Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market

Qiu, Mingyue; Shen, Yu; Akagi, Fumio

doi:10.1016/j.chaos.2016.01.004

Cited by 201 publications

(75 citation statements)

References 26 publications

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“…To implement this Phase the Multilayer Perceptron is used, which is a feedforward Artificial Neural Network (ANN). Funahashi [2], Hoenike and Stunchcombe [3] and Qiu et al [11] have all shown that only one hidden layer can effectively generate highly accurate results by also improving the processing time. Therefore an ANN Multilayer Perceptron with Backpropagation of error has been used to train the machine with 1, 2, 3 and 4 hidden layers 10 fold cross validation.…”

Section: Processingmentioning

confidence: 99%

Designing a Context-Aware Cyber Physical System for Smart Conditional Monitoring of Platform Equipment

Majdani

Petrovski

Doolan

2016

Engineering Applications of Neural Networks

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Section: Processingmentioning

confidence: 99%

Designing a Context-Aware Cyber Physical System for Smart Conditional Monitoring of Platform Equipment

Majdani

Petrovski

Doolan

2016

Engineering Applications of Neural Networks

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“…Standard deviation -measured variance or deviation of the data from their average estimated [5]. Low standard deviation means that data are closely distributed around their mean value, while high value of the standard deviation indicates that the data are arranged in a wider range.…”

Section: -2mentioning

confidence: 99%

“…Before analyzing of the input data by the neural network, they should be pretreated in order to increase the accuracy of the output results and to facilitate the learning process. The most commonly used techniques for the preprocessing of the data are transformation and normalization [5]. The transformation include the modeling of the raw data, while the normalization is used for is used for even distribution and scaling of data to eligible values for the neural network.…”

Section: Preliminary Processing Of the Datamentioning

confidence: 99%

“…Its value changes from 0 to 100. It is calculated as the ratio between positive and negative differences of the closing price for a certain period of time -usually 7 to 14 days: ( 5 ) where is the average of all positive differences between the closing prices for the period N, and is the average of all negative differences between the closing prices for the same period. If is 0, the value of RSI is considered to be 100.…”

Section: -2mentioning

confidence: 99%

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Artificial neural network intelligent method for prediction

Trifonov

Yoshinov

Pavlova

et al. 2017

AIP Conference Proceedings

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Coupling and power transfer efficiency enhancement of modular and array of planar coils using in-plane ringshaped inner ferrites for inductive heating applications Journal of Applied Physics 122, 014902 (2017) Abstract. Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

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“…The evolutionary algorithms slow down speed whenever used in other techniques. Qiu et al (2016) proposed a hybrid-approach to predict Japanese-stock-market. In this approach new data-set was introduce as input to maps non-linear data.…”

Section: Literature Reviewmentioning

confidence: 99%

Pakistan stock exchange prediction using RIDOR classifier

Akram¹,

Imran

2017

Int. j. adv. appl. sci.

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Now-a-days stock market prediction is important activity and interesting topic for professional analyst. The stock market is biggest investment platform, however investment in stock market need accurate and complete information. The accurate and fast-prediction of stock market attracted the investor for profitable output. The stock market prediction is complex task because uncertainty involves in the market movement up/down. Mostly the machine learning techniques (MLT) are used for accurate prediction of stock market, because of its capability of partitioning; extract hidden information form raw data, monitors the fluctuation rate of stock market, suitable for nonlinear data etc. This research work is about to review the strength and weakness of existing stock market prediction techniques. This research work proposed a Ripple-down-rule-learner (RIDOR) classifier based technique. The RIDOR rule base classifier generates default value and work like if-else statement for uncertainties. The other contribution is a prepared data-set using technical indicator to predict stock market trend. The output of the propose model is outperformed as compare to the exiting techniques.

show abstract

Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market

Cited by 201 publications

References 26 publications

Designing a Context-Aware Cyber Physical System for Smart Conditional Monitoring of Platform Equipment

Designing a Context-Aware Cyber Physical System for Smart Conditional Monitoring of Platform Equipment

Artificial neural network intelligent method for prediction

Pakistan stock exchange prediction using RIDOR classifier

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