论文信息 - Stock Prices Prediction using the Title of Newspaper Articles with Korean Natural Language Processing

Stock Prices Prediction using the Title of Newspaper Articles with Korean Natural Language Processing

Non-quantitative data have a significant impact on the financial market as well as quantitative data. In this paper, we propose CNN model of stock price prediction using Korean natural language processing. In the case of Korean natural language processing research was not actively performed compared to English language. We converted Korean sentences into nouns and vectorized them using skip-grams to extract the characteristics of the words. Then, the vectorized word sentence was used as input data of the CNN model to predict the stock price after 5 days of trading day. Most models have more than 50% prediction accuracy for stock price up and down. The highest accuracy of the model was about 53%. Since the result is not considerable but meaningful, it shows the possibility of developing the stock price prediction model through Korean natural language processing in the future.

[1] Kyoung-jae Kim,et al. Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[2] Alberto Ferreira de Souza,et al. Prediction-based portfolio optimization model using neural networks , 2009, Neurocomputing.

[3] Aderemi Oluyinka Adewumi,et al. Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction , 2014, J. Appl. Math..

[4] Erdinç Altay,et al. Stock Market Forecasting: Artificial Neural Network and Linear Regression Comparison in An Emerging Market , 2006 .

[5] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[6] Yue Zhang,et al. Deep Learning for Event-Driven Stock Prediction , 2015, IJCAI.

[7] Manish Kumar,et al. Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest , 2006 .

[8] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[9] Francis Eng Hock Tay,et al. Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[10] Adriano C. M. Pereira,et al. Stock market's price movement prediction with LSTM neural networks , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[11] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.