An Ensemble Classifier for Stock Trend Prediction Using Sentence-Level Chinese News Sentiment and Technical Indicators.

Chun Hao Chen; Po Yeh Chen; Jerry Chun Wei Lin

doi:10.9781/ijimai.2022.02.004

Authors

Chun Hao Chen National Taipei University of Technology
Po Yeh Chen Tamkang University
Jerry Chun Wei Lin Western Norway University of Applied Sciences

DOI:

https://doi.org/10.9781/ijimai.2022.02.004

Keywords:

News, Sentiment Analysis, Trend Prediction, Support Vector Machine, Technique Indicators

Abstract

In the financial market, predicting stock trends based on stock market news is a challenging task, and researchers are devoted to developing forecasting models. From the existing literature, the performance of the forecasting model is better when news sentiment and technical analysis are considered than when only one of them is used. However, analyzing news sentiment for trend forecasting is a difficult task, especially for Chinese news, because it is unstructured data and extracting the most important features is difficult. Moreover, positive or negative news does not always affect stock prices in a certain way. Therefore, in this paper, we propose an approach to build an ensemble classifier using sentiment in Chinese news at sentence level and technical indicators to predict stock trends. In the training stages, we first divide each news item into a set of sentences. TextRank and word2vec are then used to generate a predefined number of key sentences. The sentiment scores of these key sentences are computed using the given financial lexicon. The sentiment values of the key phrases, the three values of the technical indicators and the stock trend label are merged as a training instance. Based on the sentiment values of the key sets, the corpora are divided into positive and negative news datasets. The two datasets formed are then used to build positive and negative stock trend prediction models using the support vector machine. To increase the reliability of the prediction model, a third classifier is created using the Bollinger Bands. These three classifiers are combined to form an ensemble classifier. In the testing phase, a voting mechanism is used with the trained ensemble classifier to make the final decision based on the trading signals generated by the three classifiers. Finally, experiments were conducted on five years of news and stock prices of one company to show the effectiveness of the proposed approach, and results show that the accuracy and P / L ratio of the proposed approach are 61% and 4.0821 are better than the existing approach.

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References

M. Drenovak, V. Ranković, B. Urošević, R. Jelic, “Mean-maximum drawdown optimization of buy-and-hold portfolios using a multi-objective evolutionary algorithm,” Finance Research Letters, p. 102328, 2021.

T. E. Simos, S. D. Mourtas, V. N. Katsikis, “Time- varying black-litterman portfolio optimization using a bio-inspired approach and neuronets,” Applied Soft Computing, p. 107767, 2021.

S. T. Tayalı, “A novel backtesting methodology for clustering in mean–variance portfolio optimization,” Knowledge-Based Systems, vol. 209, p. 106454, 2021.

W. Ding, K. Mazouz, Q. Wang, “Volatility timing, sentiment, and the short-term profitability of vix-based cross-sectional trading strategies,” Journal of Empirical Finance, vol. 63, pp. 42–56, 2021.

X. Wu, H. Chen, J. Wang, L. Troiano, V. Loia, H. Fujita, “Adaptive stock trading strategies with deep reinforcement learning methods,” Information Sciences, vol. 538, pp. 142–158, 2020.

A. K. Banerjee, A. Dionisio, H. K. Pradhan, B. Mahapatra, “Hunting the quicksilver: Using textual news and causality analysis to predict market volatility,” International Review of Financial Analysis, p. 101848, 2021.

N. Pröllochs, S. Feuerriegel, D. Neumann, “Negation scope detection in sentiment analysis: Decision support for news-driven trading,” Decision Support Systems, vol. 88, pp. 67–75, 2016.

W. Chen, M. Jiang, W. G. Zhang, Z. Chen, “A novel graph convolutional feature based convolutional neural network for stock trend prediction,” Information Sciences, vol. 556, pp. 67–94, 2021.

J. Long, Z. Chen, W. He, T. Wu, J. Ren, “An integrated framework of deep learning and knowledge graph for prediction of stock price trend: An application in chinese stock exchange market,” Applied Soft Computing, vol. 91, p. 106205, 2020.

X. Zhang, A. Li, R. Pan, “Stock trend prediction based on a new status box method and adaboost probabilistic support vector machine,” Applied Soft Computing, vol. 49, pp. 385–398, 2016.

X. Li, H. Xie, L. Chen, J. Wang, X. Deng, “News impact on stock price return via sentiment analysis,” Knowledge-Based Systems, vol. 69, pp. 14–23, 2014.

G. Löffler, L. Norden, A. Rieber, “Negative news and the stock market impact of tone in rating reports,” Journal of Banking & Finance, vol. 133, p. 106256, 2021.

S. Kelly, K. Ahmad, “Estimating the impact of domain-specific news sentiment on financial assets,” Knowledge-Based Systems, vol. 150, pp. 116–126, 2018.

A. Thakkar, K. Chaudhari, “Predicting stock trend using an integrated term frequency–inverse document frequency-based feature weight matrix with neural networks,” Applied Soft Computing, vol. 96, p. 106684, 2020.

L. Lei, “Wavelet neural network prediction method of stock price trend based on rough set attribute reduction,” Applied Soft Computing, vol. 62, pp. 923–932, 2018.

C. H. Chen, P. Shih, G. Srivastava, S. T. Hung, J. C. W. Lin, “Evolutionary trading signal prediction model optimization based on chinese news and technical indicators in the internet of things,” IEEE Internet of Things Journal, p. Early access, 2021.

G. Liu, X. Wang, “A new metric for individual stock trend prediction,” Engineering Applications of Artificial Intelligence, vol. 82, pp. 1–12, 2019.

P. Y. Hao, C. F. Kung, C. Y. Chang, J. B. Ou, “Predicting stock price trends based on financial news articles and using a novel twin support vector machine with fuzzy hyperplane,” Applied Soft Computing, vol. 98, p. 106806, 2021.

W. Long, L. Song, Y. Tian, “A new graphic kernel method of stock price trend prediction based on financial news semantic and structural similarity,” Expert Systems with Applications, vol. 118, pp. 411–424, 2019.

P. Rajendiran, P. L. K. Priyadarsini, “Survival study on stock market prediction techniques using sentimental analysis,” Materials Today: Proceedings, p. Early access, 2021.

C. Martín, D. Quintana, P. Isasi, “Dynamic generation of investment recommendations using grammatical evolution,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, pp. 104–111, 2021.

L. Chen, L. Sun, C. M. Chen, M. E. Wu, J. M. T. Wu, “Stock trading system based on machine learning and kelly criterion in internet of things,” Wireless Communications and Mobile Computing, vol. 2021, pp. 1–9, 2021.

J. M. T. Wu, L. Sun, G. Srivastava, J. C. W. Lin, “A long short-term memory network stock price prediction with leading indicators,” Big Data, vol. 9, pp. 343–357, 2021.

J. M. T. Wu, S. Sun, L., G., J. C. W. Lin, “A novel synergetic lstm-ga stock trading suggestion system in internet of things,” Mobile Information Systems, vol. 2021, pp. 1–15, 2021.

H. Huang, X. Liu, Y. Zhang, F. C., “News-driven stock prediction via noisy equity state representation,” Neurocomputing, vol. 470, pp. 66–75, 2022.

A. Derakhshan, H. Beigy, “Sentiment analysis on stock social media for stock price movement prediction,” Engineering Applications of Artificial Intelligence, vol. 85, pp. 569–578, 2019.

Y. Li, H. Bu, J. Li, J. Wu, “The role of text-extracted investor sentiment in chinese stock price prediction with the enhancement of deep learning,” International Journal of Forecasting, vol. 36, pp. 1541–1562, 2020.

C. Song, X. Wang, P. Cheng, J. Wang, L. Li, “Sacpc: A framework based on probabilistic linguistic terms for short text sentiment analysis,” Knowledge-Based Systems, vol. 194, p. 105572, 2020.

J. Wu, K. Lu, S. Su, S. Wang, “Chinese micro- blog sentiment analysis based on multiple sentiment dictionaries and semantic rule sets,” IEEE Access, vol. 7, pp. 183924–183939, 2019.

W. Wang, D. Huang, J. Cao, “Chinese syntax parsing based on sliding match of semantic string,” ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 7, pp. 1–14, 2020.

P. Wang, Y. Luo, Z. Chen, L. He, Z. Zhang, “Orientation analysis for chinese news based on word embedding and syntax rules,” IEEE Access, vol. 7, pp. 159888–15898, 2019.

S. Wang, J. Zhang, C. Zong, “Empirical exploring word-character relationship for chinese sentence representation,” ACM Transactions on Asian and Low- Resource Language Information Processing, vol. 17, pp. 1–18, 2019.

F. Chen, Y. Huang, “Knowledge-enhanced neural networks for sentiment analysis of chinese reviews,” Neurocomputing, vol. 368, pp. 51–58, 2019.

T. Daudert, “Exploiting textual and relationship information for finegrained financial sentiment analysis,” Knowledge-Based Systems, p. 107389, 2021.

G. Xu, Y. Meng, X. Qiu, Z. Yu, X. Wu, “Sentiment analysis of comment texts based on bilstm,” IEEE Access, vol. 7, pp. 51522–51532, 2019.

J. Yang, R. Yang, H. Lu, C. Wang, J. Xie, “Multi- entity aspect-based sentiment analysis with context, entity, aspect memory and dependency information,” ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 18, pp. 1–22, 2019.

M. Pota, H. Ventura, M. amd Fujita, M. Esposito, “Multilingual evaluation of pre-processing for bert-based sentiment analysis of tweets,” Expert Systems with Applications, vol. 181, p. 115119, 2021.

H. Peng, Y. Ma, S. Poria, Y. Li, E. Cambria, “Phonetic- enriched text representation for chinese sentiment analysis with reinforcement learning,” Information Fusion, vol. 70, pp. 88–99, 2021.

A. Li, P. Wu, W. Wang, “Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong,” Information Processing & Management, vol. 57, p. 102212, 2020.

T. Loughran, B. Mcdonald, “When is a liability not a liability? textual analysis, dictionaries, and 10-ks,” Cognitive Computation, pp. 1167–1176, 2011.