Market Map model: Tactical Asset Allocation Using Low-Expense Index ETFs

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 (SPY), Vanguard Total market ETF (VTI), or Powershares QQQ Trust ETF (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 6 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 “

Map Signal Table 1923 - 1966

Map model signal table 1966 - 1999

Map model signal table 1999 - 2013

Performance Statistics

Model stats SP500 1924 - 2013

Model stats SP500 2000 - 2013

 

20 Year Performance Periods

Model vs. SP performance 1924 - 1943

Model vs. SP500 1944 - 1963

$1 growth Model vs. Buy Hold SP500

Model vs. SP500 1983 - 2003

Model vs. SP500 2004 - 2013

Signal dates

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

a) the last trading day in December

b) beginning of select 4th quarters

Signals that are not “predefined” but mechanically derived are also generated

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

Entry dates

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

a) the 3rd Friday of January

b) the 1st Friday after July 4th

c) the last “trading” day of the year

d) beginning of select 4th quarters

Allocation

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 judgement and subjectivity are removed from the investment process
  • The account holder has some control 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 and cognitive biases and ego driven mistakes that appear during discretionary portfolio management (ie. we let the process do the work). We can think “long term” instead of being distracted by short term “randomness” and financial news flow and media “noise”.
  • “Losses” are avoided                                                                                                                                                                                                                                                                                                                                         . . . . . . . . . . . . . . .

*  6 main non subjective, price based variables:

  1. Mean reversion measurement using consecutive occurrances 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
  3. Relationship of Long Bond price and long length moving averages during select 3rd quarters
  4.  Statistically significant equity performance in 4th quarters based on Presidential cycle
  5. SP500 price vs. long length moving average and price oscillator readings
  6. “Sell in May / Halloween” anomaly                                                                                                                      ( 7. Generational strings of consecutive years and derived heuristics (as calculated by #1) )

Future posts will illustrate expanded model choices for capital allocation.

Disclosure: We are long QQQ

tinyurl.com/nx9yt3l

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2 thoughts on “Market Map model: Tactical Asset Allocation Using Low-Expense Index ETFs

  1. Mr/ Ms Map,
    Thank you, I am enjoying the series and I keep hoping your next one shows the math behind the key components that you calculate. Are you going to make that available in future articles?
    C Hover

    • Thanks for joining us.
      As many who publish research are associated with large investment or academic institutions, we are a small entity and are not compensated ( directly ) for our research. We are exploring the legal issues of “intellectual” property/ ( exposing mathematical formulas ) pertaining to the publishing of this type of information and protective actions to implement if the option is viable.
      We will discuss the components in real time as asset allocation decisions unfold / present themselves.

      As many site will disclaim, This blog is not a solicitation or offer to buy or sell any securities and information presented here should not be construed as investment advice. Past performance should not be considered indicative of future performance.

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