Duncan and Wilkens] "Major trends are generated by
long term secular shifts in capital flows".
[Shaw] We have learned
over time that in a stock or sector's bull market, prices look
expensive - and then get more expensive - as time allows the fundamentals
to catch up. The stock often has an enormous initial run-up that
can make some investors nervous, not understanding the longer-term
potential. But over the course of a multiyear bull market, the
tenacious investor is rewarded. Yes, prices pull back.
As long as they hold at higher lows, that is the process of the
aggressive demand that constitutes a bull market.
It should be remembered
that the mere violation of a trendline is not the sole reason
a technician becomes concerned. It's the implied change in the
supply/demand trend behind the shift that is meaningful. An uptrend,
by definition, is a series of higher lows followed by higher highs,
in that sequence. A downtrend is, of course, the opposite progression.
Sticking with the uptrend,
let's more fully define the higher low, higher high progression
in terms of supply/demand. Who creates the higher low? The higher
high? It is simple logic that a buyer, or demand, is the force
creating the higher lows in an uptrend. And the persistence of
demand, over time, is what creates the higher low pattern. On
the supply side, the seller(s) is actually profiling bullish
bent. How? Because he is selling at progressively higher levels.
So, an uptrend is not just a series of higher lows and higher
highs but actually the portrayal, over time, of bullish signs
of demand and supply.
Once these forces begin
to change their style (for whatever reason), the technician will
be alerted by trend violations. Uptrend violations will most often
be spotted by the demand factor first giving a clue of change.
The higher lows will not follow through. Then the technician will
look for signs of a change in the supply side, which will manifest
itself by a change in the progression of the higher highs. Lower
highs, followed by lower lows, will be the complete evidence of
a trend change from positive to negative.
and check also this system.
I am often asked why I don't believe in trends despite the great
profits of some selected trend followers. The main reason is that
standard measures in statistics like the serial correlation coefficient
or runs or Goodman tests for m dependent time series, are designed
to test trends. I have not found many market series that show consistent
departures from randomness on such tests. Nor more importantly,
have I ever found a series that looks like it has a trend, whether
it be a moving average or lagged momentum type, that doesn't show
some serious evidence for non-randomness as measured by the above
More terribly, the human mind is very good and capable of finding
order in chaos and randomness. And what looks like
order and trend is often completely consistent with the above.
Two main points that lead to these optical illusions are the fact
that the variance of the sum of n random components is n times
the variance of a given random component. So as you get further
along in time from the starting point of a random series, the
movements away from the beginning seem to be very big and trendy,
albeit strictly consistent with randomness. The second main area
of deriving order from chance is the human minds ability to make
multiple comparisons. When it looks at a series, it is very good
at finding a million stopping and starting points which taken
in isolation do indeed show local non-random trends. However,
with all these stopping and starting points, it's bound to happen
that the straight line between the two points will seem to "explain".
I am well aware that some markets do show trends (in retrospect),
and allow back tested systems to work. But merely because it worked
back tested, why should the markets be so kind as to allow those
who can draw a straight line between two points to make money
in the future.
Well then, how do I explain the great results of the selected
trend followers compared to my own? Those results that the great
promoters of systems, seminars and books hold up to my discredit
and shame. Well, more power to them. I guess I will always be
scratching the back of such well to dos.
Trend-Following Changed?, Welton, 2001
Trend-following is the directional trading through the buying of
price strength and the selling of price weakness (Nearly all the
differences of consequence among trend-following methods are market
selection (both inclusion and exclusion from the portfolio) and
the degree of leverage employed and not in the general entry and
exit timing methods themselves). Collectively such positions will
have average holding periods from two weeks to one year. Investors
and their representatives are legitimately concerned, particularly
after periods of difficult performance, to explain to themselves
if the performance characteristics from trend-following remain true
to their expectations of the style. Dissatisfaction inspires the
curiosity to ask the question. Patience and dispassionate analysis
is required to understand the answer. Have General Trend-Following
Methods Changed? No, there is no evidence as such. Have the Characteristics
of Trends Changed? Yes, maybe. In recent years, the overall magnitudes
of trends have been smaller. This reduces the theoretical edge of
a trend-following model significantly. Another observation receiving
attention in recent years is the worsening of counter-trend asymmetry.
Counter-trend asymmetry is the long observed behavior of markets
to retrace their primary trend direction faster than the rate of
movement during the trend. This is simply the manifestation that
position liquidation occurs more rapidly than accumulation. The
old stock market adage that markets fall twice as fast as they rise
is an example of this behavior. By some measures, the speed of these
retracements has increased on average during the last few years,
sometimes reaching four to five times the rate of price movement
with the trend. The last area of observed change is within the market
representations themselves. Some market sectors have been consistently
difficult for trend-following over the past few years, some even
for a decade or more, including many non-energy commodities, sub
sectors of currencies, and more recently equity indices globally.
What Are the Implications for Trend-Following Return Characteristics?
Many. While conditions described above persist (trends of smaller
magnitude, faster countertrend price movements, difficult market
groups) risk-adjusted returns for trend-following will be lower
than they would otherwise. Experience to not chase recent performance,
to not buy highs and sell lows, to allocate broadly, and to rebalance
on schedule devoid of emotion are successful attributes to capturing
Trend Following Work on Stocks?, Wilcox and Crittenden, 2005
We decided to put a long only trend following strategy to the
test by running it against a comprehensive database of U.S. stocks
that have been adjusted for corporate actions. Delisted companies
were included to account for survivorship bias. Realistic transaction
cost estimates (slippage & commission) were applied. Liquidity
filters were used to limit hypothetical trading to only stocks
that would have been liquid enough to trade, at the time of the
trade. Coverage included 24,000+ securities spanning 22 years.
The empirical results strongly suggest that trend following on
stocks does offer a positive mathematical expectancy, an essential
building block of an effective investing or trading system.
However, read some
and also pay attention to their data-mining (timeframe: post 83
Non-Random Walk down Wall Street, Lo & MacKinlay
"Random Walk Hypothesis and its close relative, the Efficient
Markets Hypothesis, have become icons of modern financial economics
that continue to fire the imagination of academics and investment
professionals alike. The papers collected in this volume comprise
our own foray into this rich literature, spanning a decade of research
that we initiated in 1988 with our rejection of the Random Walk
Hypothesis for US stock market prices, and then following a course
that seemed, at times, to be self-propelled, the seeds of our next
study planted by the results of the previous one".
of Trending Time Series Econometrics, Phillips, 2004
A distinguishing characteristic of most economic time series is
trending behavior. In spite of many decades of research in fields
like monetary theory and economic growth, economics provides little
guidance about the source of such trends and even less guidance
concerning suitable formulations for practical work. In practice,
therefore, while many economists see trends in the data, the econometric
modeling of such trends is a much more difficult task. In short,
one of the laws of modern time series econometrics is that 'no
one understands trends, but everyone sees them in the data'.
While we may not understand the trending mechanism itself, we still
have the opportunity to coordinatize a trend in terms of simple
deterministic functions, just as we can coordinatize a function
in a space of functions using a simple set of basis functions.
in Stock Returns", Hellstrom and Holmstrom, 1998
"The results show trend behavior and autocorrelation values
that are stable even when the entire time interval is broken down
to yearly intervals".
Random Walk through the Stock Market, Hellstrom, 1998
"This thesis deals with the well-known problem of prediction
of stock prices. The discussion is almost entirely confined to technical
Movements in Speculative Markets: Trends or Random Walks, Alexander
Regressions in Financial Economics?, Ferson, Sarkissian and
"We study biases associated with regression models in which
persistent lagged variables predict stock returns, either linearly
or in interaction with contemporaneous values of a market index
return. We focus on the issue of spurious regression, related to
the classic studies of Yule (1926) and Granger and Newbold (1974).
We find that spurious regression is a concern in regressions of
stock returns on persistent lagged instruments, especially when
the predictable component of returns is large. In regressions where
the lagged instruments interact with a market index return, the
spurious regression problem is not as severe. Without persistent
time-variation in the expected market return and beta, spurious
regression bias is not an important issue. However, when a common
persistent factor drives expected market returns and betas, spurious
regression becomes a concern. Large sample sizes are no defense
against the spurious regression bias".
Analysis in the Foreign Exchange Market: A Layman’s Guide,
Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction
Theory: A Study of "Technical Analysis", Neftci, 1991