Financial Market Trading System With a Hierarchical Coevolutionary Fuzzy Predictive Model

Huang, Hao; Pasquier, M.; Quek, Chai

doi:10.1109/tevc.2008.911682

Cited by 42 publications

(14 citation statements)

References 32 publications

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“…Preprocessing the data is plays an important role in MLP. [4] Pre-processing leads to high accuracy and less computational cost. Pre-processing helps in modifying and reducing the data set size, removing a typical and noisy training samples and correcting possible erroneous training sample.…”

Section: Data Preprocessing and Normalization 1) Data Processingmentioning

confidence: 99%

“…[3] Support vector Regression (SVR) has a characteristic that instead of minimizing the observed training error, SVR tries to reduce the generalized error bound so as to achieve generalized performance. [4] This generalization error bound is the combination of the training error and a regularization term that controls the complexity of the hypothesis space [5] [6]. In this paper MLP is optimized by SVR.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient AI Model for Financial Market Prediction Optimized by SVR

Dash¹

2018

IJRASET

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Stock market is dynamic in nature. So prediction of the stock index accurately is a tedious task. But fortunately if the prediction will be accurate then it is easy for the traders to make decisions easily. The main challenge of the stock market is the dynamic nature which is influenced by several factors such as political, financial etc. So a robust and stable model which can give the accurate prediction is needed to be designed. In this paper we have proposed a hybrid model using Multi Layer Perceptron (MLP) optimized by Support Vector Regression Algorithm (SVR) to predict the JSPL stock data. This developed model will be evaluated and compared based on the various evaluations.

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Section: Data Preprocessing and Normalization 1) Data Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient AI Model for Financial Market Prediction Optimized by SVR

Dash¹

2018

IJRASET

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“…There are also many hybrid methodologies that incorporate fuzzy or some other kind of logic within the systems for authomated/algorithmic trading. Some of them are [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

An Automated System for Stock Market Trading Based on Logical Clustering

et al. 2018

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Abstract:In this paper a novel clustering-based system for automated stock market trading is introduced. It relies on interpolative Boolean algebra as underlying mathematical framework used to construct logical clustering method which is the central component of the system. The system uses fundamental analysis ratios, more precisely market valuation ratios, as clustering variables to differentiate between undervaluated and overvaluated stocks. To structure investment portfolio, the proposed system uses special weighting formulas which automatically diversify investment funds. Finally, a simple trading simulation engine is developed to test our system on real market data. The proposed system was tested on Belgrade Stock Exchange historical data and was able to achieve a high rate of return and to outperform the BelexLine market index as a benchmark variable. The paper has also provided in-depth analysis of the system's investment decision making process which reveals some exciting insights.

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“…Fuzzy systems, on the contrary, are good at explaining their operation principle in the form of readable fuzzy if-then rules [11]. In other words, fuzzy models' operating mechanisms are intuitive and easily understood by designers.…”

Section: Introductionmentioning

confidence: 99%

“…Huang et al [11] described the application of the hierarchical co-evolutionary fuzzy system for predicting financial time series. To construct an accurate predictive model, a form of generic membership function (MF), named irregular shaped MF, was employed and a hierarchical co-evolutionary genetic algorithm was adopted.…”

Section: Introductionmentioning

confidence: 99%

Integration of mutual information and CRPSO-based fuzzy model for stock index forecasting

Yu¹,

Kim²

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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In this paper, the integration of mutual information (MI) and fuzzy model is proposed to predict stock indexes with complex and non-linear characteristics. Technical indicators are considered as initial input candidates and significant inputs are determined by MI-based input selection method. To identify the structures and parameters of fuzzy models simultaneously, cooperative random learning particle swarm optimization (CRPSO), proposed by Zhao et al., is used. To confirm the effectiveness, the proposed method and comparison methods are applied to the Korea Composite Stock Price Index (KOSPI). The experimental results show that the proposed method, on average, outperforms other comparison methods.

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Financial Market Trading System With a Hierarchical Coevolutionary Fuzzy Predictive Model

Cited by 42 publications

References 32 publications

An Efficient AI Model for Financial Market Prediction Optimized by SVR

An Efficient AI Model for Financial Market Prediction Optimized by SVR

An Automated System for Stock Market Trading Based on Logical Clustering

Integration of mutual information and CRPSO-based fuzzy model for stock index forecasting

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