Frank Armstrong Ideal Index Portfolio- 2019 update
Frank Armstrong III, investment advisor and author, offers the following seven fund “Ideal Indexed” portfolio in his book, The Informed Investor: A Hype-Free Guide to Constructing a Sound Financial Portfolio (published December 16, 2003).
The portfolio employs a 70% equity / 30% fixed income split, consisting of six equity asset class funds and one fixed income fund.
In 2019 the Ideal portfolio produced the following return:
The equity slice holds a 31% portfolio allocation to international stocks. The US stock allocation has a value tilt, as the value allocations are larger than the blend and growth allocations. The asset class allocations include:
- US large blend stocks : 6.25%
- US large value stocks : 9.25%
- US small growth stocks: 6.25%
- US small value stocks : 9.25%
- US REITS : 8.00%
- International stocks: 31.00%
- US short-term bonds: 30.00%
The chart below shows the portfolio allocation (rounded values in the pie chart).
The portfolio can be implemented using the following Vanguard funds. With Vanguard having lowered the minimum investment for admiral share class funds in 2018, we now use these lower cost funds for portfolio allocations. The portfolio can be implemented with any suitable index fund or exchange-traded fund, but the Vanguard portfolios have longer performance histories.
|Small Growth Index||VSGAX||0.07%|
|Small Value Index||VSIAX||0.07%|
|Total International Index||VTIAX||0.11%|
|Short-term Bond Index||VBIRX||0.07%|
The tables below gives returns for the portfolio, using Vanguard index funds. We use admiral share class returns beginning in 2018; prior years reflect investor share performance. The returns period includes portfolio performance during the two bear markets in the 2000 – 2010 decade, as well as subsequent recoveries. Keep in mind that past performance does not forecast future performance.
|Period||Return||Standard deviation||Sharpe Ratio|
See Frank Armstrong Ideal Indexed portfolio, google drive spreadsheet for performance data