Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis

Esfahanipour, Akbar; Aghamiri, Werya

doi:10.1016/j.eswa.2009.11.020

Cited by 100 publications

(55 citation statements)

References 27 publications

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“…Moreover, TSK systems are very suitable for modeling dynamic systems in comparison with other fuzzy modeling methods [17]. Also, the good ability of the TSK systems in forecasting the stock prices has been confirmed [18,19].…”

Section: Introductionmentioning

confidence: 89%

MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

Mousavi

Esfahanipour

Zarandi

2015

Applied Soft Computing

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Section: Introductionmentioning

confidence: 89%

MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

Mousavi

Esfahanipour

Zarandi

2015

Applied Soft Computing

Self Cite

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“…Esfahanipour and Aghamiri (2010) also used ANFIS and 'Gaussian' membership function for Tehran stock exchange Indexes (TEPIX) price prediction. Trinkle (2005) applied ANFIS and neural network to forecast the annual excess returns.…”

Section: Literature and Algorithm Reviewmentioning

confidence: 99%

An innovative recurrent error-based neuro-fuzzy system with momentum for stock price prediction

Mahmud

Meesad

2015

Soft Comput

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Neuro-fuzzy system is now one of the most widely used tools in the field of artificial intelligence systems. This study proposes a novel approach for time series stock market price prediction using a recurrent error-based neuro-fuzzy system with momentum (RENFSM). The basic idea of this approach is to use time series price momentum and time series prediction error adjusted to the well-known adaptive neuro-fuzzy inference system, ANFIS. Extended from ANFIS, the aim of this study is to propose a reliable prediction system with minimal error. Moreover, to evaluate the proposed model strength, four top-listed stocks from Dhaka stock exchange were applied. In the experiments, several choices of momentum from 3 to 20 days are selected for data preprocessing. It was found that the proposed RENFSM performed superiorly and was more reliable compared to the existing methods such as ANFIS and neural networks.

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“…One of the recent work in this direction is that presented by Esfahanipour and Aghamiri (Esfahanipour and Aghamiri 2010). In this work, the authors developed Neuro-Fuzzy Inference System adopted on a Takagi-Sugeno-Kang (TSK) type Fuzzy Rule Based System for stock price prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model

Olatunji¹,

Al-Ahmadi²,

Elshafei³

et al. 2013

IJIIS

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Abstract:In this paper, we have proposed artificial neural network for the prediction of Saudi stock market. The proposed predictions model, with its high degree of accuracy, could be used as investment advisor for the investors and traders in the Saudi stock market. The proposed model is based mainly on Saudi Stock market historical data covering a large span of time. Achieving reasonable accuracy rate of predication models will surely facilitate an increased confidence in the investment in the Saudi stock market. We have only used the closing price of the stock as the stock variable considered for input to the system. The number of windows gap to determine the numbers of previous days to be used in predicting the next day closing price data has been choosing based on experimental simulation carried out to determine the best possible value. Our results indicated that the proposed ANN model predicts the next day closing price stock market value with a very low RMSE down to 1.8174, very low MAD down to 18.2835, very low MAPE of down to 1.6476 and very high correlation coefficient of up to 99.9% for the test set, which is an indication that the model adequately mimics the trend of the market in its prediction. This performance is really encouraging and thus the proposed system will impact positively on the analysis and prediction of Saudi stock market in general.

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Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis

Cited by 100 publications

References 27 publications

MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

An innovative recurrent error-based neuro-fuzzy system with momentum for stock price prediction

Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model

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