Principles
A fundamental principle of technical analysis is that a market's price reflects all relevant information, so their analysis looks at the history of a security's trading pattern rather than external drivers such as economic, fundamental and news events. Therefore, price action tends to repeat itself due to investors collectively tending toward patterned behavior – hence technical analysis focuses on identifiable trends and conditions.
Market action discounts everything
Based on the premise that all relevant information is already reflected by prices, technical analysts believe it is important to understand what investors think of that information, known and perceived.
Prices move in trends
Back in the early 1970s, I watched "Wall Street Week" on PBS. Later in the 1980s, I met Bob Nurock, a regular Technical Analist on the show. That meeting made me so happy for years.
Technical analysts believe that prices trend directionally, i.e., up, down, or sideways (flat) or some combination. The basic definition of a price trend was originally put forward by Dow theory.
An example of a security that had an apparent trend is AOL from November 2001 through August 2002. A technical analyst or trend follower recognizing this trend would look for opportunities to sell this security. AOL consistently moves downward in price. Each time the stock rose, sellers would enter the market and sell the stock; hence the "zig-zag" movement in the price. The series of "lower highs" and "lower lows" is a tell tale sign of a stock in a down trend. In other words, each time the stock moved lower, it fell below its previous relative low price. Each time the stock moved higher, it could not reach the level of its previous relative high price.
Note that the sequence of lower lows and lower highs did not begin until August. Then AOL makes a low price that does not pierce the relative low set earlier in the month. Later in the same month, the stock makes a relative high equal to the most recent relative high. In this a technician sees strong indications that the down trend is at least pausing and possibly ending, and would likely stop actively selling the stock at that point.
History tends to repeat itself
Technical analysts believe that investors collectively repeat the behavior of the investors that preceded them. To a technician, the emotions in the market may be irrational, but they exist. Because investor behavior repeats itself so often, technicians believe that recognizable (and predictable) price patterns will develop on a chart Recognition of these patterns can allow the technician to select trades that have a higher probability of success.
Technical analysis is not limited to charting, but it always considers price trends. For example, many technicians monitor surveys of investor sentiment. These surveys gauge the attitude of market participants, specifically whether they are bearish or bullish. Technicians use these surveys to help determine whether a trend will continue or if a reversal could develop; they are most likely to anticipate a change when the surveys report extreme investor sentiment. Surveys that show overwhelming bullishness, for example, are evidence that an uptrend may reverse; the premise being that if most investors are bullish they have already bought the market (anticipating higher prices). And because most investors are bullish and invested, one assumes that few buyers remain. This leaves more potential sellers than buyers, despite the bullish sentiment. This suggests that prices will trend down, and is an example of contrarian trading.
Recently, Kim Man Lui, Lun Hu, and Keith C.C. Chan have suggested that there is statistical evidence of association relationships between some of the index composite stocks whereas there is no evidence for such a relationship between some index composite others. They show that the price behavior of these Hang Seng index composite stocks is easier to understand than that of the index.
Who Represents the Industry?
The industry is globally represented by the International Federation of Technical Analysts (IFTA), which is a federation of regional and national organizations. In the United States, the industry is represented by both the Market Technicians Association (MTA) and the American Association of Professional Technical Analysts(AAPTA). The United States is also represented by the Technical Security Analysts Association of San Francisco (TSAASF). In the United Kingdom, the industry is represented by the Society of Technical Analysts (STA). In Canada the industry is represented by the Canadian Society of Technical Analysts.
Professional technical analysis societies have worked on creating a body of knowledge that describes the field of Technical Analysis. A body of knowledge is central to the field as a way of defining how and why technical analysis may work. It can then be used by academia, as well as regulatory bodies, in developing proper research and standards for the field. The Market Technicians Association (MTA) has published a body of knowledge, which is the structure for the MTA'sChartered Market Technician (CMT) exam.
What About Systematic Trading
Neural networks
Since the early 1990s when the first practically usable types emerged, artificial neural networks (ANNs) have rapidly grown in popularity. They are artificial intelligence adaptive software systems that have been inspired by how biological neural networks work. They are used because they can learn to detect complex patterns in data. In mathematical terms, they are universal function approximators, meaning that given the right data and configured correctly, they can capture and model any input-output relationships. This not only removes the need for human interpretation of charts or the series of rules for generating entry/exit signals, but also provides a bridge to fundamental analysis, as the variables used in fundamental analysis can be used as input.
As ANNs are essentially non-linear statistical models, their accuracy and prediction capabilities can be both mathematically and empirically tested. In various studies, authors have claimed that neural networks used for generating trading signals given various technical and fundamental inputs have significantly outperformed buy-hold strategies as well as traditional linear technical analysis methods when combined with rule-based expert systems.
While the advanced mathematical nature of such adaptive systems has kept neural networks for financial analysis mostly within academic research circles, in recent years more user friendly neural network software has made the technology more accessible to traders. However, large-scale application is problematic because of the problem of matching the correct neural topology to the market being studied.
Backtesting
Systematic trading is most often employed after testing an investment strategy on historic data. This is known as backtesting. Backtesting is most often performed for technical indicators, but can be applied to most investment strategies (e.g. fundamental analysis). While traditional backtesting was done by hand, this was usually only performed on human-selected stocks, and was thus prone to prior knowledge in stock selection. With the advent of computers, backtesting can be performed on entire exchanges over decades of historic data in very short amounts of time.
The use of computers does have its drawbacks, being limited to algorithms that a computer can perform. Several trading strategies rely on human interpretation, and are unsuitable for computer processing.[46] Only technical indicators which are entirely algorithmic can be programmed for computerised automated backtesting.
No comments:
Post a Comment