These oscillating concerns occurred at the end of the year against
the backdrop of:
- Unsustainably high stock market returns beginning in November of 2017, coinciding with investor optimism about US corporate tax cuts.
- The perception that the stock market was “old.” Many observers perceived that the bull market that began in 2009 was getting old and it was time for declines.
- A US stock market that had already displayed signs of peaking. The US stock market started sending warning signs about peak earnings (lower Price-to-Earnings ratios and persistently high Price-to-Book ratios) beginning in January of 2018.
CPM’s Market Resilience Index (MRI) conditions provide
additional perspective. During 2018, the
measure that indicates the long-term trend of stock prices shifted from being decidedly
positive to being decidedly negative. This shift took place in May 2018. From that time on, it was unlikely that the
market would attain new highs before visiting dramatic new lows.
From the MRI perspective, the age of the bull stock market
was not 2018’s most distinct feature.
The bull market that began in 2009 was rejuvenated in 2015/6 with a moderate
decline in what I have called a phantom
bear market. Therefore, the
age of the bull was not a primary issue.
Instead, the degree of euphoria in late 2017 and in January
2018 was a distinctive feature. The
Macro and Exceptional Macro indicate levels of euphoria. The level of the Macro MRI in January of 2018
was among the five highest over the last 100 years. The other highly euphoric weekly readings were
8/21/1997, 9/28/1986, 10/21/1955, 9/13/1929.
This comparison is cautionary.
Large declines followed the periods of high euphoria in the cases of
1997 (the 1998 declines associated with LTCM crisis mentioned below), 1986 (the
large 1987 decline), 1929 (the stock market crash of 1929). Regarding the Macro peak in 1955, the market
declined almost 20% in 1957. While these
are the most euphoric periods, there have been many highly euphoric over the
last 100 years.
Since we expect that a major price decline may follow a peak
in the Macro MRI, it is reasonable to wonder if it would be best to sell stocks
as the Macro MRI trends higher – before it peaks. Testing over the last 100 years of market history
indicates that selling in the presence of high euphoria is not a strategy that
delivers high return and low risk. Doing
so causes one to miss the periods when high euphoria persists for long periods
of time.
Instead, it is best, in general, to sell after a peak in the Macro MRI has occurred. For example, if we had sold US stocks at a level of euphoria (i.e., a level of the Macro MRI) that represented the highest point in the 2007-8 period, we would have missed most of the returns for 2017. Diamond returned about 39% in 2017, and this level of return can compensate for the challenges of 2018.
Instead, it is best, in general, to sell after a peak in the Macro MRI has occurred. For example, if we had sold US stocks at a level of euphoria (i.e., a level of the Macro MRI) that represented the highest point in the 2007-8 period, we would have missed most of the returns for 2017. Diamond returned about 39% in 2017, and this level of return can compensate for the challenges of 2018.
Our portfolios did well in 2017 because the algorithms were
trained on the similar earlier periods, such as 1920–1929, 1945-1966,
1990-2000. The experience over these periods
indicate that it is better to pick up the pieces in years like 2018 than to
miss the years like 2017.
Timeline
- We were invested in the stock market through 2017 and into early 2018.
- After the peak in the Macro MRI in early May, the Focused 15 Investing portfolios became more completely defensive in early July of 2018 after the Micro MRI had peaked. A “Harvest 2” designation ended at that time and the target weights called for zero weight in stocks in model portfolios based on the D5 signal set (i.e., signals driving Diamond and Zircon). The expectation was for declines in stock prices.
- On July 20, 2018, the algorithms recognized that the period of very low resilience passed without major declines. The algorithms started recommending higher target weights for stocks and prices did indeed move higher. My commentary reflected the deteriorating longer-term market conditions.
- By October 12, 2018, the algorithms indicated that the short-term period of resilience was nearing an end and issued the last “Harvest 2” week, which had indicated a good time to remove assets from the stock market.
- There was a bounce after the midterm elections (November 6) that occurred as generally expected.
- By November 23, the Diamond model portfolio had returned about lost about 1% year-to-date and was about 1% behind the performance of the default portfolio.
To this point in 2018, the algorithms were performing in a manner that I consider to be within the range of normal behavior.
- By November 30, the Diamond model portfolio had a return of about 2% for 2018. However, this was about 5% behind the return of the default portfolio.
- Through the rest of 2018 and in the beginning of 2019, we had false starts on a short-term rally in stock prices.
I reviewed the algorithms and their performance for 2018 and
have these observations:
- High Volatility (or Variability) Alone was Not the Problem - I evaluated the level of variability of stock prices in November and December. Some market observers have said that the level of variability is at historic highs. I have not found strong evidence to support this view. I evaluated weekly variability, daily variability, and changes in weekly and in daily variability. While high, the levels of variability were not unusually extreme.
- Reversals Without Establishing New Trends - Several price reversals in November and December did not initiate a new price trend as quickly as has been typical over the last 100 years. News-of-the-day elements seemed to have a big impact but conditions that developed since late 2017 are set the stage. Without these conditions, I believe the same November and December events would not have produced the same market dynamics.
- "Slow-Twitch" was Better for 2018 - Some of the Focused 15 Investing model portfolios avoided losses and did quite a bit better than their default mixes during 2018. The prime example of this is sg129, “Institutional: D5, LC/SC, Multi-sector Bonds”. The better performing model portfolios relied more heavily on algorithms that moved more slowly. Because of this, they stayed out of the market and were not affected by the exogenous forces in November and December. Consistent with the nature of those algorithms, those portfolios have low allocations to the stock market as of January 14, 2019, despite the rally in US stocks. These slower responding model portfolios have lower return than the others when viewed over a longer time horizon, as shown in the figure above. However, for the 2018, their level of responsiveness fit the events of the year nicely.
- None of the algorithms should be changed at this time - In general, I do not believe any of the model portfolios should be changed. The rotational portfolios based on the Macro Rotation Signal, which I describe in a blog post dated December 30, 2018, would not completely address the performance issues of 2018. Over the last several weeks, I have evaluated numerous other ways of using the Macro Rotation Signal. While there are some promising options, thus far, none are clearly superior to Diamond in terms of risk, return, and ease of implementation. I will provide updates on this research as it progresses.
- Losses Will Occur from Time to Time – The following section looks at the longer-term historical simulation of the current algorithms as used in Diamond. This information shows that there are periods of loss in otherwise attractive performance histories.
The CPM investment strategies have been evaluated for their
strength over 100 year of stock market history.
Over that time, there have been numerous periods of performance declines
like what we experienced in 2018. A
recent one is 1998. During the 1990s and
especially in the late 1990s, US stock market gain were high. During 1998, the surprise collapse of the
Long-Term Capital Management (LTCM) investment manager rippled through the
capital markets and required intervention from regulators and other financial
institutions. Performance of the model
portfolios was poor during that period.
This is indicated by arrow “A” below.
Arrow “B” indicates the end of 2018. The blue line is the model portfolio. The tan line is the default mix.
On the figure above, we can see that the model portfolio
(and the stock market) hit their highest levels in early 2018. This makes the recent period more extreme
when viewed within the calendar year.
The figure below shows a general simulation of Diamond since
1919, with the 1998 decline indicated by arrow “A.” The green line is the model portfolio. The brown line is the DJIA.
Once can see several instances of large declines prior to
1998. Even with these declines, the
traded portfolio is superior to holding the DJIA over the long period of
time. Subscribers should understand that
losses will occur.
Performance of Diamond Compared to Alternatives
Diamond is the main model portfolio. Up through early 2018, most subscribers were
using it and this section focuses on it. I also list other model portfolios for comparison. All of the portfolios and performance figures are based in US dollars based on data supplied by Bloomberg. CPM offers publications for investors in other countries. As an aside, the best performing Focused 15 Investing model portfolios are those offered to Australian investors. Please contact me for information about these model portfolios.
Figure 1 below shows the performance for Diamond compared to alternatives that have been described in the past:
Figure 1 below shows the performance for Diamond compared to alternatives that have been described in the past:
The Dow
Jones Industrial Average (DJIA)
Vanguard’s
Balanced Index Fund – VBINX
The
Russell LifePoints Growth Strategy - RALAX
Section A shows the performance of Diamond, along with two
other model portfolios. Mosaic (sg129)
is included because it consists of all major sleeves used in other model
portfolios and it was one of the original model portfolios in the publications. It holds 8 ETFs. I have also shown Zircon, which is just like
Diamond but less aggressive. Both
Diamond and Zircon hold 3 ETFs.
We will focus on the performance of Diamond. In order to have a fairer comparison to other
funds, we need to reduce the returns of the Focused 15 Investing model
portfolios. This is because the performance
for the other funds reflects actual costs associated with the management of
those funds. The average expense ratio
for the alternative funds is 1.13%. To
be conservative, I subtracted 2.00% from the returns of Diamond for the
comparison. For example, the Diamond
model portfolio returned 14.9%, annualized, over the 52-month period since the
inception of the publication. The return
adjusted for costs is therefore 12.9%.
The variability of returns is not adjusted for costs.
Figure 1
As one can see from the figure above, Diamond (adjusted for
costs) returned -8.9% for 2018. The DJIA
returned -4.6%. The worst performance among
the alternatives is the Russell LifePoints fund (RALAX) at -8.1%.
Over the roughly 52 months since the inception of the
publication, Diamond (adjusted for costs) returned 12.9%, which is better than
any of these alternatives. The
alternatives returned 1.9% to 9.6% over the 52-month period.
The ratio of the return to variability is an important
statistic. The higher this number the
better. The ratio for Diamond (adjusted
for costs) is 0.95. The highest ratio of
any of the alternatives is 0.71.
Figure 2 below adds sections D and E. Section D of the table shows the returns of alternative
ETFs that do active asset allocation. This
information is from Bloomberg. Diamond
(adjusted for costs) is within the range of performance for 2018, but has
higher returns and a higher ratio over the 52-month period than any members of
this set of alternatives.
Figure 2
The conclusions are similar across these different
groups. Diamond had a poor 2018, but its
longer-term performance is still quite good compared to alternatives.
Performance of the Range of Model Portfolios for 2018
This section reviews the performance of 14 model portfolios. This section is detailed. The bottom line is that most model portfolios
performed better than their default mixes in 2018 and over the 52-month
period.
There are fourteen Focused 15 Investing model portfolios in
two main publications. Of the fourteen,
only two had positive returns the year (12/29/2017 through 12/28/2018). However, 11 of the 14 performed better than
their default mixes. The performance of
the default mix is the return of ETFs in the model portfolio but held at
constant weights over time. All but one of
the fourteen has less variability of returns than their defaults. Less variability is a desirable feature.
Figure 3 below shows the model portfolio returns for the
fourteen main portfolios for 2018. The
publication for individuals has model portfolios with fewer ETFs for easier
trading. The publication for
institutions holds portfolios that tend to hold more ETFs and fit commonly used
asset classes. Note that these figures
are from the newsletter itself; an amount for fees has not been subtracted.
Column A shows the sleeve group number, shown as “sg’ on the
weekly publication. Column B shows the
return of the model portfolio for the period.
Only two of the portfolios had positive returns (shown in green). Column
C shows the variability of returns – the lower the number the better. There are many numbers and I present a simpler
graphical representation later.
Figure 3 – 2018 Performance Information
Diamond returned -6.9% for the period. Zircon returned -2.3%. For comparison, the DJIA returned
-4.6%.
A convenient way to understand performance is to review the
return and variability of the model portfolio less the corresponding figures
for the default mixes. These figures are
shown in columns F and G.
In Figure 4 below, Columns D and E shows the return and
variability of the default mixes for the different model portfolios for 2018. Our
focus is on columns F and G, which show the difference between the traded and
the default model portfolios.
Figure 4 - 2018
For all but three of the fourteen model portfolios, returns
were higher than their default mixes (column F). For those underperforming their defaults
(Diamond, Emerald, and Zircon) the underperformance with less than one-half of
a percent for the period. In all but one,
the variability was lower than that of the default mix (column G).
A few of the model portfolios had performance quite a bit
better than their default mixes (Mosaic Plus sg213, Mosaic sg129, and sg
122). All of these use what might be
called a “slow-twitch” loss avoidance technique, which did not attempt to
respond to the rapid return reversals that we experienced during late 2018. I discuss this issue in greater detail
later.
The figures below show this information graphically. The orange boxes indicate the return and
variability of the default mix for the model portfolio. The blue box indicates the return and
variability for the traded portfolio. The
green arrows connect the model portfolio t its default. The DJIA and Vanguard’s VBINX (60/40
Stock/Bond ETF) are shown for comparison; the names are listed in the table
above.
Figure 5
Ideally, we want the blue boxes to be very high and far to
the left of the corresponding orange box.
That position would indicate that the Focused 15 Investing loss
avoidance approach is working as intended.
For 2018, Diamond and Zircon had lower variability (a positive), but the
returns were slightly less than the defaults (a negative).
The figure below shows the broader list of model portfolios. It shows only the sg numbers for
clarity.
Figure 6
This shows that for 2018, the traded portfolios generally had
better performance characteristics than their default mixes. Most of the blue boxes are to the left and
higher than their default mixes (orange boxes) for the 2018 period.
Model portfolio sg129 did very well in 2018 compared to its
default. The green line connecting the orange
box representing the return and variability of the default mix and the model
portfolio is the longest in the figure. The
model portfolio for sg129 is the second from the top.
Figure 7 below is in the same format but shows performance
since the inception of Focused 15 Investing on July 18, 2014.
The first page of the weekly publications shows the general performance expectations for the different model portfolios based on their returns since January of 2000. I consider these expectations to be attainable.
The only model portfolio that goes against the general trend on the figure above is sg200; its default mix is located at the lower left corner of the cluster. This is Onyx. Onyx is designed to be different from the others. It invests in four ETFs that have low variability, SHY (US 2y Bonds, 10y Bonds), XLU (Stocks of Utility companies), XLP (Stocks of Consumer Staples companies), and UST (US 10y Bonds x2). It shifts money into the ETFs expected to have higher returns. In contrast, all the other sleeves and model portfolios made from those sleeves invest in high return ETFs and then shifts away from them when they are expected to have low resilience. While Onyx’ performance has not been outstanding, I believe it may be useful in the future when interests rates are higher and move in a historically more normal manner. Thus, Onyx has a special role to play. I have two versions of Onyx, one with weekly trading and one with monthly trading. The monthly version is shown. I highlight Onyx because it might be useful for pairing with an aggressive version of Diamond. I will provide updates on this over the next few months.
Appendix 1 – Alternative Asset Allocation Funds (n=114)
| RYDEX SERIES NOVA FUND-INV |
| KINETICS MARKET OPP-ADV A |
| WELLS FARGO DIV CPTL BLD-A |
| BOSTON TRUST ASSET MGMT FD |
| WALSER PORT AKTIEN USA |
| PLUMB BALANCED FUND |
| BRIDGES INVESTMENT FUND |
| STATE ST US EQTY VIS |
| VALUE LINE CPTL APPREC-INV |
| SIT BALANCED FUND |
| AMERICAN BALANCED FUND-A |
| JANUS HNDRSN BALANCED-S |
| BLCKRCK BALANCED CAPITAL-I |
| WF INDX ASST ALLOC-A |
| FIDELITY PURITAN FUND |
| VANGUARD WELLINGTON-INV |
| AMG CEP BALANCED-Z |
| VANGUARD BALANCED INDEX-INV |
| FIDELITY BALANCED FUND |
| USAA GRWTH & TAX STRAT |
| ABERDEEN INCOME BUILDER-INST |
| DODGE & COX BALANCED |
| LKCM BALANCED FUND |
| TOUCHSTONE BALANCED-A |
| GEORGE PUTNAM BALANCED-A |
| T ROWE PR PERS STRAT GRW |
| FIDELITY STR DVD & INC |
| PGIM BALANCED FUND-Z |
| EQUINOX AMPERSAND STRAT-A |
| TRANSAM M/M BALANCED-A |
| COLUMBIA BALANCED FUND-I |
| HARTFORD BALANCED HLS-IA |
| CALAMOS GROWTH & INCOME-C |
| T ROWE PRICE BALANCED FUND |
| MAIRS AND POWER BALANCED FD |
| T ROWE PR PERS STRAT BAL |
| TETON WESTWOOD BALANCED-AAA |
| DREYFUS BALANCED OPPORT-A |
| STATE FARM BALANCED FUND |
| EATON VANCE BALANCED FUND-A |
| GREEN CENTURY BALANCED-INV |
| TRIBUTARY BALANCED-INST |
| PIONEER CLASSIC BALANCED-A |
| JAN HND BAL-A US ACC |
| FIDELITY ASSET MANAGER 85% |
| FEDERATED MDT BALANCED-IS |
| HARTFORD BALANCED FUND-A |
| WF GROWTH BALANCED-ADM |
| ARCHER BALANCED FUND |
| ISHARES CORE AGGRESSIVE ALLO |
| HENNESSY BALANCED FUND |
| AMER CENT BAL-INV |
| AMER FNDS INC OF AMER-A |
| MFS TOTAL RETURN FUND-A |
| AMER CENT STR ALLOC AGG-INV |
| FDLTY ADV STR DVD & INC-C |
| CALVERT BALANCED FUND-A |
| FIDELITY ASSET MANAGER 70% |
| FIDELITY ADV ASST MGR 85%-A |
| JOHN HANCOCK BALANCED-A |
| CAVANAL HILL ACTIVE CORE-INV |
| FIDELITY ASSET MANAGER 60% |
| JPMORGAN DIVERSIFIED-L |
| FIDELITY ADV ASST MGR 70%-A |
| WESMARK BALANCED FUND |
| FRANKLIN BALANCED-A |
| FIDELITY ASSET MANAGER 50% |
| INVESCO EQUITY & INCOME-A |
| HOLLAND BALANCED FUND |
| SCHARF MULTI-ASSET OPP-INST |
| FIDELITY ADV ASST MGR 60%-A |
| PNC BALANCED ALLOCATION FD-I |
| PUTNAM DYN ASST ALL BAL-C |
| AMER CENT STR ALLOC MOD-INV |
| AMERICAN BEACON BALANCED-INS |
| FIDELITY ADV ASST MGR 50%-A |
| BRUCE FUND INC |
| BMO US DOLLAR MONTHLY IN-ANL |
| ARCHEA FD-NORDAM-B1 |
| THRIVENT BALANCE INCOME PL-A |
| OAKMARK EQUITY & INCOME-INV |
| MAINSTAY BALANCED FUND-I |
| MANN & NAP PRO BLND MAX |
| FPA CRESCENT FUND |
| IVY BALANCED FUND-A |
| CORNERCAP BALANCED FUND |
| IRONCLAD MANAGED RISK FUND |
| BUFFALO FLEXIBLE INCOME FUND |
| CGM MUTUAL FUND |
| LK BALANCED-INS |
| DELAWARE FOUND MOD ALL-A |
| SATURNA SEXTANT CORE FUND |
| EATON VANCE HEDGED STOCK-A |
| GLDMN SCHS INC BLDR-A |
| USAA CORNERSTONE MODERATE |
| VIRTUS TACTICAL ALLOC-A |
| USAA CORNERSTONE MODER AGGR |
| MSIF GLOBAL STRATEGIST-A |
| GLDMN SCHS ALT PREMIA-A |
| MANNING & NAPIER INC-PB MO |
| 1789 GROWTH & INCOME FUND-C |
| DAVIS APP & INCOME FUND-A |
| PALMER SQ STRAT CRDT-A |
| ICON RISK-MANAGED BALANCED-C |
| AMERICAFIRST TACT ALPH-A |
| DWS REAL ASSET-A |
| TOEWS TACTICAL MONUMENT |
| JAMES BAL GOLDN RAINBOW-RTL |
| EAS CROW POINT ALTERN-A |
| VILLERE BALANCED FUND |
| VIRTUS RAMPART M/A TRND-A |
| AMERICAFIRST QUANT STRAT-A |
| TOEWS TACTICAL OPPORTUNITY |
| CATALYST HEDGED FUT STRAT-A |
