Abstract
Soft computing represents that area of computing adapted from the physical sciences. Artificial intelligence techniques within this realm attempt to solve problems by applying physical laws and processes. This style of computing is particularly tolerant of imprecision and uncertainty, making the approach attractive to those researching within “noisy” realms, where the signal-to-noise ratio is quite low. Soft computing is normally accepted to include the three key areas of fuzzy logic, artificial neural networks, and probabilistic reasoning (which include genetic algorithms, chaos theory, etc.). The arena of investment trading is one such field where there is an abundance of noisy data. It is in this area that traditional computing typically gives way to soft computing as the rigid conditions applied by traditional computing cannot be met. This is particularly evident where the same sets of input conditions may appear to invoke different outcomes, or there is an abundance of missing or poor quality data. Artificial neural networks (henceforth ANNs) are a particularly promising branch on the tree of soft computing, as they possess the ability to determine non-linear relationships, and are particularly adept at dealing with noisy datasets. From an investment point of view, ANNs are particularly attractive as they offer the possibility of achieving higher investment returns for two distinct reasons. Firstly, with the advent of cheaper computing power, many mathematical techniques have come to be in common use, effectively minimizing any advantage they had introduced (see Samuel & Malakkal, 1990). Secondly, in order to attempt to address the first issue, many techniques have become more complex. There is a real risk that the signal-to-noise ratio associated with such techniques may be becoming lower, particularly in the area of pattern recognition, as discussed by Blakey (2002). Investment and financial trading is normally divided into two major disciplines: fundamental analysis and technical analysis. Articles concerned with applying ANNs to these two disciplines are reviewed.
TopBackground
There are a number of approaches within the literatures, which deal with applying ANN techniques to investment and trading. Although there appears to be no formal segmentation of these different approaches, this review classifies the literature into the topics proposed by Tan (2001), and augments these classifications with one more category, namely, hybrid. These categories of ANN, then, are:
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Time series: Forecasting future data points using historical data sets. Research reviewed in this area generally attempts to predict the future values of some time series. Possible time series include Base time series data (e.g., closing prices), or time series derived from base data, (e.g., indicators--frequently used in technical analysis).
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Pattern recognition and classification: Attempts to classify observations into categories, generally by learning patterns in the data. Research reviewed in this area involved the detection of patterns, and segregation of base data into “winner” and “loser” categories as well as in financial distress and bankruptcy prediction.
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Optimization: Involves solving problems where patterns in the data are not known, often non-polynomial (NP)-complete problems. Research reviewed in this area covered the optimal selection of parameters, and determining the optimal point at which to enter transactions.
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Hybrid: This category was used to distinguish research, which attempted to exploit the synergy effect by combining more than one of the previous styles.
There appears to be a wide acceptance of the benefit of the synergy effect, whereby the whole is seen as being greater than the sum of the individual parts.
Further, the bias in this style of research toward technical analysis techniques is also evident from the table, with one-third of the research pursuing the area of pattern recognition and classification. Technical analysis particularly lends itself to this style of research, as a large focus of technical analysis concerns the detection of patterns in data, and the examination of the behavior of market participants when these patterns are manifest.
Key Terms in this Chapter
Technical Indicators: Technical indicators are produced as results of various computations on technical data. They are primarily designed to confirm price action.