Stock Market Forecasting with Financial Micro-Blog Based on Sentiment and Time Series Analysis

doi:10.1007/s12204-017-1818-4

摘要/Abstract

摘要： During the past few decades, time series analysis has become one popular method for solving stock forecasting problem. However, depending only on stock index series makes the performance of the forecast not good enough, because many external factors which may be involved are not taken into consideration. As a way to deal with it, sentiment analysis on online textual data of stock market can generate a lot of valuable information as a complement which can be named as external indicators. In this paper, a new method which combines the time series of external indicators and the time series of stock index is provided. A special text processing algorithm is proposed to obtain a weighted sentiment time series. In the experiment, we obtain financial micro-blogs from some famous portal websites in China. After that, each micro-blog is segmented and preprocessed, and then the sentiment value is calculated for each day. Finally, an NARX time series model combined with the weighted sentiment series is created to forecast the future value of Shanghai Stock Exchange Composite Index (SSECI). The experiment shows that the new model makes an improvement in terms of the accuracy.

关键词: time series, micro-blog, sentiment analysis, parsing, neural network

Abstract: During the past few decades, time series analysis has become one popular method for solving stock forecasting problem. However, depending only on stock index series makes the performance of the forecast not good enough, because many external factors which may be involved are not taken into consideration. As a way to deal with it, sentiment analysis on online textual data of stock market can generate a lot of valuable information as a complement which can be named as external indicators. In this paper, a new method which combines the time series of external indicators and the time series of stock index is provided. A special text processing algorithm is proposed to obtain a weighted sentiment time series. In the experiment, we obtain financial micro-blogs from some famous portal websites in China. After that, each micro-blog is segmented and preprocessed, and then the sentiment value is calculated for each day. Finally, an NARX time series model combined with the weighted sentiment series is created to forecast the future value of Shanghai Stock Exchange Composite Index (SSECI). The experiment shows that the new model makes an improvement in terms of the accuracy.

Key words: time series, micro-blog, sentiment analysis, parsing, neural network

中图分类号:

WANG Yinglin (王英林). Stock Market Forecasting with Financial Micro-Blog Based on Sentiment and Time Series Analysis[J]. 上海交通大学学报（英文版）, 2017, 22(2): 173-179.

WANG Yinglin (王英林). Stock Market Forecasting with Financial Micro-Blog Based on Sentiment and Time Series Analysis[J]. Journal of shanghai Jiaotong University (Science), 2017, 22(2): 173-179.

参考文献 12

[1]	PEDRYCZ W, CHEN S M. Time series analysis, modelingand applications [M]. Berlin: Springer-Verlag,2013.
[2]	WANG L, ZOU H F, SU J, et al. An ARIMA-ANNhybrid model for time series forecasting [J]. SystemsResearch and Behavioral Science, 2013, 30: 244-259.
[3]	CORTEZ P, RIO M, ROCHA M, et al. Multi-scaleInternet traffic forecasting using neural networks andtime series methods [J]. Expert Systems, 2012, 29(2):143-155.
[4]	KUMAR D A, MURUGAN S. Performance analysisof Indian stock market index using neural networktime series model [C]//Proceedings of the 2013 InternationalConference on Pattern Recognition, Informaticsand Mobile Engineering (PRIME). [s. l.]: IEEE,2013: 72-78.
[5]	O’CONNOR N, MADDEN M G. A neural network approachto predicting stock exchange movements usingexternal factors [J]. Knowledge-Based Systems, 2006,19(5): 371-378.
[6]	SCHUMAKER R P, ZHANG Y L, HUANG C N, etal. Evaluating sentiment in financial news articles [J].Decision Support Systems, 2012, 53(3): 458-464.
[7]	LIANG X, CHEN R C, HE Y, et al. Associatingstock prices with web financial information time seriesbased on support vector regression [J]. Neurocomputing,2013, 115: 142-149.
[8]	HAGENAU M, LIEBMANN M, NEUMANN D. Automatednews reading: Stock price prediction basedon financial news using context-capturing features [J].Decision Support Systems, 2013, 55: 685-697.
[9]	RUIZ E J, HRISTIDIS V, CASTILLO C, et al. Correlatingfinancial time series with micro-blogging activity[C]// Proceedings of the fifth ACM International Conferenceon Web Search and Data Mining. New York,USA: ACM, 2012: 513-522.
[10]	BOLLEN J, MAO H, ZENG X J. Twitter mood predictsthe stock market [J]. Journal of ComputationalScience, 2011, 2(1): 1-8.
[11]	LI Q, WANG J, WANG F, et al. The role of socialsentiment in stock markets: A view from joint effects ofmultiple information sources [J]. Multimedia Tools andApplications, 2016. DOI: 10. 1007/s11042-016-3643-4(published online).
[12]	ROMANOWSKI A, SKUZA M. Towards predictingstock price moves with aid of sentiment analysis ofTwitter social network data and big data processingenvironment [C]// Advances in Business ICT: NewIdeas from Ongoing Research, Studies in ComputationalIntelligence. [s.l.]: Springer International Publishing,2017: 105-123.

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