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Market Map Model: Tactical Asset Allocation Using Low-Expense Index ETFs - Fall 2015

The Objectives of the Market Map Model

1a. To capture multi-month to multi-year market index price trend appreciation through investment in index products that track the S&P 500 or the Nasdaq 100 indices (SPDR Trust ETF (NYSEARCA:SPY), Vanguard Total market ETF (NYSEARCA:VTI), or Powershares QQQ Trust ETF (NASDAQ:QQQ)) during "equity" allocation periods, and

1b. To invest in: 1) Long dated bond securities represented by ETF products such as TLT or EDV ( Barclays Long treasury ETF / Vanguard Extended Duration Treasury ETF ) and 2) Short term Treasury market instruments during "cash" allocation periods .

2. To add long term value to the portfolio's total return by utilizing low expense and low tax ratio investment products (this under auspices of investment principles set forth by John Bogle, founder of Vanguard Group of funds).

3. To make infrequent asset allocation changes occurring on time specific, predefined dates during the course of market cycles.

4. To show consistent and comparable performance accompanied by reduced volatility relative to the S&P 500 index over many decades of diverse stock market environments and cycles (using historical testing).

5. To strategically decrease equity allocation as upside price appreciation occurs and increase equity allocation as price depreciation occurs.

Basis for and Components Applied Towards Asset Allocation Decisions

The model utilizes calculations on 7 main "price + time" based variables * ( variables' data series not subject to revision ) which are then constructed into a chronological sequence of explicit signaling steps or components.

The underlying basis of the signaling adjusts equity / bond / cash allocation exposure for inevitable periods of below-average returns after empirically defining periods of above-average performance. Conversely the signaling adjusts equity allocation exposure for inevitable periods of above average returns after empirically defining periods of below average performance.

Shown below is the historical record of the model which, during entry dates, allocates 100% capital to the SP500 when "SP500" is indicated and 100% or two 50% capital allocations during certain signature market declines and "bottoming" processes; and 75% capital allocation to Long dated bond equivalents when "BOND" is signaled, or short term treasury equivalents when "cash" is indicated.

Note: in an attempt to replicate the present interest rate environment, the interest rate on short term treasury equivalents shown during all historical "cash" periods has been set at 2% per annum.

Because of the historical efficacy of the key components and calculations, the vast improvement on equity drawdown versus buy and hold, and the "long term" investment philosophy on which the model is based, stop loss methods have been deemed unnecessary.

  • SP500 price history supplied by dqydj.net " S&P500 Dividends Reinvested Price Calculator " + Longrundata.com using VFINX

Table 1

Performance Statistics

Tables 2 & 3

20 Year Performance Periods

Signal dates

Predefined "equity" ( via SP500 / QQQ ) allocation dates occur on:

a) the last trading day in December

b) ( beginning of ) select 4th quarters

* A minority of all signals are not "predefined" but still mechanically derived

Predefined "bond" ( via TLT ) allocation dates occur on the first Friday after the 4th of July ( 3rd quarters ):

Predefined "cash " allocation ( treasury bills, short term cash equivalents ) occur on:

a) the 1st "trading" Day of February

b) the 1st Friday after July 4th

c) the last "trading" day of the year

d) ( beginning of ) select 4th quarters

Benefits of using a systematic and mechanical process

    • Fees, expenses, and commissions are low ( with use of a low cost broker, low expense index etfs, elimination of "excess" fees paid to portfolio managers )
    • The chronological format gives a "map" and "advanced knowledge" of fixed signal dates as a guide in making the appropriate allocation transactions.
    • Human judgment and subjectivity are removed from the investment process
    • The account holder has some control over position management and knowledge of the signaling.
    • "Sequence risk" is alleviated.
    • Because of the empirically derived nature of the research and robustness of data sample, a level of confidence and precommitment in executing the allocation transactions in gained, thus avoiding the behavioral / cognitive biases and ego driven mistakes that appear during discretionary portfolio management (ie. we let the process do the work).
    • We procure "long term" thinking instead of being distracted by short term "randomness", financial news flow, and media "noise".
    • "Losses" are avoided

7 main non subjective, price based variables:

    1. Mean reversion measurement using consecutive occurrences of annual overperformance or underperformance of SP500 versus a fixed baseline.
    2. Time series calculation conducted on performance of SP500 over specific months, categorized into risk profiles ( Favorable, Neutral, and High) applied towards upcoming year, indicated on 1st trading day of Februarys
    3. Relationship of long bond price and short length moving average of bond prices to long length moving average during select 3rd quarters
    4. Statistically significant equity performance in 4th quarters based on Presidential cycle
    5. SP500 price vs. long length moving average combined with the use of price oscillator
    6. * "Sell in May / Halloween" anomaly ( used for map model applied to small cap universe seekingalpha.com/article/1915041-market-map-model-2-using-small-cap-value-weighted-portfolio)
    7. Generational strings of consecutive years and derived heuristics (as calculated by #1)

Disclosure: Model is in cash position since 1/20/2015

Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.