Application notes: dynamic physical behavior analysis for financial trading decision support

Authors:
An-Pin Chen;Yu-Chia Hsu
Affiliations:
National Chiao Tung University, Taiwan;National Chiao Tung University, Taiwan
Venue:
IEEE Computational Intelligence Magazine
Year:
2010

Citing 14
Cited 1

An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network

Fuzzy Sets and Systems
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Integrating a piecewise linear representation method and a neural network model for stock trading points prediction

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Portfolio algorithm based on portfolio beta using genetic algorithm

Expert Systems with Applications: An International Journal
Time series prediction using support vector machines: a survey

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Intelligence in Markets: Asset Pricing, Mechanism Design, and Natural Computation [Technology Review]

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Mean-Entropy Models for Fuzzy Portfolio Selection

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Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks

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Support vector machine with adaptive parameters in financial time series forecasting

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Improving option pricing with the product constrained hybrid neural network

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Model Risk for European-Style Stock Index Options

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Dynamic index tracking via multi-objective evolutionary algorithm

Applied Soft Computing

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Abstract

This article aims to discuss the application of computational intelligence (CI) techniques in combination "with classical concepts in physics in devising investment strategies. In the analysis of investment strategies, many CI techniques are employed to predict market trends, such as the neural network (NN), the support vector machine (SVM), and particle swarm optimization (PSO) techniques. Other techniques such as evolutionary computing (EC) and genetic algorithm (GA) are utilized to identify the knowledge rules of trading. However, changes in market behavior are dynamic and time variant. Thus, using a single CI technique can occasionally be better than traditional statistic models, but the trading models may pose risks from the changing market. Recently, the hybrid model and the data mining concept, "which combine multiple CI techniques into multiple stages, have emerged to improve the trading model's stability and profitability. For example, fuzzy logic is employed to differentiate the parameters in the first stage, and then similarity search is used for data clustering in the second stage.