Frequently Asked Questions
I've heard timing systems sound great, but performance
figures are based on theoretical models that are tuned to historical
data. In reality, they switch too often. And they have to be
right twice: getting out of the market and getting back in. And
they don't beat buy and hold. Right?
Yes, many
timing systems are guilty on all counts. Historical data are used because it
gives us a means to discover patterns and relationships that affect investment
performance. The trick is to generalize as much as possible, so that future
patterns are recognized when similar to past patterns and adaptable when not.
The work reported in the Hulbert Financial Digest, the popular
chronicler of market timers, shows that the top-performing timing services just
about match a buy and hold strategy, but at much lower risk (less downside
volatility).
Buying and
holding is okay in theory but dangerous in practice. In reality, few investors
are capable of buying and holding. Emotions get in the way of needed analysis
and discipline. Panic selling and comfortable buying usually lead to selling
low and buying high. Moreover, I'm not so sure that authentic buy-and-holders
exist. Those who decry timing are perhaps "closet" timers themselves.
Examples include raising cash by money managers, rebalancing portfolios among
asset classes, and buying and selling individual stocks, whether based on
fundamentals, technicals, rumors, tips, emotion, or whims.
A buy-and-hold
strategy can under-perform for long periods of time. For example, the stock
market lost out to both inflation and money markets over the ten years spanned
by the 1970s and again in the 2000s “lost decade,” yet the timing model's
backtested standard portfolio more than doubled the market's performance. It
can also decimate portfolios by the end of a severe bear market, particularly
if funds need to be withdrawn shortly thereafter. The 20% declines in 1990 and
1998 and the 34%
crash in 1987 were followed by reasonably fast recoveries of capital for those
who stayed invested. But the 48% grind in 1973-1974 delayed new highs for seven
years, as did the 49%
2000-2002 bear market. The nearly three-year pounding starting with the crash
in 1929 devastated Dow-based portfolios by some 90%; it then took 25 years for the Dow to
regain its former high! The 2007-2008 bear market took the S&P 500
down by 52%.
The bear in 2009 marked a low 57% under the all-time peak in 2007. The Index rescaled that peak 5 ½ years
later. Note that buy-n-hold assumes that
investor never sell through panic or fear, not once, never seriously consider
that the return of capital can take precedence
over the return on capital.
The déjà vu markets are more subtle and
frustrating when misfortunate points in time are picked. The Dow hit a high of
995 in 1966 and finally crossed 1000 six years later, with intervening roller-coaster
rides of down 22%,
up 48%,
down 36%,
up 73%,
and then... the 48%
debacle of 1973-74. Once again the Dow sank below 1000... for another six years
into 1980. It then seesawed above and below 1000 for a couple more years,
bottoming at 777 in August, 1982, and not piercing above 1000 for good until
late that year. (See chart.) Imagine
living as a buy-and-hold investor through that grueling 16-year odyssey! How
many did? Eventually conviction is
broken during serious and prolonged declines… and it’s sell, Sell, SELL… all
too often near the bottom.
The usual
investor behavior that’s well documented in the literature is to chase (buy)
stocks late in bull-market cycles and shed (sell) stocks late in bear-market
cycles. Moreover, the typical investor
carries high cash balances, degrading performance during the roughly 70% of the
time the stock market is in an uptrend.
And keep in mind that a -50% portfolio axing takes a subsequent +100% gain to break even; a -25% trim requires +33%. It takes time
to recover such losses. Time is a real
loss that can’t be recovered. A good
timing system avoids major downturns (preserves capital and time), so we don’t have to spend time afterwards recovering capital
to break even.
Various
studies of S&P 500 losses show the following results based on different
perspectives:
·
5%
losses three times a year, 10% losses once a year, 15% losses once every two
years, 20%
losses once every three to four years.
·
More
than 5%
losses about half the time, more than 10% about one-third the time, and more than
20% about one-quarter the
time.
·
Probability
of a 5%
or more decline in any 12-month period about 50%, 10% or more about 30%, 15% or more about 20%, 20% or more about 10%.
The
model’s focus is on the 10% plus
losses. We ignore the noisy, mostly
random 5%
or so fluctuations as far too difficult to anticipate. Moreover, note that the last set of
probabilities are unconditional, that
is, not conditional on the market’s state of “health.” The timing model would
bend these probabilities in a favorable direction, as its probability score is conditional on the state of the market.
In other words, low scores, primary downtrends, and sell signals are associated
with more risk and greater likelihoods of declines.
The buy-n-hold
mantra (usually accompanied by buy-n-hope) popularized during the remarkable
super-bull markets of the 1980s and 1990s was shattered by the “lost” 2000
decade and its triple bear markets.
Holding through severe down cycles postpones dreams at best, shatters
them at worst. And how about the ulcers? Yet, long term, the stock market is
the only game in town that leads to financial wellbeing (see Did you know that...? below).
The conduct of
the market over long periods is cyclical,
revealing sustained uptrends lasting months to years, followed by persistent,
but shorter, downtrends. The 1966-82 market was a shining example of
cyclical behavior, a timer's dream market. The period 1982-1990 also offered
persistent cycles, surrounding an increasing secular (long-term) trend, unlike
the flat trend of the earlier period. The 1990s market was unusually acyclical, with a steep
uptrend. Cyclical behavior was back during the 2000-2009 decade. Markets
mirror our human affairs, the push and pull of conflicted greed (envy too) and
fear. And they are seriously complex,
especially in reaction to often irrational and emotionally fragile
investors. Prices are influenced by
fundamentals, yes, but clearly determined as well by our human foibles,
negating the belief that efficient markets set the “right” price.
Market prices
show cyclicality and randomness. The shorter the time frame, the more
influential the random effect; conversely, the longer the time frame the more
dominant the cyclicality. That's why the
model is mid-term to long-term oriented and describes its primary trends to reduce randomness, by the chosen percentage
fluctuation (more than 8%) and number of weeks (at least 8) that define primary
trends (within these cycles). The timing model's objective then is to detect
changes in these cyclically-influenced primary trends as they happen.
It's not a model that tries to forecast the future level of the S&P weeks
or months ahead, a considerably difficult exercise that's been largely
unsuccessful in the literature with which I'm familiar. Rather, it's a
model that tries to identify cyclical inflection points, the tops and bottoms
of primary cycles (named primary trends
in my work), the points at which the primary trend changes direction. To
be successful, a model does not have to
be right twice, out at tops and in at bottoms, as critics maintain; it
"just" needs to sell higher than its buy points and buy lower than
its sell points, ideally with few trades.
Have you heard
this one from the critics? If a timing system misses the best x weeks (or
days, months) over some investment horizon, its performance drops to about half
that of buying and holding, assuming its performance is the same as buying and
holding over the remaining weeks. Yes, true. But this view is
decidedly one-sided and self-serving; they fail to mention what happens if the
timing model avoids the worst x weeks, which after all, is a capital preservation objective of all
timing systems. Looking at the 1970-2017 period, investment performance
drops by 40% should a timing system miss the 30 best weeks, while remaining invested the rest
of the time. Let's be self-serving ourselves. If a timing system avoids
the 30 worst weeks
and remains invested the rest of the time, its performance beats buy and hold
by 45%. Ok, let's be fair. Suppose the timing system misses the 30
best weeks but avoids the 30 worst weeks, while remaining invested the rest of
the time. Now, the timing system beats buy and hold by 5%. In the
final analysis, these exercises are simplistic and futile, because (1) best and
worst weeks are more likely to occur within respective cyclical up and down
trends, not randomly, and (2) cycle-aware timing models are designed to detect
these trends, not best or worst weeks per
se.
|
Backtested Per Year
Returns Basis S&P 500, with Reinvested Dividends (Last row is Total
Return) |
|||
|
Period |
Buy and Hold |
Standard Timing |
Aggressive Timing |
|
1973-1982 |
6.6% |
17.2% |
21.7% |
|
1978-1987 |
15.2% |
24.9% |
34.1% |
|
1988-1997 |
18.1% |
20.7% |
28.1% |
|
2000-2009 |
-1.0% |
10.6% |
21.6% |
|
2000
peak - 2017 |
5.8% |
12.8% |
20.5% |
As the
accompanying table illustrates, both timing and the timing period
are everything. The standard strategy based on this year’s tested timing
model just beat buy and hold over the placid (acyclical) 1988-97 period.
The edge for timing was more decisive over 1978-87, a period that included a
down 27%
bear market over 21 months during 1980-82 and a 34% bear market that
lasted four months in 1987. The model trounced buy and hold during
1973-82, a span that housed the devastating 1973-74 bear that sliced portfolios
nearly in half. The 2000s “lost decade” included three severe bear
markets, once again giving the timing model a serious capital-preservation and
return advantage. Through the end of
last year buy-n-hold returned just 5.8% from the bull market peak in 2000. Just
5.8% total return over 17+ years. The timing strategies well outperformed
during this time. Timing models feed on
cycles for sustenance.
The timing
model has been live since the middle of 1989, slightly besting buy-n- hold
through last year based on total return, but with much less risk, and with far
higher risk-adjusted return. The 1990s ushered in an era of historically
low downside volatility, except for the brief near-bear markets in 1990 and
1998. And we have not had a bear market since the last one ended in 2009. It's
nearly impossible for a timing model, indeed for anyone as the records show, to
beat buy-n-hold in markets that exhibit very little cyclical behavior and no
punishing downturns. The live model's advantage clearly became apparent
during the 2000-2002, 2007-2009 bear markets and the lost decade
2000-2009.
The model is
built around probabilities; it assesses probabilities. It's far from exact, but
pretty good regarding high vs low probabilities, which is why we have the gap
between buy/sell bands, to account for inaccurate probability estimates.
Keep in mind
that we can expect
losses from time to time, although the model has stepped aside from serious
losses in its live performance. The model’s actual historical record shows a
reduction in downside volatility, as would diversification, especially across
asset classes such as bonds, utilities, REITs, and precious metals.
And, to be
honest, the live implementation (using out-of-sample data) of a model rarely
performs better than its backtested results (using in-sample data). Since 1990, the annually-revised live models under-performed the
current theoretical
model by about 5 percentage points per year (10% v 15%), a
metric called shrinkage. The question
is: Does its use improve my investment performance... and soothe my nerves? For
me the answer is yes,
on both counts.
I am more concerned about the return of my
money than the return on my money.
A politician [strategist, technician, quant] needs the
ability to foretell what is going to happen tomorrow, next week, next month,
and next year. And to have the ability afterwards to explain why it didn't
happen.
On Capital Preservation
Investment strategies have a dual
objective: Preserve capital during bad times and build wealth during good
times.
The avoidance of major losses during steep
declines is far more important than capturing all gains during uptrends.
Drawdowns leading to permanent losses
drain wealth and victimize emotions.
Experiments in behavioral
economics show that the pain of loss is twice as great as the joy of gain,
a result consistent with the psychological value of preserving rather than
losing capital.
So, we want to avoid as much pain as
possible (loss), but also experience joy (gain).
As the Wall Street adage says: Cut your
losses short and let your gains run.
On time
If you’re young, time is on your side,
given the magic of compounding; otherwise, time is not on your side.
Remember that a 50% loss of capital
needs a 100% gain back to break even and usually requires years of lost time in
doing so.
Capital preservation is important; lost
time might be even more important.
We can rebuild capital if we have the
time, but we can’t recover time.
Near and during retirement capital
preservation dominates time. Big losses must be protected, time is short.
Disciplined investment strategies
address both capital preservation and time.
Investment truths?
Do nothing, stand still, be patient, or stay
the course (at your own risk).
Time in the market
beats timing the market (see preceding box).
Don't time the market
(unless you follow a proven timing system).
Cash is trash (except
during bear markets).
Cash is king (except
during bull markets).
Practice dollar cost
averaging with new purchases and you will be fine (ok, but meanwhile you might
lose half your current capital).
Stocks outperform in
the long run, stick to your plan (Buy & hold and don't sell? We’re all dead in the long run anyway).
Just what is a bull or bear market? And what’s the difference between cyclical
markets and secular markets?
Based on the commonly-cited 20% change to define cyclical bull and bear markets, the S&P 500 printed 16 cyclical
bear markets and 15 cyclical bull markets (technically, we’re now in the 16th
bull market) since 1929. The average
bear lost 38% over 17 months, with half losing more than 34%
in 17 months; the average bull gained 144% over 45 months; half the gains
exceeded 101% over 44 months. The
45-month average length of a bull cycle is also the average time between bear markets, almost 4 years. It’s not a zero-sum game; it does pay to be
in the market most (about 70%) of the time.
The bull-market high in
March, 2000 was followed by an 18-month 37% cyclical bear. This low was technically followed by a
lightening cyclical bull market that gained 21% over four months,
ending in January, 2002. Another
cyclical bear followed into October, 2002, ending a 9-month 34%
cyclical bear decline, very close to the averages. The next cyclical bull market ran five years
into October, 2007, yielding a 101% gain. A 13-month
bear market followed, axing 52% from the index by November, 2008. This was followed by the shortest bull market
on record, a 24% surge over two months. Consistent with recent volatility,
the succeeding bear market shaved 28% over a record-short two
months. We’re in a 16th bull
market as of this writing, confirmed by a greater than 20% gain since March,
2009.
Secular markets are not
clearly defined, but do span multiple cyclical bull and bear markets. A secular bear market is characterized by
lower cycle highs and lower cycle lows (a downwardly sloping M, the opposite of a secular bull’s upwardly sloping W). Secular bear
markets show lower returns than average; secular bulls higher returns than
average. Flat secular markets show more or less zero cumulative returns, as
cyclical bears and bulls cancel each other out. The five-year secular bear that
ended in 1942 dropped 60%; the somewhat flat eight-year secular bear that ended in
1974 shaved about 34%. The great secular
bull that started in 1974 ended in March, 2000, a record 26 years with a
stunning gain of 2353%, more than doubling the previous record gain from 1942 to
1966. From an alternative vantage point, the secular trend from 1966 to 1982
was essentially flat, with a 9% gain. A secular bull measured from 1982 to 2000 posted a
gain of 1389%. The secular trend from 2000 to 2007 is flat,
characterized by both new cyclical highs and lows (Ms and Ws); the view from
2000 to 2009 is that of a sharp downward (bearish) secular trend with an
overall loss of 57%. The secular trend
from 2000 through 2017 rises at an increasing rate, by about 62 points per
year. This flattens further if the
annualized return of 5.4% over these 18 years is adjusted for 2.1% inflation,
giving a real return of 3.3%. This secular trend could continue as the economy
(individuals, private and public sectors) unwinds debt from the debt
super-cycle of the past 20 years or so… and if elevated valuations such as
price/earnings come down from rising profits.
Moreover, the secular trend might very well turn downward if economic
freedom in the US continues its decline (11th for the linked report). Or… it’s possible that the 2009 bottom marked
the end of that secular bear and we’re now in the 9th year of a new secular
bull that could get extended life from business-friendly policies in
Washington. We will know only in
hindsight. Long, somewhat flat secular
markets with many bull and bear cycles are tailor-made for good timing systems.
Price to Earnings
ratios (P/Es)
often define the beginning and end of a secular market. The 
average P/E for the S&P 500 is about 16. At the bottom of a secular bear (beginning of
a secular bull) the P/E is below 10. Many
secular bulls end (secular bears begin) with a P/E in the mid-20s. The 1966-82 flat secular started with a P/E
of 24 and ended with 7. A major bull
secular followed into 2000. The secular top in 2000 was characterized by a
blowout P/E of 44 (what were we thinking?); at the 2009 bottom the P/E stood at
15, suggesting that the current flattish secular trend may have more to go.
It's part art and part
technicals that define secular trends, with far from universal agreement on the
number, length, and returns that describe them.
According to this article, five secular bulls from
1877 through 2009 lasted an average 16 years with average gain 415%; five
secular bulls averaged 10 years and 65% loss. The jury is still out on the secular trend
since the 2009 low.
While we prefer secular uptrends for better and more reliable market
performance, returns are also available during secular flat to down trends, as
the index cycles above and below the secular trend. The model has done a reasonable job of riding
a good portion of the upward waves (cyclical bulls) and not sticking long with
the downward waves (cyclical bears). See
its live performance in Reality
Check. In particular, compare
performances in the 2000s summary table during the 2000-2009 secular bear
market.
By the way… impressive countertrend rallies during bear markets are far
more common than we might believe, even dramatic, with frequent 50% or so
retracements of the gap between low and high The twin-bears over 2000-2002 (-48%) had 6 weekly gains greater than +4%, the biggest at +7%; the shorter twin bears spanning
2007-2009 (-56%) marked 5 rallies over +4% in
one week, the best clocking in at over +10%.
Cyclical bear markets and recessions are often linked by pundits in the
media, many saying that we’re not having a bear because a recession is not in
the cards,
while others say a recession is coming and so a cyclical
bear market is certain. Take a careful look at
this chart. This shows that we’ve
had 9 cyclical bears and 10 recessions since 1950, with the closely allied twin
cyclical bears in 2000-02 and 2007-09 in the table above joined as one (they
had very short bull markets in between).
Of these, 4 bears started before and ended within recessions (1956-57,
1968-70, 1973-74, 1980-82), 4 recessions were free of bears (1953-54, 1960-61,
1980, 1990-91), 3 bears did not experience recessions (1961-62, 1966, 1987), 1
twin bear entirely included a recession (2000-02), and 1 twin bear coincided
with a recession (2007-09). Of the 9
cyclical bears, 6 were associated with recessions, 3 were not. In all 6 associated
cases (2/3 overall), the start of a bear market presaged a recession.
Recessions and cyclical bears are related, but one is neither a
sufficient nor necessary condition for the other. In particular, an upcoming recession does not
necessarily mean an upcoming cyclical bear market. Moreover, a cyclical bear market does not require a recession. But, the most severe cyclical bears (1973-74,
twins 2000-02, twins 2007-2009) have overlapped with recessions. And the start
of a cyclical bear signals a 67% likelihood that a recession is imminent,
although this probability is based on a very small sample. Note that cyclical bear markets are easily
identified by the 20% rule, but recessions are confirmed well after the
fact.
Having said this,
the model does not directly concern itself with bull or bear markets per se;
rather it strives to detect the beginning of primary up or down trends of 8% or
more over at least eight weeks. These primary trends do operate
within the context of cyclical bull and bear markets. Primary uptrends often last months during
cyclical bear markets as countertrend rallies; 14% of primary uptrends are
present within cyclical bears, 86% within cyclical bulls. Surprisingly, 60% of
primary downtrends occur within cyclical bull markets, the minority 40% within
cyclical bears. While it’s possible to
exploit a primary uptrend within a bear market, or a primary downtrend within a
bull market, experience with the model shows that it’s difficult to do so. And that such counter-signals are likely to
be short-lived. During cyclical bears
and steeply downward secular trends, intermediate to long-term strategies
should pretty much remain in capital
preservation (money market) modes.
Eventually, patience will be rewarded with a new bull market and the dry
powder to exploit it. Hanging on to your
money for future deployment (capital preservation)
is, after all, the main benefit of a good timing system.
Sentiment
Cycle
Here is one
version of a sentiment cycle that starts at the bottom of a bear market:
Despair (all or most out)
Disbelief (that a new bull
market has started, frozen in the headlights)
Acceptance (cautious
buying)
Euphoria (all or most in)
Disbelief (that a new bear
market has started, frozen in the headlights)
Acceptance (cautious
selling)
Despair (here we go again).
Long
disbelief/acceptance stages promote long trading ranges (sometimes many years,
as in SPX 1966-1982) that are often followed by big moves (sometimes many
years, SPX 1982-2000) when the market breaks either up or down.
History doesn't repeat itself, but it does rhyme.
Bull markets are born
in pessimism, grow on scepticism, mature on optimism
and die on euphoria..
Sir John
Templeton
The bull market story
is always most compelling on the highs. The bear market story is always most
compelling on the lows.
I'm unhappy with switching 100% into stocks at a buy signal,
or into a money market at a sell signal. Does it have to be all or nothing?
No. By a 100% switch we mean the stock portion of a portfolio. And as stated elsewhere, this all-or-nothing trade is used as a means to compare performance to buy-n-hold. To generalize, a portfolio would raise cash during a sell signal and add stock positions during a buy signal. Your stock allocation should be a percentage that you can sleep with and would be dependent on factors such as age, net worth, propensity for risk, and other personal attributes.
If you want to
be up to 60% invested in stocks, move 60% or less of your overall portfolio
into stock funds at a buy signal. If you always want to have at least 25% in
stocks, keep that amount in stock funds during a sell signal. Or progressively
shift funds following a switch signal. For example, you could move some
proportion into stocks just after a buy signal. Then wait for a market pullback
of, say, 3-5% and move another portion into stocks. Given
a sell signal, another partial standard strategy execution might be to “lighten
up” on stocks, shifting part of the stock portfolio to a money market fund,
while waiting for either the next buy signal or continued market weakness or a
rally to “take additional stocks off the table.” Alternatively, you could phase in a sell
switch, as in moving into cash over a period of weeks.
Pullbacks
present opportunities to add to stock positions, consistent with risk profile
and while the model is on a buy signal.
Conversely, rallies are opportunities to reduce stock positions, again
consistent with risk profile and while the model is on a sell signal. Or,
regardless of the signal, rallies and pullbacks might tell us to sit tight if
comfortable with risk exposure while we wait for the model’s continuing
assessments.
Slow, cautious
exit and entry strategies during signal changes make sense during high market
turbulence and a series of short switch signals, as was the case in 2015. Also see about diversification, next FAQ.
You favor domestic stock ETFs and mutual funds. What about diversification? How about bonds, gold, commodities, foreign
equities, and individual stocks?
From
Investopedia: An
exchange-traded fund or ETF is a security that tracks
an index, a commodity or a basket of assets like an index fund, but trades like
a stock on an exchange. ETFs experience price changes throughout the day as
they are bought and sold. By owning an
ETF, you get the diversification of an index fund as well as the ability to
sell short, buy on margin and purchase as little as one share. Another
advantage is that the expense ratios for most ETFs are lower than those of the
average mutual fund. When buying and selling ETFs, you have to pay the same
commission to your broker that you'd pay on any regular order.
A closed end fund or CEF is
a publicly traded investment company that raises a fixed amount of capital
through an initial public offering (IPO). The fund is then structured, listed
and traded like a stock on a stock exchange.
Unlike regular stocks, a closed-end stock fund represents an interest in
a specialized portfolio of securities that is actively managed by an investment
advisor and which typically concentrates on a specific industry, geographic
market, or sector. The stock prices of a closed-end fund fluctuate per market
forces (supply and demand) as well as the changing values of the securities in
the fund's holdings. Many CEFs, by the
way, are bond funds. A popular stock-based CEF these days is CUBA, which I
have traded from time to time.
NOTE: Important advantages of ETFs and CEFs over mutual funds
are no restrictions on number of trades and the ability to trade at any time of
the day. A disadvantage: Dividends are issued, but not reinvested; instead they
flow to the core cash account. Dividends
and their reinvestment significantly add to performance over long time periods,
making up about 50% of S&P 500 total return since 1926 (its lifespan) and
about 30% since 1970 (model’s lifespan). Also, when you trade ETFs and CEFs pay
attention to the difference between the market price per share of the ETF or CEF
and its net asset value (NAV), the
value per share of the underlying securities (plus cash) represented by the ETF
or CEF. Ideally, we would want to buy at
a discount to NAV (a negative value expressed as a percent difference between
price and NAV). Finally, pay attention to the expense ratio. Many ETFs that
track major indexes have low expense ratios (about 0.1% or less), unlike those that mimic more unusual strategies.
To follow the
standard strategy for a portfolio's stock investments suggests the use of an
indexed mutual fund or ETF that tracks the S&P 500. Mutual fund families
such as Fidelity and Vanguard include menus of index funds. A low-cost
alternative to individual stocks and mutual funds is to trade ETFs, such as
Spiders (S&P 500 Depositary Receipts, symbol SPY)
through your broker, a derivative that mimics the S&P 500 and "looks,
sounds, and acts" like a stock. If
you want low fees, tax efficiency, and diversification among many categories
then indexing on a broad index such as the S&P 500 or Wilshire 5000 is the
way to go. Keep in mind, however, that our performance figures include
dividends and their reinvestment, which over time account for a significant
portion of total returns. It’s worth
remembering that ETFs issue dividends to the core cash account, but do not
reinvest these dividends.
And remember
that the timing model addresses only the equity portion of a portfolio, which
generally should not be the entire
portfolio. It’s a good idea to diversify portfolios with other asset classes such as bonds, precious
metals, commodities, international securities, and real estate.
Diversification is a method of
managing risk, besides that offered by the model, by owning asset
classes that have different risk profiles, meaning that correlations across
asset classes are less than correlations of investments within the same asset
class. Think of higher correlation as
higher synchronicity, whereby cross-asset prices tend to move in the same
direction. Note that the timing model addresses only the equity portion of a
portfolio, which generally should not be the entire portfolio. My
portfolio is modestly diversified by including ETFs with traditionally-lower
correlations with the S&P 500, such as bonds, maybe gold, and sometimes
index-based commodities.
Diversification
also includes investment styles
(value vs blend vs growth) and company sizes (large cap vs mid cap vs small
cap). The S&P 500, for example, would be classified as a large-cap blend
Index by Morningstar. The model’s buy and sell trades are focused on the
S&P 500, which is correlated with many stock indexes, although imperfectly
due to differences in investment styles and company sizes. This means that investing in a menu of stock
mutual funds and ETFs that reflect styles and sizes different from the S&P
500 would be appropriate during a buy signal, as it would add another (weaker)
layer of diversification, yet keep movement in a direction consistent with the
buy signal. I also invest in several stock ETFs that are highly but imperfectly
correlated with the S&P 500, the model’s focus, which adds style diversification.
The so-called diversification
quilt is an effective visual that illustrates diversification
strategies and the dynamics over years regarding returns.
Note, however, that diversification among stock
classes and countries may help little during a crash or
bear market, given today's interconnected global world and the resulting high
correlations among stock indexes. During these times, a good timing system
helps. For example, in 2008, a painful bear-market year, the S&P
500 plunged -37%, including dividends; the live model’s standard strategy
mitigated the loss to -13%, while the aggressive
strategy eked out a gain of +3%. Here’s how some of my
favorite stock ETFs fared in 2008, confirming that, at least in these examples,
stock/country diversification mostly made matters worse: SPY -38% (benchmark);
QQQ -42%; IWM -35%; IWR -42%; IWS -40%; VEU -45%; VWO -55%. Including bonds as an asset class would have helped:
TLT +28% (wow, as the Fed seriously eased interest rates and
liquidity); LQD -3%; BND +2%. As for gold and utilities: GLD +5%; XLU +5%.
If you wish to
allocate 40% to assets other than stocks, then move up to 60% of your portfolio
between stocks and money market funds at switch signals. The ETF database is a good source for alternative ETFs, as is Morningstar.
By the way,
"domestic" funds and ETFs usually hold a fair percentage of offshore
stocks. Also, large domestic companies are multinational, and many other
companies profit from overseas economies, so there's actually a substantial
international exposure within most domestic stock ETFs and mutual funds. Moreover, as of early 2018, the S&P 500
includes asset classes like real estate (2%), health care (14%), utilities
(3%), energy (6%), technology (21%), financial services (17%), and Europe/Asia
(1%).
It's
also okay to use individual stocks, but keep in mind that their price behavior
can differ substantially from the market's behavior. At a sell signal, you
should consider either dumping or reducing exposure to individual stocks,
unless you have reason to believe that they will swim upstream, or the tax
consequences are too much for you to "bear". Consider buying your
favorite individual stocks at a buy signal, because an up
trending market is often favorable for most stocks.
We
can think of diversification across asset classes as strategic diversification and changes in asset classes based on
timing as tactical asset allocation. Here
is a good explanation of these strategies.
Jeff
Bezos, of Amazon fame, once allegedly said, “We are stubborn on the vision. We
are flexible on the details.” The model's signals reflect both the strategic
(vision) and tactical (details) investment views. Should we or should we not be exposed to the
stock market. The micro or operational or tactical speaks to the implementation
of the strategic decision: what percent exposure, what diversified asset
allocation, and what specific investments that reflect the tradeoff between
return and risk.
CAUTION
Care with the intra-day timing of ETF
investments during volatile market action. The flash
crash
on August 24, 2015 included some startling early-trading
deviations between the prices of ETFs and the net asset values of their
respective underlying securities. Some
examples… QQQ was down 17%
yet the underlying index was off 10% at the same time; the S&P 500 was
down 5%
but its proxy SPY fell 8%.
And these are very large, liquid ETFs. The less liquid the worse the difference
of price from NAV, although some big ones were also hit pretty bad, such as
Guggenheim S&P 500 Equal Weight ETF (RSP) off 43% from its previous
close, but down “just” 4%
by the end of the day. SPLV, the S&P
500 “low volatility” ETF, dropped as much as 48% from the close of the
previous day. Many ETFs were down 30-50% while their underlying
indexes were off around 10-15%.
Bid-ask spreads early during that day were much wider than later in the day,
especially near the open. Best not trade
at the open under such volatile circumstances, although a quick-witted investor
might buy a favorite at a big discount to the underlying NAV during a selloff…
or sell at a big premium during a buying panic.
Or just wait until ETFs adjust to their underlying indexes as trading
continues... don't use sell stop or market orders during such unstable
action. If you must trade use “limit
orders” stipulating the highest price at which you would buy (or the lowest at
which you would sell).
A rising tide lifts “all” boats.
A retreating tide “drops” all boats.
Anonymous
I would like to be more aggressive with some investments at
buy and sell signals. What strategies are available? And what do you do during
buy and sell signals?
At a sell
signal, aggressive portfolios might consider the purchase of mutual funds or
exchange traded funds (ETFs) that anticipate down markets (short the markets). So-called inverse mutual funds include ProFunds
Bear (BRPIX) and Rydex Inverse S&P 500
(RYURX). These mutual funds are designed as an inverse to the S&P
500, with a beta of -1
(multiplier -1x where x is daily
return). For example, if the S&P 500 declines 5% in one day, they
should theoretically advance 5%. Other alternatives include ETFs that
short an index: DOG for the Dow30, SH for the S&P 500, and PSQ for the
Nasdaq 100. Ultra funds are still more
aggressive alternatives. These include
either a mutual fund such as ProFunds UltraBear (URPIX), which shorts the S&P 500 200% (-2x),
or one of several -2x ETFs: DXD for the Dow30, SDS for the S&P500, and QID
for the Nasdaq 100. Super aggressive portfolios might look into -3x funds
and ETFs, such as SPXU for the S&P 500. We can also directly short the S&P 500 through Spiders
(SPY), the Dow Industrials via Diamonds (DIA), or the Nasdaq 100 Index using
Nasdaq-100 Shares (QQQ). These ETFs act like index funds but trade like
shares and are bought and sold in stock brokerage accounts. The most
aggressive alternative for very sophisticated investors is buying index put
options for the Dow Jones Industrials (^DJX), S&P 500 (^GSPC), S&P 100
(^OEX), Major Market (^XMI), Nasdaq 100 (^NDX), or Russell 2000 (^RUT).
At a buy
signal, ETFs that mimic our main indices include the previously mentioned DIA,
SPY, and QQQ. For example, Rydex Nova (RYNVX) has a
beta of 1.5, meaning that its expected daily return is 50% better than the
S&P 500 return in an up market (multiplier 1.5x). The dark side of
this flip is an expected 50% greater loss in a down market! Ultra funds offer
more aggressive alternatives. ProFunds UltraBull (ULPIX) has a
multiplier of 2x. Ultra ETFs leveraged 200% (2x) include DDM for the Dow30, SSO for
the S&P500, and QLD for the Nasdaq 100.
So-called 3x long funds and ETFs are also available, such as UPRO for
the S&P 500. Buying Spiders,
Diamonds, or Nasdaq-100 Shares on margin are other aggressive buy
strategies. Even more aggressive alternatives for sophisticated investors
include index call options.
These risky
strategies can turbo-charge returns, but should be exercised only by
sophisticated investors with high risk tolerances and cast-iron stomachs. They
can do serious damage to portfolios should the market go against the model’s
signal and trade. Keep commitments to these leveraged instruments, if at all,
to a small portion of a portfolio, the actual percentage depending on tolerance
for risk and confidence in the model’s signals.
See CAUTION following
the table.
The
accompanying table summarizes the simplest implementations of the model's
standard and aggressive strategies using commonly-traded mutual funds and ETFs.
|
Easy Portfolio Implementations |
|||
|
Signal |
Strategy |
Position |
Sample Investments |
|
Buy |
Standard |
100%
long S&P 500 (1x) |
Any S&P 500 index mutual fund, such as Fidelity's Spartan 500 Index (FUSEX) or Vanguard 500 Index (VFINX). SPY (
"spiders") or VOO
or IVV shares. Note that dividends may not be reinvested
for these ETFs. In
general, add to stock holdings. |
|
Aggressive Ultra Aggressive |
150%
long S&P 500 (1.5x) 200% long S&P 500 (2x) 300% long S&P 500 (3x) |
Rydex Nova mutual fund (RYNVX). ProFunds UltraBull
(ULPIX) Rydex S&P 500 2x Strategy H (RYTNX) SSO
shares. UPRO shares |
|
|
Sell |
Standard |
100% T-Bills |
Any
money market mutual fund that emphasizes Treasuries. In general, reduce stock holdings. |
|
Aggressive Ultra Aggressive |
100%
short S&P 500 (-1x) 200%
short S&P 500 (-2x) 300% short S&P 500 (-3x) |
Mutual fund such as Rydex Inverse S&P 500 (RYURX) or ProFunds Bear (BRPIX). SH
shares. ProFunds UltraBear (URPIX). SDS
shares. SPXU shares |
|
Ultra aggressive strategies leverage either 200% or 300% long during a buy
signal (instead of the 150% long used in these pages) and 200% or 300% short
during a sell signal (instead of 100% short as used here). ETFs make it easy to implement these ultra strategies, as in the preceding table. The following
tables show performance results for alternative strategies, first for the
back-tested model and next for the live models that would be used in practice
(ignoring expenses for proper comparisons to buy-n-hold).
For example,
buying SSO (2x) during buy signals and SDS (-2x) during sell signals, then
according to Ultra Aggressive C in the first table, starting with $1,000 in
1970, would (in theory based on backtesting) end with $3.7 billion this past year, versus about $54 million for our stated
1.5x/-1x aggressive strategy and about $2 million for the standard
strategy. Yeah, you read that right…
that’s billion. This would pump the annualized return to 37%
(versus 25% and 17%). Ultra Aggressive D blew up the spreadsheet. ;-)
Results are
more realistic in the live table, where the start is 1990 vs 1970. Note that ultra strategies that use -1x Sell in the live table
perform better than those using -2x Sell, suggesting that when actually (live)
using the model we’re better off keeping sells at -1x inverse, rather than
leveraging up to -2x… and pumping buy leverage up to 3x.
Ok, this ain’t gonna happen in the top
table. Don’t even think about it. Let’s not let greed get the best of us as we
rush to convert our portfolio to an ultra strategy. In practice, expense ratios, trading fees,
and especially decay from holding daily ultra leveraged
instruments for long time periods in
very up and down volatile
environments can seriously reduce expected performance. On the other hand, low volatility can give
better than expected results, should the dominant direction of the volatility
favor the model’s position (long or short).
Keep in mind
that so far in practice the aggressive strategy (second table) underperforms
the standard strategy, and with more risk to boot. The
live implementation of a backtested system will normally underperform the
optimized backtested version, a reality called shrinkage. See actual
results in Reality
Check. But then Ultras A and B did
outperform in the live trials seen in the second table. So, if you would rather implement an ultra strategy based on the model, think ultras A & B,
but don’t bet the bank. Allocate a small
percentage (maybe 5 to 10%) of the overall portfolio to any ultra
aggressive strategy.
A disadvantage of aggressive (leveraged, ultra)
long and short mutual funds and ETFs is volatility
decay or beta slippage, the
negative performance effect of daily up/down volatility and leverage over
periods greater than a day, as these funds are designed to replicate the
multiplied return for the underlying index on a daily basis. Here is a simple
arithmetic example of decay, taken from an article in A
Wealth of Common Sense:
If you invest $100 into the 3x leveraged ETF and the index
is up 5% the ETF would be up 15% and go to $115. If the index
then loses 5% the next day your 15% loss would drop the market value to $97.75.
An investment in the
simple index would be worth $99.75 in this scenario ($100 to $105 to $99.75)
meaning your loss of -2.25% in the 3X leverage is much higher than the index
loss of -0.25%.
Unlike their unleveraged (1x)
counterparts or the use of margin for aggressive strategies, a 150% (beta 1.5
or multiplier 1.5x) long fund or ETF will not likely give a 15% return if the
underlying index gains 10% over an extended measurement period, and may even
show a loss, depending on multiplier, volatility, and time horizon. For
example, the year-long buy signal in 2009 resulted in a gain of about 23% for
the S&P 500 index (27% with reinvested dividends), but just 30% for Rydex Nova (31% not counting the expense ratio), not the
approximately 35% that would be expected by a simple 50% increase over the
index. These results are reasonably
close because the index in 2009 showed mostly daily gains rather than daily
losses. Note that the live aggressive
portfolio in Reality
Check returned 34% in 2009, closer to the 35% we would expect. This is because the model updates weekly, not
daily, therefore reducing the volatility component in the calculation.
The greater the multiplier (most ultra
ETFs use 2x or 3x for long funds and -2x or -3x for inverse funds), the longer
the time period, and the greater the up and down daily volatility during the
signal’s period, the greater the potential for loss decay, a result shown
mathematically based on compounding formulas. When volatility is very low
an ultra fund can return more than expected,
sometimes spectacularly so, if either daily gains
dominate and the multiplier is positive or daily losses prevail and the
multiplier is negative. See also this
and this
and these
tables on pages 85 (for SH) and 120 (SDS) within the Prospectus for ProShares Funds.
Again: These instruments and strategies
are risky and should constitute a small percentage of a portfolio, if at all.
I’m often asked what I do during buy and
sell signals. To generalize, I let the model identify primary trends and
trade in that direction: high cash/low stocks and small shorts during sells
and low cash/high stocks during buys.
At a buy signal I’m up to 70% in equity ETFs. SPY or VOO is my
core investment, both of which track the model’s own benchmark, the S&P
500. This allocation is up to half of my
stock investments. Next in importance
are QQQ and IBB, to cover technology
and biotechnology long-term themes. I
might fill the rest of the stock portfolio with optional and various
combinations of IWM
(small-cap), VBR (small-cap value), IWR (mid-cap), IWS (mid-cap value), VEU (all-world x-US), SCZ (international small caps), and VWO (emerging markets, to acknowledge
about half of world GDP). The remaining
30% or more includes a money market, and especially if interest rates are
falling, maybe one or two bond funds such as TLT
(long Treasuries), LQD (corporate
investment grade), JNK (corporate high
yield), PIMCO’s total return BOND, and
Vanguard’s total bond BND, the latter
two as good globally diversified bond ETFs.
Straddling these might be XLU
(utilities), which can behave either like a stock fund or a bond fund (based on
dividends and the direction of interest rates) or both. In fact, the behavior of utilities is
included in a monetary composite within the model. I also consider a small position (5 to 10%)
in GLD, if its chart looks good and
geopolitics warrant. I also might
include a commodity index, such as DBC
or DBA.
And I might allocate a small portion to a REIT, such as VNQ, depending on my read on the economy,
interest rates, and housing. I also
might allocate 5% to SSO
(2x buy) or UPRO (3x buy) during a 5%
or more pullback, if I have high confidence that the buy signal will stick,
with stop
losses, and a short-term time horizon (days to weeks).
Here is one way to keep it diversified
and simpler during a buy signal: Buy VOO (25%), VTI (25%), VXUS (20%), BND
(15%), maybe GLD (5%), maybe DBC or DBA (5%), and a money market. Vanguard’s VTI is a total U.S. stock market ETF that
covers nearly 4 thousand stocks across styles and capitalizations; Vanguard’s VXUS blankets the international stock
space (ex-U.S.) with nearly 6000 securities in developed and emerging markets.
See also the Ivy
portfolio. And take a look at ARK for some innovative but volatile ETFs
that I dabble in with small positions.
And for you risk seekers out there looking at the cryptocurrency space: I also trade GBTC with
limit orders and a very small position of about 1%.
At a sell signal I sell most if not all stock ETFs, proceeds stashed in a
money market fund, maybe a portion in an enhanced short-maturity fund such as MINT or GSY, and maybe up to a 10% position in SH, an S&P 500 inverse. Moreover, I might keep some bond funds and/or
utilities, depending on my reads on the direction of interest rates and flights
to safety (the risk-off trade). And if
we have turbulent geopolitics or the specter of inflation, I might buy TIP (inflation-protected US Treasuries)
and keep or buy GLD as well. That’s
about it.
Please keep in mind that I don’t give
individualized investment advice. I’m
willing to tell you more or less what I
generally do, as here, but not what you
should do.
I'm not sure how to interpret the terms "Annualized
Return," "Years to Double," and "Morningstar Risk" in
the performance comparisons?
The label
"Annualized Return" is the annual compound rate that would give the
ending amounts over the given time horizon. For example, a return of 40% in the
first year and a loss of 10% in the second year is equivalent to an annualized
return of about 12.2%. If we were to apply the 12.2% rate over each of two
years, we would end up with the same amount of money as if we had applied 40%
one year and -10% the other year. Note that the annualized rate is not a simple
average! In the literature, this is
sometimes abbreviated AR (Annual Return), CAR (Compound Annual Return), CAGR
(Compound Annual Growth Rate), and ATR (Annualized Total Return). See this for its
mathematical calculation.
The term
"Years to Double" is the number of years it would take to double an
investment for the given annualized return. It's an alternative measure of
performance that's easy to relate to: "Now let's see, if I park my money
in a money market with returns equivalent to T-Bills and the average
performance over the 1990s is repeated, my investment should double in about 15
years. Umm..."
These pages
emphasize risk as quantifiable losses or downside volatility by different
measures: Morningstar risk, max drawdowns, and Sortino standard deviation. There are other forms of risk, such as the
risk of lower returns than needed for life style or retirement; of running out
of capital in retirement; of not keeping up with inflation; sequence of returns
risk, meaning precipitous portfolio capital declines resulting in forever lost
shares during early retirement, bear markets, and significant withdrawals. But
in real time, in our day to day investment existence, the most visible risk is
the risk of loss, which is historically measurable by the mentioned
metrics. To be invested means to assume
risk.
“Risk” is the
cost we pay for seeking reward (return), should capital be injured. Risk is an attempt to quantify a rather
illusory and subjective concept. We can think of risk as the loss of capital. A popular calculation is the percent
drawdown of a portfolio by day, week, month, quarter, or year. Another is the Sortino Standard Deviation,
described below and elsewhere within this site.
A third is explained next.
The label
"Morningstar Risk" in these pages says that risk is incurred if the
portfolio can't beat the return on cash, where T-Bills serve as the proxy for
cash or money markets; it's the average deviation between a position's return
and the corresponding year's T-Bill return for those returns that under
performed T-Bill returns. For example, suppose that over a two-year period
T-Bills return 4% and 5%. Over the same two years, investment A gains 20% and
loses 10%, while investment B shows returns of 10% and 50%. The average risk
for A is 7.5% (the sum of 0 in the 1st year and 15% in the 2nd year divided by
2 years) and the average risk for B is 0% (it outperformed the cash alternative
in each year). Investment A is showing risk because it under performed the
"riskless" cash alternative in one of those years, whereas investment
B did not exhibit this type of risk over those two years. Interestingly, the
traditional quantitative measure of risk (standard
deviation of returns) would show that B is more "risky" than A
(it's more volatile). I'll take upside volatility anytime. It's downside
volatility that I want to avoid. By the way, this measure of risk is
conceptually the same as that used by Morningstar in their calculation of a fund’s risk, which eventually
leads to their method of calculating a fund’s star rating within its category.
Return is
often adjusted to account for risk as well.
In these pages the risk-adjusted
return is simply the annualized return divided by a risk calculation. For example, a 12% annualized return with
Morningstar risk 4% would show a 3% risk-adjusted return.
Two
often-cited metrics that combine return and risk are the Sharpe Ratio and
Sortino Ratio. The Sharpe Ratio is excess return (return less risk-free return given
by T-bill return) divided by standard deviation. So, for an S&P 500 annualized return of
9%, T-Bill of 3%, and standard deviation of returns of 12%, the Sharpe Ratio
would be 0.50 or (9-3)/12. A shortcoming
of the Sharpe Ratio from a timing standpoint is that the standard deviation
includes both upside and downside volatility.
To eliminate the upside component (which is welcome in timing) we would
use the Sortino Standard Deviation, a
standard deviation of losses and thus an alternate metric for risk. The Sortino
Ratio then is calculated as in the Sharpe Ratio, but by substituting the
alternate standard deviation. Thus, in
our preceding example, given a Sortino Standard Deviation of 2%, the Sortino
Ratio would be 3.00 or (9-3)/2.
All three of
these risk-adjusted-return metrics are used in these pages to compare
buy-n-hold, standard timing, and aggressive timing.
A key
objective of this timing system is to generate returns that match or exceed
those of its S&P 500 benchmark index, but with lower risk, that is, lower
downside volatility as calculated, which would also give higher risk-adjusted
returns.
How does the model account for external
events such as political, terrorist, or military crises? Or for the occurrence of rare, unanticipated
events?
Models do not
include these so-called exogenous
(external) events. While the model is
not directly influenced by unique
military, political, terrorist, and natural or man-made disasters, it does have
its finger on the pulse of the market-patient based on its diagnostic readings,
which do react indirectly to current
events through its technical and sentiment indicators. The model does not assess the cause that explains the market’s
behavior; rather it focuses on the market’s symptoms. Using a medical analogy, it analyzes the
patient’s behavior or symptoms based on measurable diagnostics. The model looks for neither a cure nor root
cause; it looks for a course of action (buy or sell) that responds to the symptoms
(its diagnostic indicators). The model thus assesses how the “patient” reacts to
these events, much like a real patient’s vital signs are measurable by
instrumentation. When vital signs
(scores) are strong, the patient is healthy and can better sustain shocks to
the system (harmful external events such as the destructive Japanese tsunami in
2011). When the model is weak, negative
external events can tip it into a sell signal, if not already on a sell signal,
as it was at the beginning of the 2008 financial crisis. Other than short time
periods, it would seem that sentiment
trumps external events.
Nor can
financial models anticipate future Black Swan events, very rare
or extremely improbable, surprising and immensely consequential occurrences
that nevertheless do happen in the historical record. Statistically speaking, these rare
consequences illustrate extreme tail risk, often erroneously based
on the assumption and sketch of a normal probability distribution (bell curve)
with skinny tails describing extremely low probabilities for events very far
away from the center (5 to 10 sigma or more for the statistically savvy out
there), in either direction. These
events, which can lead to either positive or negative consequences, are not
capturable in advance by financial models, although it is possible to model
Black Swans after the fact, in hindsight, once they’re better understood. A
positive Black Swan was the invention of the internet, with huge (mostly)
positive consequences that are ongoing.
Another is electronic miniaturization.
Interestingly, both of these examples are the result of military and
aerospace research.
Historically,
negative events include the Black Monday stock market crash in 1987, the burst
dot-com bubble in 2000, the attack on September 11, 2001, and the financial
crisis in late 2008. Negative future
examples? We could have a series of
cascading and interlinked detrimental
and wholly unexpected global economic events that engulf the world in a
depression that might make the 1930s seem like happy times. Or, in response to
American and Israeli covert or even overt military actions,
Note that
positive Black Swans usually develop over long periods of time; the negative
variety typically happens in compressed time.
Moreover, future Black Swans can be classified as narrated (known unknowns
as their possibilities are discussed in the media) or totally unknown (unknown unknowns or those we haven’t
even thought of). The Black Swan
examples cited here are narrated, which we can better prepare for in our
investment choices. See The Black Swan by Nassim Taleb
for a compelling and entertaining read on the Black Swan Theory.
Unfortunately,
the use of a normal curve in finance, with its known probabilities and
mathematical amenability within models, underestimates the probabilities of
rare events. In other words, most actual
probability curves have fat (heavy) tails that show greater
probabilities for rare events than those estimated by the skinny-tailed normal
distribution. Thus, “rare” events such
as stock market crashes are not quite so rare as believed. For example, the tails of oil crises appear
to be “fatter” than previously believed;
so is the likelihood of a Chinese market crash.
We assume fatter tails in these examples because they are narrated Black
Swans. Moreover, the perfectly informed,
unemotional, and normal-curve-based mathematical world envisioned by Efficient Market and Random Walk Theories (which negate the
possibility of market timing) are clearly inconsistent with market behavior and
tail risk. The newer theory of endogenous (internal) risk first
described in research papers at Stanford University and engagingly explained in
Woody Brock’s book American Gridlock addresses the endogenous factors in
financial systems (and models) that give rise to rare events, including the
uncertainty of which models correctly price assets, the role of leverage, the
use of hedging strategies, and the existence of mistakes. More generally, the book uses rigorous
deductive logic to address economic crises and gridlocked political issues such
as distributive justice, government deficits, and the health-care dilemma.
The
probabilities of future Black Swans are not predictable by definition, but we
can seek to protect against their negative consequences and exploit their
positive effects. A robust market timing
model reduces, but far from eliminates, the likelihood that we would be caught
in a very negative event (by being out of a vulnerable market) and increases
the likelihood that we will be in the market when it’s receptive to very
positive events. The live model, for
example, took some losses following the dot-com fiasco and its 2000-2002
effects on markets, but which were mitigated compared to actual market losses;
ditto the 2008 crisis (see Reality
Check). The real-time model in 2016 got
caught on the wrong side of the Trump-Bump trade following the Presidential
election; the backtested model that’s used live this year rode the wave up.
Extreme
tail events in finance are uncommon, but far from rare… to fear them so that we
remain perpetually uninvested would be unwise… and surely a prescription for
chronic financial underperformance. Think not investing since the Black-Swan
crashes in 1929 and 1987. The backtested
model, by the way, captures the 1987 event, given hindsight and the historical
data to work with. And, keep in mind,
that the model does not predict the
future behavior of the stock market; rather, it seeks to detect whether or not
the just-completed week affirms or rejects a primary trend, based on its particular historical data going back to
1970. Still, a healthy dose of
skepticism is warranted by hedging our bets based on the model, should we have
negative and sudden Black Swans, as these would not be in the model’s DNA: by
not being fully invested during buy signals, by diversifying across asset
classes, and by taking some positions that might benefit from negative
consequences. And even then there are no guarantees of even partial inoculation.
Those who do not remember the past are condemned to repeat
it.
The third-rate mind is only happy when it is thinking with
the majority. The second-rate mind is only happy when it is thinking with the
minority. The first-rate mind is only happy when it is thinking.
I’ve read that it’s best to sell in May
and go away until November. Really?
Not
really. It’s true that historically the
period from May through October has a lower return and is more volatile than
the season from November through April.
But you would be giving up return and capital by only staying in the
market during the favorable season. Take
a look at these results for the time frame 1990-2016, which include reinvested
dividends when invested and T-Bill returns when not. Working with weekly data we would sell at the
end of the last week of April and buy at the end of the last week of October.
The table left tells us that, over the entire
27 years, the seasonal strategy underperforms buy and hold (B&H) by a 0.9%
annualized return (9.4% v 8.4%), which amounts to a deficit of $236,491. So, it’s best to simply buy-n-hold over the
given seasonal strategy. The live
standard model outperforms buy-n-hold by 0.6 percentage point annualized, which
amounts to a greater portfolio ending value by $193,116. Note
that these results are real time (live) for the model, as in the Reality Check
menu link, not backtested.
Looking at
return specifics within each season in the table right, we see that the average
seasonal return during the “Sell Season” May-Oct is 2.8% for B&H (market
return) v 1.4% for Sell May (T-Bill return), a regret average of 1.4% per
season. Again, the model outperforms
both at 3.9% by avoiding some declines during this season, while staying
invested otherwise. During the Nov-Apr
“Buy Season,” B&H is equivalent to Buy Nov, as expected. Here the model underperforms
as it misses some
gains during uptrending periods.
The seasonal
strategy does reduce risk from a drawdown perspective. It shows no losses during the May-Oct
seasonal (since the strategy is not invested), while B&H took a 9.1% average loss and a
worse drawdown of 29.9%. So, the tradeoff in choosing to implement the
seasonal strategy is lower annualized return (9.4% v 8.4%) as a
price for lower drawdown risk (9.1% v 0% average loss May-Oct and 29.9% v 9.3% worse seasonal
drawdown). As expected, the average loss
and worse drawdown is less for the Model than for B&H.
So, we can
say that the seasonal strategy does reduce risk some, but at the expense of a
return deficit. One strategy is to
reduce stock exposure during the May-Oct season, thereby reducing the
likelihood of a major selloff (29.9%), while giving up some gains. Or, work with a good timing model and maybe
have your cake (higher return) and eat it too (lower risk).
What
motivated you to develop the model? Why
is your service free? Do you plan to charge in the future?
New York Times
STOCKS
PLUNGE 508 POINTS, A DROP OF 22.6%; 604 MILLION VOLUME NEARLY DOUBLES RECORD
Bedlam on
It was October 19, 1987.
Black Monday. A Black
Swan for sure. A nearly 23% dizzying plunge, in one day. Think about that: nearly one-quarter of capital
evaporates! One quarter. In one day. Poof. I
panicked along with most everyone. The evaporation of retirement dreams, the
loss of financial freedom? That crash in
1987, those days of terror, made for some soul searching. My 1971 PhD was in
quantitative analysis and finance. The financial buzz since the 1950s included
terms such as Modern Portfolio Theory
(MPT), Capital Asset Pricing Model (CAPM), the efficient frontier, portfolio
insurance, markets as random walks,
the folly of timing the market. “Folly”
was a kind word. It was more like “stupidity,” and worse. Subsequently (in
1990) the Nobel Prize in Economics was awarded to three Americans for their
pioneering work in the theory of financial economics, including Harry Markowitz
for his development of MPT and
William F. Sharpe, of CAPM and Sharpe ratio fame.
Never mind. Within a week of the crash I started thinking
about and subsequently developing a model rooted in financial hypotheses and
constructed with the building blocks of mathematical statistics and
forecasting. Invention is born out of necessity, alarm in this case. After more
trial and error than I would want to recount, or even remember, I had the first
really good working model by mid-1989, with full-year implementation in 1990.
Each year since then the model has been tested, revised, and improved. A new
model each year. (All models have issued sell signals one week before the ‘87 crash.) The current model is
optimized (or tries) to max the value of the standard portfolio, based on
historical data since 1970; the actual (live) performance of each model since
1990 (for its respective year) is seen in Reality Check. See this for a
veteran’s account of Black Monday. And
check out this video.
I would not have retired in 2007 if not for
the model’s guidance since 1990.
There is no registration, newsletter, or
email updates for this free service.
What you see at this website is what you get: Complete current and year-by-year tested
(from 1970) and actual (from 1990) results with no spin and no bias regarding
selected performance dates. A for-fee
service is not available.
It started as a free service in 1995 more
because of time constraints and other commitments than anything. My university
faculty job was full time, the market timing modeling work was part of my
research function, and the free site was part of my service function. It did
serve to establish a public record, it's been consistent with the original
(free) spirit of the Internet among researchers, and it's been fun. I plan to
continue this service as a free giveback for the foreseeable future.
There is only one kind of shock worse than the totally
unexpected: the expected for which one has refused to prepare.
Did you know that... ?
Since 1926, a
money market investment based on T-Bills would have earned about 3.3% per year,
barely edging out inflation at 2.9%. Long-term cash investments are like bad
dreams where you run in place. They're the slow boat to poverty. Long
government bonds have done ok, at about 5.7% annually. But the S&P 500,
with reinvested dividends, clocked in at 10.1%, a wealth building real
(inflation-aware) difference. In plain dollars, Rip-van-Winkling one thousand
bucks into T-bills in 1926 would give about $21 thousand for today's wakeup
present. Pretty shabby, especially considering that it takes about $14 thousand
today to buy the equivalent of $1 thousand then. Bonds did better, upping the
wealth to $158 thousand. But stocks
looked good in the Roaring 20s. If Rip had placed the money in stocks, the
wakeup fund would have grown to an eye-popping $7.2 million. Rip's dreams came
to pass... but do we have that much time? And do we remain oblivious to market
volatility... as Rip surely did and buy and holders must?
Compound interest is man’s greatest invention.
Albert Einstein (allegedly)
In the long run we are
all dead.
Last
revised 21 Jan 2018
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Copyright © 2018 Richard Mojena. All rights reserved. All materials contained
on this site are protected by
Disclaimer
Specific and personalized investment advice is not intended by this
communication. Its contents are for the public record as a free public service.
Information is based on the analysis of past data and assessments by the
models. Future performance may not reflect past performance. Profitable trades
are not guaranteed. No system or methodology ensures stock market profits. No
guarantee is made regarding the reliability or accuracy of data. In other
words, use this stuff at your own risk!