基于推特情感分析预测股指回报率

Journal: Economics DOI: 10.12238/ej.v7i6.1611

赵丹阳

中国社会科学院大学国际政治经济学院

Abstract

随着互联网经济的发展,互联网评论渗透在人们生活的方方面面。为了研究Twitter上关于新能源汽车的大量评论情绪是否是TESLA的股价波动产生的原因, 本论文假设从Twitter收集到的用户情绪数据与TESLA股票市场价格相关。并采取CS新能车指数399976和Twitter上的关于新能源汽车的评论情绪数据与CS新能源车指数的股价数据进行格兰杰因果检验。研究结果表明中国投资者情绪是指数价格变化的主要原因,且投资者对新能源汽车指数的正面冲击是短期的,长期来看情绪对股票价格的影响将会消失。

Keywords

股价预测;情绪分析;行为金融学

References

[1] Author Index/Текст:электронный // Applied Hydro–Aeromechanics in Oil and Gas Drilling / Book Authors: _:n138. — Hoboken, NJ, USA: John Wiley & Sons,Inc., 2010.—С.431–432.—URL:https://onlinelibrary.wiley.com/doi/10.1002/9780470542392.indauth(датаобращения:02.05.2022).
[2] Bagheri H. Twitter Sentiment Analysis / H. Bagheri, M. J.Islam.—Open Science Framework,2017.—URL:https://osf.io/6x c4y(дата обращения: 02.05.2022).— Текст: электронный.
[3] Bing L. Public Sentiment Analysis in Twitter Data for Prediction of a Company’s Stock Price Movements / L. Bing, K.C.C.Chan,C.Ou.—Текст:электронный// 2014 IEEE 11th International Conference on e-Business Enginee ring 2014 IEEE 11th International Conference on e–Business Engineering(ICEBE).—Guangzhou,China:IEEE,2014.-С.232-239.—URL:http://ieeexplore.ieee.org/document/6982085/(датаобращения:02.05.2022).
[4] Chen L.-P.Using Machine Learning Algorithms on Pred iction of Stock Price / L.–P. Chen // Journal of Modeling and Optimization.—2020.—Т.12.—№2.—С.84–99.
[5] Comparing Methods for Twitter Sentiment Analysis: / E. Psomakelis, K.Tserpes, D. Anagnostopoulos, T. Varvarigou. Текст:электронный//Proceedings of the Int ernational Conference on Knowledge Discovery and Informatio n Retrieval International Conference on Knowledge Discovery and Information Retrieval.—Rome,Italy:SCITEPRESS–Science and and Technology Publications, 2014. — Comparing Methods for Twitter Sentiment Analysis.—С.225-232.URL:http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/000507530225 0232 (датаобращения:02.05.2022).
[6] Dinsoreanu M.Unsupervised Twitter Sentiment Classif ication:/M.Dinsoreanu,A.Bacu.—Текст:электронный//Proceedings of the International Conference on Knowl edge Management and Information Sharing International Conf erence on Knowledge Management and Information Sharing. —Rome,Italy:SCITEPRESS–Science and and Technology Publicati ons,2014.—Unsupervised Twitter Sentiment Classification.—С. 220–227.—URL: http://www.scitepress.org/DigitalLibrary/ Link.aspx?doi=10.5220/0005079002200227(датаобращения:02.05.2022).
[7] El Kalush Y. What is the impact of the crude oil price index on the performance of oil and gas firms? : Master of Business Administration/Y.El Kalush. — University of Northern British Columbia,2009.—URL: http://unbc.arcabc.ca/islandora/ object/unbc%3A16425(дата обращения: 02.05.20 22).—Текст:электронный.
[8] Gupta R. Forecasting oil and stock returns with a Qual VAR using over 150years off data / R. Gupta, M. Wohar // EnergyEconomics.-2017.-Т.62.-С.181-186.
[9] Hatch O. G. The Politic of Supply–Side Economics / O.G.Hatch.—Текст:электронный//Review.—1981.—Т.63.URL:https://research.stlouisfed.org/publications/review/1981/05/01/the–politic–of–supply–side–economics(датаобращения:02.05.2022).
[10] Xiao W. Sentiment analysis in twitter: M.Phil. / W. Xiao.—Clear Water Bay, Kowloon, Hong Kong : The Hong Kong University of Science and Technology,2015.—URL:http://lbezo ne.ust.hk/bib/b1514648 (date accessed: 02.05.2022).—Text: electronic.

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