actions of a large number of market participants, one can always observe the uncanny accuracy with which price tests and reacts at a psychologically significant barriers or prices. It is hard to believe that price action is the result of random acts of buying and selling by market participants where the participants are totally unencumbered by cost, biases, psychology, or emotion.
• The strong form of the Efficient Market Hypothesis (EMH) argues that since the markets discount all information, price would have already adjusted to the new information and any attempt to profit from such information would be futile. This would render the technical analysis of price action pointless, with the only form of market participation being passive investment. But such efficiency would require that all market participants react instantaneously to all new information in a rational manner. This in itself presents an insurmountable challenge to EMH. The truth is that no system comprising disparate parts in physical reality reacts instantaneously with perfect coordination. Hence it is fairly safe to assume that although absolute market efficiency is not attainable, the market does continually adjust to new information, but at a much lower and less-efficient rate of data discounting. Therefore, technical analysis remains a valid form of market investigation until the markets attain a state of absolute and perfect efficiency.
• Another argument against technical analysis is the idea of the Self-Fulfilling Prophecy (SFP). Proponents of the concept contend that prices react to technical signals not because the signals themselves are important or significant, but rather because of the concerted effort of market participants acting on those signals that make it work. This may in fact be advantageous to the market participants. The trick is in knowing which technical signals would be supported by a large concerted action. The logical answer would be to select only the most significantly clear and obvious technical signals and triggers. Of course, one can further argue that such signals, if they appear to be reliable indicators of support and resistance, would begin to attract an increasing number of traders as time passes. This would eventually lead to traders vying with each other for the best and most cost-effective fills. What seems initially like the concerted action of all market participants now turns into competition with each other. Getting late fills would be costly as well as reduce or wipe out any potential for profit. This naturally results in traders attempting to preempt each other for the best fills. Traders start vying for progressively earlier entries as price approaches the targeted entry levels, leading finally to entries that are too distant from the original entry levels, increasing risk and reducing any potential profits. This disruptive feedback cycle eventually erodes the reliability of the signals, as price fails to react at the expected technical levels. Price finally begins to react reliably again at the expected technical levels as traders stop preempting each other and abandon or disregard the strategy that produced the signals. The process repeats. Therefore, SFP may result in technical signals evolving in a kind of six-stage duty cycle, where the effects of SFP may be advantageous and desirable to traders in the early stages but eventually result in forcing traders into untenable positions. See Figure 1.8.
Figure 1.8 The Idealized Six-Stage Self-Fulfilling Prophecy Cycle.
1.5 SUBJECTIVITY IN TECHNICAL ANALYSIS
As with most forms of analysis, technical analysis has both objective and subjective aspects associated with its application. It is objective insofar as the charts represent a historical record of price and market action. But it is subjective when the technical analyst attempts to analyze the data.
Analyzing price and market action consists of three main activities, namely:
1. Identifying price and indicator patterns
2. Interpreting the data
3. Inferring potential future price behavior
Analyzing price and market action is ultimately subjective because all analysis is interpreted through various behavioral traits, filters, and biases unique to each analyst or observer. Behavioral traits include both the psychological and emotional elements. As a consequence, each analyst will possess a slightly different perception of the market and its possible future behavior.
Subjectivity in the Choice of Analysis and Technical Studies
The sheer number of ways to analyze an individual chart contributes to the overall level of subjectivity associated with each forecast. The problem is twofold:
• What is the most appropriate form of technical analysis that should be applied to a particular chart?
• What is the most appropriate choice of indicators to apply to a particular chart?
These are the usual questions that plague novices. The following charts depict the various popular forms of analysis that can be applied to a basic chart of price action. The following examples are by no means exhaustive. Figure 1.9 starts off with a plain chart devoid of any form of analysis.
Figure 1.9 A Simple Price Chart.
Source: MetaTrader 4
The next chart, Figure 1.10, shows the application of basic trendline analysis on the same chart, tracking the flow of price action in the market.
Figure 1.10 Trendline Analysis on the Same Chart.
Source: MetaTrader 4
In Figure 1.11, moving average analysis is now employed to track the same flow of price action and to provide potential points of entry as the market rises and falls.
Figure 1.11 Moving Average Analysis on the Same Chart.
Source: MetaTrader 4
Figure 1.12 depicts the application of chart pattern analysis to track and forecast the shorter-term bullish and bearish movements in price.
Figure 1.12 Chart Pattern Analysis on the Same Chart.
Source: MetaTrader 4
Figure 1.13 is an example of applying two forms of technical analysis, that is, linear regression analysis and divergence analysis to track and forecast potential market tops and bottoms. Notice that the market top coincided perfectly with the upper band of the linear regression line, with an early bearish signal seen in the form of standard bearish divergence on the commodity channel index (CCI) indicator.
Figure 1.13 Linear Regression and Divergence Analysis on the Same Chart.
Source: MetaTrader 4
Figure 1.14 is an example of applying a couple of additional forms of analysis to the basic linear regression band. In this chart, price action analysis is used in conjunction with volume analysis to forecast a potential top in the market, evidenced by the preceding parabolic move in price that is coupled by a blow-off.
Figure 1.14 Linear Regression and Volume Analysis on the Same Chart.
Source: MetaTrader 4
In Figure 1.15, volatility band, volume, and overextension analysis are all employed to seek out potential reversals in the market.