An adaptive neuro-fuzzy system for stock portfolio analysis

Alizadeh, Meysam; Rada, Roy; Jolai, Fariborz; Fotoohi, Elnaz

doi:10.1002/int.20456

Cited by 20 publications

(13 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Clustering the input space and projecting the input clusters to the output space. • Clustering the input and output space and then projecting the multidimensional clusters to each one of the two spaces [13].…”

Section: E Projectionmentioning

confidence: 99%

Fuzzy clustering rule-based expert system for stock price movement prediction

Shakeri

Zarandi

Tarimoradi

et al. 2015

2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) Held Jointly With 2015 5th World Con

View full text Add to dashboard Cite

Through the years, the ability to predict the future trend of financial time series has drawn serious attention from both researchers and practitioners aiming to have better investment decisions. In this paper a fuzzy rule-based expert system is developed for predicting stock price movement. The importance of the proposed expert system is that it would be applicable for stock market's speculators and traders' daily transactions. For the experiment and in order to demonstrate the effectiveness of the model, the stock price of Apple Company is used as a sample data set. Keywords-prediction; stock market; expert system; fuzzy pattern recognition978-1-4673-7248-0/15/$31.00 ©2015 IEEE

show abstract

Section: E Projectionmentioning

confidence: 99%

Fuzzy clustering rule-based expert system for stock price movement prediction

Shakeri

Zarandi

Tarimoradi

et al. 2015

2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) Held Jointly With 2015 5th World Con

View full text Add to dashboard Cite

show abstract

“…For example, the portfolio programming approaches have been created based on the programming calculation. They include the portfolio model based on the fuzzy goal programming, the portfolio optimization approach using the quadratic programming, the portfolio selection approach using the multiobjective stochastic programming, the adaptive neuro‐fuzzy portfolio model, the multiperiod fuzzy portfolio optimization model, and the fuzzy multiobjective higher order moment portfolio model . In addition, the portfolio approaches have been proposed based on the operational models such as the decision support portfolio selection approach, the neural network portfolio model, the value at risk portfolio model, the data envelopment analysis cross‐efficiency portfolio model, the genetic algorithm portfolio model, the mean‐semi‐entropy portfolio selection approach, and the P‐spline clustering portfolio selection model .…”

Section: Introductionmentioning

confidence: 99%

Score-hesitation trade-off and portfolio selection under intuitionistic fuzzy environment

Zhou

Zhang

2018

Int J Intell Syst

View full text Add to dashboard Cite

Qualitative portfolio selection approach is a suitable technique for obtaining an optimal portfolio when quantitative data are unavailable and traditional portfolio models are ineffective. However, few studies focus on this issue. This study addresses the lack of research by defining the score‐hesitation trade‐off rule and introducing the intuitionistic fuzzy set (IFS), based on which an intuitionistic fuzzy portfolio selection (IFPS) model is proposed. The IFS is introduced because of its comprehensive consideration of preference and nonpreference, and is used to represent qualitatively evaluated information from investors and experts. Furthermore, an intuitionistic fuzzy investment scenario is established and a trisection approach is designed to distinguish three types of risk investors, based on which three corresponding IFPS models are constructed. After this, a portfolio selection process under the intuitionistic fuzzy environment is provided, and a simple example is given to show the application of the process. In addition, the investment opportunities and efficient frontier of the IFPS model are investigated to demonstrate the effectiveness of the proposed portfolio selection model. Finally, an example of calculating optimal investment ratios and selecting an optimal portfolio for four newly listed stocks in China is provided to demonstrate the feasibility and practicability of the proposed approaches.

show abstract

“…The basic idea of this multi-model approach is the use of each component model's unique capability to better capture different patterns in the data. Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model (Mousavi et al 2014;Hwang and Oh 2010;Alizadeh et al 2011;Shen et al 2011;Kar et al 2015). Baba et al (2000) applied NNs and GAs to design an intelligent decision support system (DSS) for analyzing the Tokyo Stock Exchange Prices Indexes (TOPIX).…”

Section: Introductionmentioning

confidence: 99%

Development of an evolutionary fuzzy expert system for estimating future behavior of stock price

Mehmanpazir

Asadi

2016

J Ind Eng Int

View full text Add to dashboard Cite

The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a ''data mining-based evolutionary fuzzy expert system'' (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K-means is used to divide the data into subpopulations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for stock price forecasting problems.

show abstract

An adaptive neuro-fuzzy system for stock portfolio analysis

Cited by 20 publications

References 26 publications

Fuzzy clustering rule-based expert system for stock price movement prediction

Fuzzy clustering rule-based expert system for stock price movement prediction

Score-hesitation trade-off and portfolio selection under intuitionistic fuzzy environment

Development of an evolutionary fuzzy expert system for estimating future behavior of stock price

Contact Info

Product

Resources

About