Predicting Financial Market Trends using Time Series Analysis and Natural Language Processing
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Huina Mao & Scott Counts & Johan Bollen, 2011. "Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data," Papers 1112.1051, arXiv.org.
- Costola, Michele & Hinz, Oliver & Nofer, Michael & Pelizzon, Loriana, 2023.
"Machine learning sentiment analysis, COVID-19 news and stock market reactions,"
Research in International Business and Finance, Elsevier, vol. 64(C).
- Costola, Michele & Nofer, Michael & Hinz, Oliver & Pelizzon, Loriana, 2020. "Machine learning sentiment analysis, Covid-19 news and stock market reactions," SAFE Working Paper Series 288, Leibniz Institute for Financial Research SAFE.
- Xingyu Zhou & Zhisong Pan & Guyu Hu & Siqi Tang & Cheng Zhao, 2018. "Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, April.
- Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Bianconi, Marcelo & Hua, Xiaxin & Tan, Chih Ming, 2015.
"Determinants of systemic risk and information dissemination,"
International Review of Economics & Finance, Elsevier, vol. 38(C), pages 352-368.
- Marcelo Bianconi & Xiaxin Hua & Chih Ming Tan, 2013. "Determinants of Systemic Risk and Information Dissemination," Working Paper series 67_13, Rimini Centre for Economic Analysis.
- Marcelo Bianconi & Xiaxin Hua & Chih Ming Tan, 2013. "Determinants of Systemic Risk and Information Dissemination," Discussion Papers Series, Department of Economics, Tufts University 0776, Department of Economics, Tufts University.
- Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
- Ahelegbey, Daniel Felix & Cerchiello, Paola & Scaramozzino, Roberta, 2022.
"Network based evidence of the financial impact of Covid-19 pandemic,"
International Review of Financial Analysis, Elsevier, vol. 81(C).
- Daniel Felix Ahelegbey & Paola Cerchiello & Roberta Scaramozzino, 2021. "Network Based Evidence of the Financial Impact of Covid-19 Pandemic," DEM Working Papers Series 198, University of Pavia, Department of Economics and Management.
- Eric. W. K. See-To & Yang Yang, 2017. "Market sentiment dispersion and its effects on stock return and volatility," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 283-296, August.
- Sushant Chari & Purva Hegde Desai & Nilesh Borde & Babu George, 2023. "Aggregate News Sentiment and Stock Market Returns in India," JRFM, MDPI, vol. 16(8), pages 1-18, August.
- Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2014. "Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics," Papers 1412.3948, arXiv.org, revised Dec 2015.
- Al-Nasseri, Alya & Menla Ali, Faek, 2018. "What does investors' online divergence of opinion tell us about stock returns and trading volume?," Journal of Business Research, Elsevier, vol. 86(C), pages 166-178.
- Jaroslav Bukovina, 2015. "Sentiment of a society and large-cap stock liquidity," MENDELU Working Papers in Business and Economics 2015-56, Mendel University in Brno, Faculty of Business and Economics.
- Felix Ming Fai Wong & Zhenming Liu & Mung Chiang, 2014. "Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization," Papers 1406.7330, arXiv.org.
- Zhang, Xiaotao & Li, Guoran & Li, Yishuo & Zou, Gaofeng & Wu, Ji George, 2023. "Which is more important in stock market forecasting: Attention or sentiment?," International Review of Financial Analysis, Elsevier, vol. 89(C).
- Abdollahi, Hooman & Fjesme, Sturla L. & Sirnes, Espen, 2024. "Measuring market volatility connectedness to media sentiment," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).
- Yousra Trichilli & Mouna Abdelhédi & Mouna Boujelbène Abbes, 2020. "The thermal optimal path model: Does Google search queries help to predict dynamic relationship between investor’s sentiment and indexes returns?," Journal of Asset Management, Palgrave Macmillan, vol. 21(3), pages 261-279, May.
- Lyócsa, Štefan & Halousková, Martina & Haugom, Erik, 2023. "The US banking crisis in 2023: Intraday attention and price variation of banks at risk," Finance Research Letters, Elsevier, vol. 57(C).
- Tushar Rao & Saket Srivastava, 2012. "Modeling Movements in Oil, Gold, Forex and Market Indices using Search Volume Index and Twitter Sentiments," Papers 1212.1037, arXiv.org.
- Wasim ul Rehman & Omur Saltik & Faryal Jalil & Suleyman Degirmen, 2024. "Viral decisions: unmasking the impact of COVID-19 info and behavioral quirks on investment choices," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-20, December.
- Justina Deveikyte & Helyette Geman & Carlo Piccari & Alessandro Provetti, 2020. "A Sentiment Analysis Approach to the Prediction of Market Volatility," Papers 2012.05906, arXiv.org.
- Arezoo Hatefi Ghahfarrokhi & Mehrnoush Shamsfard, 2019. "Tehran Stock Exchange Prediction Using Sentiment Analysis of Online Textual Opinions," Papers 1909.03792, arXiv.org, revised Sep 2019.
- Shen, Shulin & Xia, Le & Shuai, Yulin & Gao, Da, 2022. "Measuring news media sentiment using big data for Chinese stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 74(C).
- Andrew Todd & James Bowden & Yashar Moshfeghi, 2024. "Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(1), March.
- Jung, Sang Hoon & Jeong, Yong Jin, 2021. "Examining stock markets and societal mood using Internet memes," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
More about this item
NEP fields
This paper has been announced in the following NEP Reports:- NEP-AIN-2023-10-02 (Artificial Intelligence)
- NEP-BIG-2023-10-02 (Big Data)
- NEP-CMP-2023-10-02 (Computational Economics)
- NEP-FMK-2023-10-02 (Financial Markets)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2309.00136. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.