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[Warsager, 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.
see also this article.
and check also this system.

[Niederhoffer] 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 mentioned tests.

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.

Has 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 trendfollowing returns.

Does 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 criticism here;
and also pay attention to their data-mining (timeframe: post 83 bull).

A 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".
Challenges 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.
Understanding Spurious Regressions in Econometrics, Phillips, 1986
"This paper provides an analytical study of spurious regressions involving the levels of economic time series".
Trends versus Random Walks in Time Series Analysis, Durlauf and Phillips, 1988
This paper studies the effects of spurious detrending in regression.
The Relevance of Trends for Predictions of Stock Returns, ellstrom and Holmstrom, 1998
"A statistical investigation of the relevance of a trend concept for prediction of future returns is presented for individual stocks".

"Predictable Patterns 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".
Article A
Article B

A 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 analysis".
Price Movements in Speculative Markets: Trends or Random Walks, Alexander
Spurious Regressions in Financial Economics?, Ferson, Sarkissian and Simin, 1999
"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".
Technical Analysis in the Foreign Exchange Market: A Layman’s Guide, Neely, 1997
Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of "Technical Analysis", Neftci, 1991