Stock Price Trends Prediction Based on the Classical Models with Key Information Fusion of Ontologies
Article No.: 154, Pages 1 - 22
Abstract
An ontology of the financial field can support effective association and integration of financial knowledge. Based on behavioral finance, social media is increasingly applied as one of the data sources for information fusion in stock forecasting to approximate the patterns of market changes. By predicting Tesla (TSLA) stock price trends, this study finds that satisfactory forecasting results can be achieved using classical models and incorporating key information features from the technical indicator ontology class and the investor behavior ontology class, even in the face of the impact of the COVID-19 epidemic. In the post-epidemic period, the back propagation neural network (BPNN) model is used to predict the price trend of TSLA for the next five trading days with an accuracy of up to 91.34%, an F1 score of 0.91, and a return of up to 268.42% obtained from simulated trading. This study extends the research on stock forecasting using fused information in the ontology of the financial field, providing a new basis for general investors in the selection of fusion information and the application of trading strategies and providing effective support for organizations to make intelligent financial decisions under uncertainty.
References
[1]
Laura Alfaro, Anusha Chari, Andrew N. Greenland, and Peter K. Schott. 2020. Aggregate and firm-level stock returns during Pandemics, in real time. NBER Working Papers 26950. National Bureau of Economic Research, Inc. Retrieved from https://ideas.repec.org/p/nbr/nberwo/26950.html.
[2]
Badar Nadeem Ashraf. 2020. Stock markets’ reaction to COVID-19: Cases or fatalities? Res. Int. Busin. Finan. 54 (2020), 101249. DOI:
[3]
Scott Baker, Nicholas Bloom, Steven Davis, Kyle J. Kost, Marco C. Sammon, and Tasaneeya Viratyosin. 2020. The unprecedented stock market impact of COVID-19. NBER Working Papers 26945. National Bureau of Economic Research, Inc. Retrieved from https://econpapers.repec.org/RePEc:nbr:nberwo:26945.
[4]
Johan Bollen, Huina Mao, and Xiaojun Zeng. 2011. Twitter mood predicts the stock market. J. Computat. Sci. 2, 1 (2011), 1–8. DOI:
[5]
O. Bustos and A. Pomares-Quimbaya. 2020. Stock market movement forecast: A systematic review. Expert Syst. Applic. 156 (2020), 1–15. DOI:
[6]
Federico Castanedo. 2013. A review of data fusion techniques. Scient. World J. 2013 (2013), 19. DOI:
[7]
C. H. Chen, P. Y. Chen, and J. C. W. Lin. 2022. An ensemble classifier for stock trend prediction using sentence-level Chinese news sentiment and technical indicators. Int. J. Interact. Multim. Artif. Intell. 7, 3 (2022), 53–64. DOI:
[8]
Zhi Da, Joseph Engelberg, and Pengjie Gao. 2015. The sum of all FEARS investor sentiment and asset prices. Rev. Finan. Stud. 28, 1 (2015), 1–32. DOI:
[9]
S. Dhall, A. D. Dwivedi, S. K. Pal, and G Srivastava. 2022. Blockchain-based framework for reducing fake or vicious news spread on social media/messaging platforms. ACM Trans. Asian Low-resour. Lang. Inf. Process. 21, 1 (2022). DOI:
[10]
R. J. Dolan. 2002. Emotion, cognition, and behavior. Science 298, 5596 (2002), 1191–4. DOI:
[11]
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. 1986. Learning representations by back-propagating errors. Nature 323 (1986), 533–536.
[12]
Ronen Feldman. 2013. Techniques and applications for sentiment analysis. Commun. ACM 56, 4 (2013), 82–89. DOI:
[13]
Dana Rose Garfin, Roxane Cohen Silver, and E. Alison Holman. 2020. The novel coronavirus (COVID-2019) outbreak: Amplification of public health consequences by media exposure. Health Psychol. 39, 5 (2020), 355–357. DOI:
[14]
Jianping Gou, Liyuan Sun, Baosheng Yu, Shaohua Wan, Weihua Ou, and Zhang Yi. 2022. Multi-level attention-based sample correlations for knowledge distillation. IEEE Trans. Industr. Inform. (2022), 1–11. DOI:
[15]
Jianping Gou, Liyuan Sun, Baosheng Yu, Shaohua Wan, and Dacheng Tao. 2022. Hierarchical multi-attention transfer for knowledge distillation. ACM Trans. Multim. Comput. Commun. Appl. (2022). DOI:
[16]
U. Gupta, V. Bhattacharjee, and P. S. Bishnu. 2022. StockNet-GRU based stock index prediction. Expert Syst. Applic. 207 (2022), 117986. DOI:
[17]
Bruno Miranda Henrique, Vinicius Amorim Sobreiro, and Herbert Kimura. 2019. Literature review: Machine learning techniques applied to financial market prediction. Expert Syst. Applic. 124 (2019), 226–251. DOI:
[18]
Calliope Holingue, Elena Badillo-Goicoechea, Kira E. Riehm, Cindy B. Veldhuis, Johannes Thrul, Renee M. Johnson, M. Daniele Fallin, Frauke Kreuter, Elizabeth A. Stuart, and Luther G. Kalb. 2020. Mental distress during the COVID-19 pandemic among US adults without a pre-existing mental health condition: Findings from American trend panel survey. Prevent. Med. 139 (2020). DOI:
[19]
Mikolaj Kamiński, Cyntia Szymańska, and Jan Krzysztof Nowak. 2021. Whose tweets on COVID-19 gain the most attention: Celebrities, political, or scientific authorities? Cyberpsychology Behavior and Social Networking 24, 2 (2021), 123–128. DOI:
[20]
Qing Li, LiLing Jiang, and Ping Li. 2015. Tensor-based learning for predicting stock movements. In 29th AAAI Conference on Artificial Intelligence. 1784–1790.
[21]
Qing Li, TieJun Wang, Ping Li, Ling Liu, Qixu Gong, and Yuanzhu Chen. 2014. The effect of news and public mood on stock movements. Inf. Sci. 278 (2014), 826–840. DOI:
[22]
Jiawei Long, Zhaopeng Chen, Weibing He, Taiyu Wu, and Jiangtao Ren. 2020. An integrated framework of deep learning and knowledge graph for prediction of stock price trend: An application in Chinese stock exchange market. Appl. Soft Comput. 91 (2020), 106205. DOI:.
[23]
Tim Loughran and Bill McDonald. 2011. When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Finan. 66, 1 (2011), 35–65. DOI:
[24]
C. K. Malhotra and Arvind Malhotra. 2016. How CEOs can leverage Twitter. MIT Sloan Manag. Rev. 57, 2 (2016), 73–79.
[25]
John R. Nofsinger. 2005. Social mood and financial economics. J. Behav. Finan. 6, 3 (2005), 144–160. DOI:
[26]
Isaac Kofi Nti, Adebayo Felix Adekoya, and Benjamin Asubam Weyori. 2020. A systematic review of fundamental and technical analysis of stock market predictions. Artif. Intell. Rev. 53, 4 (2020), 3007–3057. DOI:
[27]
Andrea Picasso, Simone Merello, Yukun Ma, Luca Oneto, and Erik Cambria. 2019. Technical analysis and sentiment embeddings for market trend prediction. Expert Syst. Applic. 135 (2019), 60–70. DOI:
[28]
Andrea Picasso, Simone Merello, Luca Oneto, Yukun Ma, Lorenzo Malandri, and Erik Cambria. 2018. Ensemble of technical analysis and machine learning for market trend prediction. In IEEE Symposium Series on Computational Intelligence (SSCI’18). 2090–2096. DOI:
[29]
Shaohua Qiang, Yunlu Luo, Yupeng Li, and Peng Wu. 2019. Ontology reasoning for financial affairs with RBR and CBR. Data Anal. Knowl. Discov. 3, 8 (2019), 94–104. DOI:
[30]
Alessandro Rovetta and Akshaya Srikanth Bhagavathula. 2020. Global infodemiology of COVID-19: Analysis of Google web searches and Instagram Hashtags. J. Med. Internet Res. 22, 8 (2020), e20673. DOI:
[31]
Khurram Shehzad, Liu Xiaoxing, and Hayfa Kazouz. 2020. COVID-19’s disasters are perilous than global financial crisis: A rumor or fact? Finan. Res. Lett. 36 (2020), 101669. DOI:
[32]
Li-Yen Shue, Ching-Wen Chen, and Weissor Shiue. 2009. The development of an ontology-based expert system for corporate financial rating. Expert Syst. Applic. 36, 2, 1 (2009), 2130–2142. DOI:
[33]
2020. State of social media report: The impact of COVID-19. Technical Report. Socialbakers.
[34]
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1 (2014), 1929–1958. DOI:
[35]
Nadine Strauss and Christopher Holmes Smith. 2019. Buying on rumors: How financial news flows affect the share price of Tesla. Corpor. Commun. 24, 4 (2019), 593–607. DOI:
[36]
Serena Tardelli, Marco Avvenuti, Maurizio Tesconi, and Stefano Cresci. 2022. Detecting inorganic financial campaigns on Twitter. Inf. Syst. 103 (2022), 101769. DOI:
[37]
Paul C. Tetlock. 2007. Giving content to investor sentiment: The role of media in the stock market. J. Finan. 62, 3 (2007), 1139–1168. DOI:
[38]
Ankit Thakkar and Kinjal Chaudhari. 2021. Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions. Inf. Fusion 65 (2021), 95–107. DOI:
[39]
Chih-Fong Tsai and Yu-Chieh Hsiao. 2010. Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches. Decis. Supp. Syst. 50, 1 (2010), 258–269. DOI:
[40]
Vladimir Naumovich Vapnik. 1998. Statistical learning theory. Wiley, New York (1998), 768 pages.
[41]
Huiwen Wang, Shan Lu, and Jichang Zhao. 2019. Aggregating multiple types of complex data in stock market prediction: A model-independent framework. Knowl.-based Syst. 164 (2019), 193–204. DOI:
[42]
Shanshan Wang, Zhang Zhe, Ye Kang, Huaiqing Wang, and Xiajian Chen. 2008. An ontology for causal relationships between news and financial instruments. Expert Syst. Applic. 35, 3 (2008), 569–580. DOI:
[43]
Xiaoguang Wang, Hongyu Wang, Wanli Chang, Chen Zhang, and Lei Xu. 2020. Construction of smart data toward Dunhuang grottoes. Libr Trends 69 (2020), 177–200. DOI:
[44]
Bin Weng, Mohamed A. Ahmed, and Fadel M. Megahed. 2017. Stock market one-day ahead movement prediction using disparate data sources. Expert Syst. Applic. 79 (2017), 153–163. DOI:
[45]
WHO. 2020. Novel Coronavirus(2019-nCoV) Situation Report - 13. Retrieved from https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200202-sitrep-13-ncov-v3.pdf?sfvrsn=195f4010_6.
[46]
Shenyong Xiao, Han Yu, Yanan Wu, Zijun Peng, and Yin Zhang. 2018. Self-evolving trading strategy integrating Internet of Things and big data. IEEE Internet Things J. 5, 4, SI (2018), 2518–2525. DOI:
[47]
Jian Xu and Yefu Qian. 2020. The definition, transmission and governance of infodemic. J. SJTU (Philos. Soc. Sci.) 28, 5 (2020), 121–134. DOI:
[48]
Yong Yu, Xiaosheng Si, Changhua Hu, and Jianxun Zhang. 2019. A review of recurrent neural networks: LSTM cells and network architectures. Neural Computat. 31 (2019), 1–36. DOI:
[49]
Kun Yuan, Guannan Liu, Junjie Wu, and Hui Xiong. 2020. Dancing with Trump in the stock market: A deep information echoing model. ACM Trans. Intell. Syst. Technol. 11, 5 (2020), 62:1–22. DOI:
[50]
Dayong Zhang, Min Hu, and Qiang Ji. 2020. Financial markets under the global pandemic of COVID-19. Finan. Res. Lett. 36 (2020), 101528. DOI:
[51]
Lu Zhang, Xiaopeng Fan, and Chengzhong Xu. 2017. A fusion financial prediction strategy based on RNN and representative pattern discovery. In 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT’17). 92–97. DOI:
[52]
Qun Zhang, Lijun Yang, and Feng Zhou. 2021. Attention enhanced long short-term memory network with multi-source heterogeneous information fusion: An application to BGI Genomics. Information Sciences 553 (2021), 305–330. DOI:
[53]
Zhongbao Zhou, Meng Gao, Qing Liu, and Helu Xiao. 2020. Forecasting stock price movements with multiple data sources: Evidence from stock market in China. Phys. A-Statist. Mech. Applic. 542 (2020), 1–15. DOI:
Index Terms
- Stock Price Trends Prediction Based on the Classical Models with Key Information Fusion of Ontologies
Recommendations
Deep Learning-based Integrated Framework for stock price movement prediction
AbstractStock market prediction is a very important problem in the economics field. With the development of machine learning, more and more algorithms are applied in the stock market to predict the stock price movement. However, stock market ...
Highlights- The combination of public opinions and sentiments, SA-DLSTM can provide robust and accurate predictions for the stock market trends.
Multi-source aggregated classification for stock price movement prediction
AbstractPredicting stock price movements is a challenging task. Previous studies mostly used numerical features and news sentiments of target stocks to predict stock price movements. However, their semantics-based sentiment analysis is sub-...
Highlights- We incorporate news about related companies for stock price movement forecasting.
Stock Price Trend Prediction using Emotion Analysis of Financial Headlines with Distilled LLM Model
PETRA '24: Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive EnvironmentsCapturing the volatility of stock prices helps individual traders, stock analysts, and institutions alike increase their returns in the stock market. Financial news headlines have been shown to have a significant effect on stock price mobility. Lately, ...
Comments
Information & Contributors
Information
Published In
May 2023
653 pages
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 May 2023
Online AM: 13 April 2023
Accepted: 30 March 2023
Revised: 17 February 2023
Received: 07 April 2022
Published in TALLIP Volume 22, Issue 5
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- Innovation and Talent Base for Digital Technology and Finance
- Fundamental Research Funds for the Central Universities
- Talent Training Program for Graduate Students
- Zhongnan University of Economics and Law for their financial support
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 180Total Downloads
- Downloads (Last 12 months)123
- Downloads (Last 6 weeks)12
Reflects downloads up to 02 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in