Technical Analysis 4
Industry[edit]
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.[34] In Australia, the industry is represented by the Australian Technical Analysts Association (ATAA),[35] (which is affiliated to IFTA) and the Australian Professional Technical Analysts (APTA) Inc.[36]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.[37] The Market Technicians Association (MTA) has published a body of knowledge, which is the structure for the MTA's Chartered Market Technician (CMT) exam.[38]
Systematic trading[edit]
Neural networks[edit]
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,[39][40] 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.[41][42][43]
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[edit]
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,[44] and are unsuitable for computer processing.[45] Only technical indicators which are entirely algorithmic can be programmed for computerised automated backtesting.
Combination with other market forecast methods[edit]
John Murphy states that the principal sources of information available to technicians are price, volume and open interest.[10] Other data, such as indicators and sentiment analysis, are considered secondary.However, many technical analysts reach outside pure technical analysis, combining other market forecast methods with their technical work. One advocate for this approach is John Bollinger, who coined the term rational analysis in the middle 1980s for the intersection of technical analysis and fundamental analysis.[46] Another such approach, fusion analysis,[47] overlays fundamental analysis with technical, in an attempt to improve portfolio manager performance.
Technical analysis is also often combined with quantitative analysis and economics. For example, neural networks may be used to help identify intermarket relationships.[48] A few market forecasters combine financial astrology with technical analysis. Chris Carolan's article "Autumn Panics and Calendar Phenomenon", which won the Market Technicians Association Dow Award for best technical analysis paper in 1998, demonstrates how technical analysis and lunar cycles can be combined.[49] Calendar phenomena, such as the January effect in the stock market, are generally believed to be caused by tax and accounting related transactions, and are not related to the subject of financial astrology.
Investor and newsletter polls, and magazine cover sentiment indicators, are also used by technical analysts.[50]
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