceted problem resulting in years of incremental developmental improvements, typical of all impressive modern technology. Our mission is to develop and market high-performance investment algorithms, as tools and models, to support the needs of investment advisors and individual investors alike. Meet our team members HERE and see our Innovation Timeline HERE. Disclosures and Disclaimers
The Company: SumGrowth Strategies, LLC of Seattle,
Washington (the Company) is not a registered investment advisor,
does not provide professional financial investment advice specific
to anyone's life situation, makes no custom strategies or portfolios
for anyone, and has no fiduciary relationship with its portfolio
model subscribers. The Company develops and markets high-performance investment
tools and models. Our Models: The Bull-Rider Bear-Fighter Portfolio, based on the MAI Bull-Rider Bear-Fighter Index, employs the methods and algorithms described in the Performance Drivers section of this document in combination with funds having asset class allocations as specified by the Model. These Models are published online daily by the Company for use by its subscribing financial advisor clients. Historical performance is generated by applying the current models to the specified historical period using exchange-provided price data, not from actual traded accounts. Thus, trading performance displayed is hypothetical, only represents historical conditions of the market, and does not include the costs of trading fees, portfolio management fees, account management fees, or financial advisor fees. There is no guarantee that such performance will be achieved in the future and there is no representation being made that any account will or is likely to achieve profits or losses similar to those shown. Investing in securities involves risk of loss that clients should be prepared to bear. An investment's objective, risks, charges, and expenses must be carefully considered before investing. The Data: Market data occasionally contains errors or inaccuracies and may also be changed or updated without notice. The MAI Bull-Rider Bear-Fighter Models are provided "as is" without warranty of any kind. SumGrowth Strategies, LLC, its affiliates and employees make no representation or warranty, expressed or implied as to the suitability, effectiveness, accuracy, availability or completeness of its investment models, and specifically disclaim all other warranties, expressed or implied, including but not limited to implied warranties or fitness for any particular purpose. Neither SumGrowth Strategies, LLC, nor any of its affiliates or employees shall be liable for any direct, indirect, incidental, special, punitive, or consequential damages that result in any way from use, non-use, reliance upon the information, or that may result from mistakes, omissions, interruptions, deletions of files, defects in market data, operational delays, transmission delays, failure of equipment, or failure of performance. The sole and exclusive remedy for dissatisfaction with any information or service provided by the Company is to discontinue using said information or service.
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