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Conservative Portfolio
Prudent Momentum 40:60

Portfolio Overview

Conservative Asset Class Weights:  40% Stocks, 60% Bonds

Conceptually modeled after the classic Conservative risk portfolio's 40:60 allocation split with an important difference: This portfolio is composed of four underlying tactical momentum strategies, each of which selects one ETF for the portfolio to hold. The names and allocation weights of the four underlying strategies are:

▶  Stylebox Index SPDR: 30%;        ▶  Global Regions iShares: 10%;       ▶  Bonds II SPDR: 30%;        ▶  Bonds II iShares: 30%.
 

Tactical Momentum Execution

Each of the four underlying strategies uses a rules-based tactical momentum algorithm to evaluate a set of candidate ETFs at the end of each month, at which time the trend leader is selected to be the strategy's representative in the Portfolio for the subsequent month. Tactical momentum improves the odds of owning next month's trend leader while simultaneously avoiding portfolio-draining laggards.
 

Integrated Bear Market Strategies — Because Yin Follows Yang

Each underlying strategy further includes StormGuard−Armor, a market direction/safety indicator that determines when to switch to an integrated Bear Market Strategy that selects the trend leader from a set of safe harbor investment candidates during a market downturn.
 

Risk is Not a One-Dimensional Problem Cured by a Single Dose of Diversification

Prudent Momentum Portfolios apply a double dose of risk-dilution and of risk-avoidance to address multiple sources of risk.
▶   Portfolio-of-Stocks:  Employs broadly diversified ETFs to dilute risks associated with owning a single stock or bond.
▶   Tactical Momentum:  Means never owning trend laggards within a set of candidate ETFs — a critical form of risk avoidance.
▶   StormGuard−Armor:  Escapes the next market crash by sensing when the market's unsafe — a critical form of risk avoidance.
▶   Portfolio-of-Strategies:  Further dilutes the risk associated with any single strategy by owning multiple divergent strategies.

Model Performance

Prudent Momentum Portfolio

  www.AlphaDroid.com     PrudentMomentum@AlphaDroid.com     SumGrowth Strategies, LLC  Seattle, WA

Relative Performance

The performance of the Prudent Momentum 40:60 Portfolio is plotted below alongside its four underlying Strategies , its performance benchmark , and the Industry's set of Consensus Asset Class Portfolios  1  through  5  (per white paper Satisfying the Prudent Man). While buy-and-hold performance under Modern Portfolio Theory is bounded by the Efficient Frontier (gray curve), Prudent Momentum Portfolios break through the Efficient Frontier by owning the trend leader reducing risk in four ways.

Prudent Momentum Portfolio

Methodology - Tactical Rotation

Prudent Momentum Portfolios use four underlying tactical momentum strategies to identify each of the four ETFs it will hold. The strategies uses our award-winning Automated Polymorphic Momentum algorithm to determine which one of its 12 candidate ETFs is the trend leader and thus most likely to outperform its peers next month. Its StormGuard-Armor algorithm evaluates market safety in three ways to determine if a Bear Market Strategy should instead select the trend leader from a set of safe harbor ETFs. Each set of candidate ETFs is assembled in a manner that supports the asset class allocation weight objectives of the portfolio. 

Prudent Momentum Portfolio

  www.AlphaDroid.com     PrudentMomentum@AlphaDroid.com     SumGrowth Strategies, LLC  Seattle, WA

 

Performance Drivers

A Primary Focus on Risk

Risk is not a one-dimensional problem cured by a single dose of diversification. Prudent Momentum Portfolios apply a double dose of risk-dilution and of risk-avoidance to better address the many sources of risk, including (1) single-stock risk, (2) loss of momentum, (3) bear markets, and (4) single-strategy performance failure.

StormGuard−Armor:  Better Crash Protection

Better market-crash protection requires incorporating more information, not just tweaking a simple algorithm. StormGuard−Armor achieves its superior performance by combining three distinct indicators: Market-Index Price Trend, Institutional Momentum, and Hi/Low Value Sentiment. The three indicators are combined using fuzzy logic to produce the final StormGuard−Armor decision.

Integrated Bear Market Strategy:  Downside Profit Potential

When StormGuard−Armor indicates the market is no longer safe, a Bear Market Strategy automatically takes charge and selects the trend leader from a set of safe harbor candidates such as money market funds, bond funds, gold funds, and U.S. Treasuries.

Polymorphic Momentum:  Our Performance Pedigree

MPT reliably achieves average returns with reduced risk — 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:
Jegadeesh & Titman publish in 1993 what is considered the seminal academic paper proving momentum exists in market data.
Eugene Fama, Nobel Laureate, confirms in 2008 that momentum truly is "the premier market anomaly" that's "above suspicion."
Claude Shannon, National Medal of Science, in 1948 proves signal-to-noise ratio sets the probability of making the right choice.
J. H. Van Vleck, Nobel Laureate, showed in his 1946 paper that a matched filter design produces the best signal-to-noise ratio.
Samuel H. Christie, Royal Society Fellow, in 1833 developed differential signal processing to reduce common mode system noise.
Finally, because the character of bonds, treasuries, sectors, countries, and commodities are quite different from one another, our award-winning Automated Polymorphic Momentum algorithm has been designed to automatically adapt the momentum filter's shape and duration to (1) the character of the strategy's candidate funds, and (2) the changing character of the market.

Modeling Information

Tactical Model Risk  vs.  Current Holding Risk
The Riskalyze Risk Numbers in this document reflect hypothetical long-term portfolio performance. However, some AutoPilot web pages display a short-term portfolio Risk Number that assumes (incorrectly) that current ETFs will be held for another 6 months ... without the benefit of trades that tactically avoid trend laggards and bear markets. Fortunately, some pages have an option to display the Risk Number in SMA mode (separately managed account) that includes the full portfolio history with the benefits of tactical momentum and which result in the same lower Risk Number found here. The above example values are for the Prudent Momentum 60:40 Portfolio in May of 2017 when it held MDYG, IJJ, JNK, and HYG.

Forward-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 emerges.  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 Fidelity Asset Manager mutual funds to represent corresponding classic asset allocation portfolios. Hypothetical model performance does not include the costs of trading fees, portfolio management fees, account management fees, or financial advisor fees.
 

  www.AlphaDroid.com     PrudentMomentum@AlphaDroid.com     SumGrowth Strategies, LLC  Seattle, WA

The Four Underlying Strategies

Strategy #1: Bonds II iShares

The Bonds iShares Strategy seeks to identify the trend leader among numerous iShares bond ETFs.

Prudent Momentum Portfolio

Strategy #2: Bonds II SPDR

The Bonds SPDR Strategy seeks to identify the trend leader among numerous SPDR bond ETFs.

Prudent Momentum Portfolio

 

  www.AlphaDroid.com     PrudentMomentum@AlphaDroid.com     SumGrowth Strategies, LLC  Seattle, WA

 

Strategy #3: Stylebox Index SPDR

The Stylebox Index SPDR Strategy seeks to identify the trend leader among ETFs representing the major investment classes of stocks.

Prudent Momentum Portfolio

Strategy #4: Global Regions iShares

The Global Regions iShares Strategy seeks to identify the trend leader among index ETFs representing geographic global regions.

Prudent Momentum Portfolio



  www.AlphaDroid.com     PrudentMomentum@AlphaDroid.com     SumGrowth Strategies, LLC  Seattle, WA


Other Prudent Momentum Portfolio Models

There are five Prudent Momentum Portfolio family members, each conceptually modeled after one of the five classic asset class portfolios, but with our performance enhancing twists. Each portfolio holds four underlying strategies with an asset class focus and allocation consistent with the portfolio's target definition, as detailed in the table below. Model performance is hypothetical.

Prudent Momentum Portfolio

About Us

We 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: Prudent Momentum Portfolio Models employ 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 Prudent Momentum Portfolio 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.

  www.AlphaDroid.com     PrudentMomentum@AlphaDroid.com     SumGrowth Strategies, LLC  Seattle, WA