The application of evolutionary approach for stock trend awareness

Tsai, Yi-Chi; Hong, Cheng-Yih

doi:10.1109/icawst.2017.8256468

Cited by 3 publications

(1 citation statement)

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“…Bagging method was used to combine multiple neural network method to predict Chinese stock index (including the Shanghai composite index and Shenzhen component index) [4], each neural network was trained by back propagation method and Adam optimization algorithm, the results show that the method has different accuracy for prediction of different stock index, but the prediction on close is unsatisfactory. The evolutionary method was applied to predict the change trend of stock price [5]. The deep belief network with inherent plasticity was used to predict the stock price time series [6].…”

Section: Related Workmentioning

confidence: 99%

Study on the prediction of stock price based on the associated network model of LSTM

Ding

Qin

2019

Int. J. Mach. Learn. & Cyber.

200

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Stock market has received widespread attention from investors. It has always been a hot spot for investors and investment companies to grasp the change regularity of the stock market and predict its trend. Currently, there are many methods for stock price prediction. The prediction methods can be roughly divided into two categories: statistical methods and artificial intelligence methods. Statistical methods include logistic regression model, ARCH model, etc. Artificial intelligence methods include multi-layer perceptron, convolutional neural network, naive Bayes network, back propagation network, single-layer LSTM, support vector machine, recurrent neural network, etc. But these studies predict only one single value. In order to predict multiple values in one model, it need to design a model which can handle multiple inputs and produces multiple associated output values at the same time. For this purpose, it is proposed an associated deep recurrent neural network model with multiple inputs and multiple outputs based on long short-term memory network. The associated network model can predict the opening price, the lowest price and the highest price of a stock simultaneously. The associated network model was compared with LSTM network model and deep recurrent neural network model. The experiments show that the accuracy of the associated model is superior to the other two models in predicting multiple values at the same time, and its prediction accuracy is over 95%.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Section: Related Workmentioning

confidence: 99%

Study on the prediction of stock price based on the associated network model of LSTM

Ding

Qin

2019

Int. J. Mach. Learn. & Cyber.

200

View full text Add to dashboard Cite

show abstract

Strategic - Risk, Return and Technical Analysis of Stock Prices

et al. 2019

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Stock Market E-Assistance on Platform-as-a-Service (PaaS)

Hameed

2022

International Journal of Cloud Applications and Computing

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Stock market has received widespread attention from investors. How to grasp the changing regularity of the stock market and predict the trend of stock prices has always been a hot spot for investors and researchers. The rise and fall of stock prices are influenced by many factors such as politics, economy, society and market. For stock investors, the trend forecast of the stock market is directly related to the acquisition of profits. The more accurate the forecast, the more effectively it can avoid risks. For listed companies, the stock price not only reflects the company’s operating conditions and future development expectations, but also an important technical index for the analysis and research of the company. Stock forecasting research also plays an important role in the research of a country’s economic development. Therefore, the research on the intrinsic value and prediction of the stock market has great theoretical significance and wide application prospects.

show abstract

The application of evolutionary approach for stock trend awareness

Cited by 3 publications

References 20 publications

Study on the prediction of stock price based on the associated network model of LSTM

Study on the prediction of stock price based on the associated network model of LSTM

Strategic - Risk, Return and Technical Analysis of Stock Prices

Stock Market E-Assistance on Platform-as-a-Service (PaaS)

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