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 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, 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,
yet the timing model's 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market, particularly if funds need to be withdrawn shortly thereafter.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%. How long will it take
this Index to recover? It’s now
three years and counting.
The buy-and-hold mantra
popularized during the remarkable super-bull market 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 well being (see Did you know that...? below).
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 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. Imagine living as a buy-and-hold investor through
that grueling 16-year odyssey! How many did? We are currently living through a similar
performance with Dow 10,000.
The behavior 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 and fear. Markets are not random walks, nor are they efficient.
The timing
model's objective is to detect changes in these cycles 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, the points at which the market changes
direction. To be successful, a model does not have to be right twice, at
tops and bottoms, as critics maintain; it "just" needs to sell higher
than its buy points and buy lower than its sell points.
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 1970-2011
time period, investment performance drops by 49% 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 54%. 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 6%. 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.
|
Per Year Returns Basis S&P 500, with Reinvested Dividends |
|||
|
Period |
Buy and Hold |
Standard Timing Model |
Aggressive Timing Model |
|
1973-1982 |
6.6% |
16.1% |
18.4% |
|
1978-1987 |
15.2% |
22.1% |
25.7% |
|
1988-1997 |
18.1% |
19.6% |
25.3% |
|
2000-2009 |
-1.0% |
7.9% |
13.9% |
As the accompanying table
illustrates, both timing and the timing period are
everything. The standard 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 models
trounced buy and hold during 1973-82, a span that housed the devastating
1973-74 bear that sliced portfolios nearly in half. The 2000s decade
included three severe bear markets, once again giving the timing model a
capital-preservation advantage. Timing
models feed on cycles for sustenance.
The timing model has been live since the middle of 1989, out-performing buy and
holders through last year, with much less risk. The 1990s ushered in an
era of historically low downside volatility, except for the brief near-bear
markets in 1990 and 1998. It's nearly impossible for a timing model, indeed for
anyone as the records show, to beat buy and hold in markets that exhibit very
little cyclical behavior and no punishing downturns. The model's
advantage clearly became apparent during the 2000-2002 and 2007-2008 bear
markets. And, to be honest, the live implementation of a theoretical
system rarely performs better than its tested (theoretical) version. Since 1990, the annually-revised live models under-performed the current theoretical model by about 3 percentage
points per year (10% vs 13%), 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.
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. 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% spike within
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).
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
10% 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 56%. The secular trend from 2000
through 2011 looks to have somewhat flattened, but still downtrending. This secular trend could easily continue for
another decade as the economy (individuals, private and public sectors) unwinds
debt from the debt super-cycle of the past 20 years or so. 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
bull started with a P/E of 24 and ended with 7.
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 secular bear has more to go.
Having said this, the model does not concern itself with
bull or bear markets per se; rather it strives to detect the beginning of
primary up or down trends of 10% or more over at least eight weeks. These primary trends do operate within the context of cyclical bull and bear markets. Primary uptrends are more likely within
cyclical bull markets, as primary downtrends are more likely within cyclical
bear markets. 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
downward secular bear markets, 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 is, after all, the main benefit of a good timing system.
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. Your stock allocation should
be a percentage that you can sleep with. For example, 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. Alternatively, you could phase in a
switch, as in moving into cash in 25% chunks over four weeks. Also see
about diversification, next FAQ.
You favor domestic stock ETFs and mutual funds. 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 more recent innovation is exchange-traded
notes (ETNs). These also track indexes (and many other
financial investments) and behave like ETFs, but are in reality debt securities
issued by banks. As such, their net
asset values include the risk of issuer credit downgrades and possible
defaults. They do have certain tax
advantages in real estate and commodity spaces, which are beyond the scope of
this writing.
NOTE: Important advantages of ETFs and ETNs 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 returns over long time periods, making up thirty to fifty percent of
total returns for the S&P 500, depending on time period.
Keep in mind 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. I primarily invest in ETFs consistent with
the model. But I also diversify by
including ETFs in bonds, gold, and certain commodities such as water,
agriculture, and energy. 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. If you wish to
allocate 40% to assets other than domestic stocks, then move up to 60% of your
portfolio between stocks and money market funds at switch signals.
To follow the standard
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. Another low-cost alternative 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. 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.
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 2011, the S&P 500
includes asset classes like real estate (2%) and commodities such as oil, gas,
and consumable fuels (10%).
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.
I would like to be more aggressive with some investments at buy and sell signals. What strategies are available?
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 Bear ProFund (BRPIX),
At a buy signal,
ETFs that mimic our main indices include the previously mentioned DIA, SPY, and
QQQQ. Aggressive portfolios could invest in so-called high-beta or ultra funds. For example, Potomac
U.S. Plus (PSPLX) and Rydex Nova (RYNVX) have a beta of 1.5, meaning that their
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! More aggressive still? UltraBull ProFund (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.
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 signal. The likelihood of loss
when trading options is high, even with reasonably good timing. Commitments to
these instruments should not exceed 5% of a portfolio.
The accompanying table
summarizes the simplest implementations of the model's standard and aggressive
portfolios.
|
Portfolio Implementations |
|||
|
Signal |
Portfolio |
Position |
Sample Investments |
|
Buy |
Standard |
100% long
S&P 500 |
Any S&P 500 index fund, such as Fidelity's Spartan Market Index (FSMKX) or Vanguard Index Trust 500 (VFINX). SPY shares ( "spiders" ). * Note that dividends are not reinvested for this ETF. |
|
Aggressive |
150% long
S&P 500 (1.5x) 200% long S&P 500 (2x) |
Potomac Rydex Nova Fund (RYNVX). * SSO or RSU shares. |
|
|
Sell |
Standard |
100% T-Bills |
Any money market mutual fund that emphasizes Treasuries. |
|
Aggressive |
100%
short S&P 500 200% short S&P 500 (-2x) |
Bear ProFund (BRPIX). * Rydex Ursa Fund (RYURX). * SH shares. SDS or RSW shares. |
|
|
*Spiders are traded on the American Stock Exchange at a price that's approximately one-tenth that of the S&P 500 Index. Unlike most mutual funds, these are traded intraday. Rydex Funds are available either directly from Rydex (www.rydexfunds.com) or through discount brokers such as Fidelity, Schwab, and Jack White. Ditto Potomac Funds (www.potomacfunds.com) and ProFunds (www.profunds.com). |
|||
An ultra aggressive
strategy is to be 200% long during a buy signal (instead of 150% long) and 200%
short during a sell signal (instead of 100% short). For example, buying RSU (2x) during buy
signals and RSW (-2x) during sell signals, the model’s aggressive
strategy starting with $10,000 in 1970 would (in theory) end with $626 million
in 2010 (versus $37 million for our stated 1.5x/-1x aggressive strategy and
about $5 million for the standard strategy).
This would pump the annualized return to 30% (versus 22% and 16%). In practice, expense ratios, trading fees,
and especially holding for long time periods in volatile environments would put
a very serious dent in these returns. (See CAUTION.)
CAUTION: An important disadvantage of aggressive (leveraged, ultra) long and short mutual funds and ETFs is the negative performance effect of volatility over periods greater than a day, as these funds are designed to replicate the multiplied return for the underlying index on a daily basis. Unlike 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 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, not the approximately 35% that would be expected by a simple 50% increase over the index. The greater the multiplier (most ultra ETFs use 2x or 3x), the longer the time period, and the greater the up & down daily volatility during the signal’s period, the greater the potential for loss, a result that can be shown mathematically based on compounding formulas. Again: These instruments and strategies are very risky and should constitute a small percentage of a portfolio, if at all.
I'm not sure how to interpret the terms "Annualized", "Years to Double", and "Risk" in the performance comparisons?
The label
"Annualized" or "Per Yr Return" is the annualized return from
the beginning to the ending year. It's 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 used
40% one year and -10% the other year. Note that the annualized rate is not a
simple average!
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. Uhmm..."
The label "Risk"
is an attempt to quantify a rather illusory and subjective concept. By my
definition, risk is incurred if I 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.
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.
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 statistical 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,
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 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 tail of a current oil crisis
now appears to be “fatter” than
previously believed; so is the likelihood of a Greek default and eventual
exit from the eurozone. 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 events. 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). Financial extreme tail
events are rare indeed… 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 theoretical model, by
the way, now captures a 1987-type 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 may 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.
Why is your service free? Do you plan to charge in the future?
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 retired in June 2007, cruised and lived on my boat until recently, and
plan to continue this service as a free giveback for the foreseeable future.
Did you know that... ?
Since 1926, a money market
investment based on T-Bills would have earned about 3.6% per year, barely
edging out inflation at 3.0%. Long-term cash investments are like bad dreams
where you run in place. They're the slow boat to poverty. Long government bonds
haven't been so hot either, at about 5.8% annually. But the S&P 500, with
reinvested dividends, clocked in at 9.7%, a wealth building real
(inflation-aware) difference. In plain dollars, Rip-van-Winkling one thousand
bucks into T-bills in 1926 would give about $20 thousand for today's wakeup
present. Pretty shabby, especially considering that it takes about $13,000
today to buy the equivalent of $1000 then. 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 $3.0 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?
Last revised 03/7/2012
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Copyright © 2012 Richard Mojena. All rights reserved. All materials contained
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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!