A hybrid fuzzy intelligent agent-based system for stock price prediction

Zarandi, M. H. Fazel; Hadavandi, Esmaeil; Türkşen, I.B.

doi:10.1002/int.21554

Cited by 53 publications

(22 citation statements)

References 45 publications

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“…Chen [27] proposed a heterogeneous fuzzy collaborative forecasting method to predict semiconductor product yield, where experts fitted the yield learning process of the product with FLR by solving mathematical programming problems or training ANNs. Zarandi et al [13] proposed a four-layer fuzzy multiagent system to forecast next-day stock prices based on collaboration among software agents. Chen and Wang [28] and Chen and Romanowski [29] proposed software agents, rather than real experts, for fuzzy collaborative forecasting to expedite collaboration.…”

Section: Literature Reviewmentioning

confidence: 99%

An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

2020

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Current fuzzy collaborative forecasting methods have rarely considered how to determine the appropriate number of experts to optimize forecasting performance. Therefore, this study proposes an evolving partial-consensus fuzzy collaborative forecasting approach to address this issue. In the proposed approach, experts apply various fuzzy forecasting methods to forecast the same target, and the partial consensus fuzzy intersection operator, rather than the prevalent fuzzy intersection operator, is applied to aggregate the fuzzy forecasts by experts. Meaningful information can be determined by observing partial consensus fuzzy intersection changes as the number of experts varies, including the appropriate number of experts. We applied the evolving partial-consensus fuzzy collaborative forecasting approach to forecasting dynamic random access memory product yield with real data. The proposed approach forecasting performance surpassed current fuzzy collaborative forecasting that considered overall consensus, and it increased forecasting accuracy 13% in terms of mean absolute percentage error.

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Section: Literature Reviewmentioning

confidence: 99%

An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

2020

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“…In order to assess the effectiveness and overall efficiency of our stock price prediction algorithm, in this section, we simulate our methodology compared with the method1 [3], method2 [4] and method3 [5]. We take the stock price of IFLYTEK CO., LTD. from December 9 th , 2011 to the later 14 trading days.…”

Section: Simulation and Verificationmentioning

confidence: 99%

A Novel Data Mining Algorithm based on BP Neural Network and Its Applications on Stock Price Prediction

Shu¹,

Li²,

Zhuang³

2016

Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control

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Abstract.As an indispensable part in financial industry, stock investment has gradually become more and more important in people's life. The major fluctuation in stock market directly affects the stability of financial markets and the healthy development of the economy. The successful stock price and trend prediction will help investors to yield profit and provide timely and reasonable guidance to market regulators. Therefore, how to forecast stock market is a crucial and worthy topic in financial area. Neural network simulated how brain functions, can automatically extract features from the historical data and related information to predicate future trend, with the primary characteristics of self-organization and adaption. Therefore, neural network is suitable for processing prediction in the field of nonlinear time series. This paper presents a novel data mining algorithm based on the BP neural network and its applications on stock price prediction. Taking improving the efficiency of the traditional back propagation network, we revise the perspective error, and modify incentive function and the adaptive learning rate in order to avoid the local minimum and decrease training time. Moreover, to eliminate the influences caused by noisy data, we combine the fuzzy rough set theory to pre-process the original data. The experimental result verifies feasibility of our model, without the local minimum and slow convergence speed, compared to the other state-of-the-art approaches. Therefore, our algorithm on the predications of the exact stock price value and future trend are applicable and satisfactory.

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“…Exponential smoothing ,moving average and ARIMA are common linear models for predicting future prices [17,18]. Several research activities have done for extensive predictions with Artificial Neural Networks (ANN), Genetic Algorithms (GA), fuzzy logic etc [19][20][21]. Zhang et al [22] combined Improved Bacterial Chemotaxis Optimization (IBCO) with artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Stock Market Prediction

Nabipour¹,

Nayyeri²,

Jabani³

et al. 2020

Preprint

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Prediction of stock groups values has always been attractive and challenging for shareholders. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms utilized for prediction of future values of stock market groups. We employed Decision Tree, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting and eXtreme Gradient Boosting (XGBoost), and Artificial neural network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting and XGBoost.

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A hybrid fuzzy intelligent agent-based system for stock price prediction

Cited by 53 publications

References 45 publications

An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

A Novel Data Mining Algorithm based on BP Neural Network and Its Applications on Stock Price Prediction

Deep Learning for Stock Market Prediction

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