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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (12): 33-42    DOI: 10.11925/infotech.2096-3467.2018.0420
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Predicting Stock Prices with Text and Price Combined Model
Chuanming Yu1,Yutian Gong1,Feng Wang1,Lu An2()
1School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
2School of Information Management, Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] This paper tries to predict stock price fluctuation with the help of big data, aiming to improve the accuracy of the forecasting and reduce the trading risks. [Methods] We proposed a new Text and Price Combined Model (TPCM) to process comments retrieved from a stock forum. Then, we employed deep representation learning algorithm to generate text feature matrix and utilized the K-means clustering method to generate text category. Finally, we used the Multi-Layer Perceptron (MLP) to predict stock price fluctuation based on the opening price, closing price and other 15 original price indicators. [Results] The accuracy of TPCM was 65.91%, which was 7.76% higher than that of the model (58.15%) employing price features only, and 11.37% higher than that of the model (54.54%) employing text features only. [Limitations] The study only used one stock to examine the proposed model. [Conclusions] Stock price forecasting could be improved through the combination of text and price, which creates novel perspectives for future studies.

Key wordsText      Stock Price      Stock Price Fluctuation Prediction      Text and Price Combined Model     
Received: 16 April 2018      Published: 16 January 2019

Cite this article:

Chuanming Yu,Yutian Gong,Feng Wang,Lu An. Predicting Stock Prices with Text and Price Combined Model. Data Analysis and Knowledge Discovery, 2018, 2(12): 33-42.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0420     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I12/33

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