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Intelligent Is — Artificial Neural Networks In Financial Trading, Bruce Vanstone, Clarence Tan
Intelligent Is — Artificial Neural Networks In Financial Trading, Bruce Vanstone, Clarence Tan
Bruce Vanstone
From the Introduction:
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 includes Genetic Algorithms, Chaos Theory, etc).
A Survey Of The Application Of Soft Computing To Investment And Financial Trading, Bruce Vanstone, Clarence Tan
A Survey Of The Application Of Soft Computing To Investment And Financial Trading, Bruce Vanstone, Clarence Tan
Bruce Vanstone
This paper surveys recent literature in the domain of applying Soft Computing to Investment and Financial Trading. It analyses the literature according to the style of soft computing used, the investment discipline used, the successes demonstrated, and the applicability of the research to real world trading. This papers contribution is to expose the key areas where research is being undertaken, and to attempt to quantify the degree of successes associated with the different research approaches.
Enhancing Security Selection In The Australian Stockmarket Using Fundamental Analysis And Neural Networks, Bruce Vanstone, Gavin Finnie, Clarence Tan
Enhancing Security Selection In The Australian Stockmarket Using Fundamental Analysis And Neural Networks, Bruce Vanstone, Gavin Finnie, Clarence Tan
Bruce Vanstone
This paper examines financial trading from the aspect of security selection. In practice, it is unrealistic for a financial trader to participate in the full market of tradeable securities, and a selection mechanism must be employed to reduce the number of possible securities competing for investment capital. Essentially, there are two main methodologies used, namely, Fundamental Analysis, and Technical Analysis. This paper examines the practice of Fundamental Analysis, and demonstrates how neural networks can be practically employed to enhance the fundamentalist selection process.