Open Access
SHS Web Conf.
Volume 194, 2024
The 6th ETLTC International Conference on ICT Integration in Technical Education (ETLTC2024)
Article Number 01003
Number of page(s) 23
Section Intelligent Applications in Society
Published online 26 June 2024
  1. Kamble, Rupesh A. “Short and long term stock trend prediction using decision tree.” 2017 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2017. [Google Scholar]
  2. Nugroho, FX Satriyo D., Teguh Bharata Adji, and Silmi Fauziati. “Decision support system for stock trading using multiple indicators decision tree.” 2014 The 1st International Conference on Information Technology, Computer, and Electrical Engineering. IEEE, 2014. [Google Scholar]
  3. Vu, Tien Thanh, et al. “An experiment in integrating sentiment features for tech stock prediction in twitter.” Proceedings of the workshop on information extraction and entity analytics on social media data. 2012. [Google Scholar]
  4. Xie, Boyi, et al. “Semantic frames to predict stock price movement.” Proceedings of the 51st annual meeting of the association for computational linguistics. 2013. [Google Scholar]
  5. Raj, Nimesh. “Prediction of Stock Market Using LSTM-RNN Model.” 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2023. [Google Scholar]
  6. Bao, Wei, Jun Yue, and Yulei Rao. “A deep learning framework for financial time series using stacked autoencoders and long-short term memory.” PloS one 12.7 (2017): e0180944. [CrossRef] [Google Scholar]
  7. Selvin, Sreelekshmy, et al. “Stock price prediction using LSTM, RNN and CNN-sliding window model.” 2017 international conference on advances in computing, communications and informatics (icacci). IEEE, 2017. [Google Scholar]
  8. Gao, Ya, Rong Wang, and Enmin Zhou. “Stock prediction based on optimized LSTM and GRU models.” Scientific Programming 2021 (2021): 1–8. [Google Scholar]
  9. Althelaya, Khaled A., El-Sayed M. El-Alfy, and Salahadin Mohammed. “Stock market forecast using multivariate analysis with bidirectional and stacked (LSTM, GRU).” 2018 21st Saudi Computer Society National Computer Conference (NCC). IEEE, 2018. [Google Scholar]
  10. Matsunaga, Daiki, Toyotaro Suzumura, and Toshihiro Takahashi. “Exploring graph neural networks for stock market predictions with rolling window analysis.” arXiv preprint arXiv:1909.10660 (2019). [Google Scholar]
  11. Wang, Jianian, et al. “A review on graph neural network methods in financial applications.” arXiv preprint arXiv:2111.15367 (2021). [Google Scholar]
  12. Hoseinzade, Ehsan, and Saman Haratizadeh. “CNNpred: CNN-based stock market prediction using a diverse set of variables.” Expert Systems with Applications 129 (2019): 273–285. [CrossRef] [Google Scholar]
  13. Pandey, Sakshi. “Forecasting of the Stock Market Price Using LSTM-CNN Model with Various Representations of Collected Dataset.” 2023 4th International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2023. [Google Scholar]
  14. Chan, Wesley S. “Stock price reaction to news and no-news: drift and reversal after headlines.” Journal of financial economics 70.2 (2003): 223–260. [CrossRef] [Google Scholar]
  15. Schumaker, Robert P., and Hsinchun Chen. “Textual analysis of stock market prediction using breaking financial news: The AZFin text system.” ACM Transactions on Information Systems (TOIS) 27.2 (2009): 1–19. [CrossRef] [Google Scholar]
  16. Ashtiani, Matin N., and Bijan Raahemi. “News-based intelligent prediction of financial markets using text mining and machine learning: A systematic literature review.” Expert Systems with Applications 217 (2023): 119509. [CrossRef] [Google Scholar]
  17. Avramelou, Loukia, et al. “Deep reinforcement learning for financial trading using multi-modal features.” Expert Systems with Applications 238 (2024): 121849. [CrossRef] [Google Scholar]
  18. George S Atsalakis and Kimon P Valavanis. 2009. Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert systems with applications 36, 3 (2009), 5932–5941. [CrossRef] [Google Scholar]
  19. Yuhong Li and Weihua Ma. 2010. Applications of ANN in financial economics: a survey. In 2010 International symposium on computational intelligence and design, Vol. 1. IEEE, 211–214 [Google Scholar]
  20. Michel Ballings, Dirk Van den Poel, Nathalie Hespeels, and Ruben Gryp. 2015. Evaluating multiple classifiers for stock price direction prediction. Expert systems with Applications 42, 20 (2015), 7046–7056 [CrossRef] [Google Scholar]
  21. Michal Tkácˇ and Robert Verner. 2016. ANN in business: Two decades of research. Applied Soft Computing 38 (2016), 788–804 [CrossRef] [Google Scholar]
  22. Rodolfo C Cavalcante, Rodrigo C Brasileiro, Victor LF Souza, Jarley P Nobrega, and Adriano LI Oliveira. 2016. Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55 (2016), 194–211. [CrossRef] [Google Scholar]
  23. Qing Li, Yan Chen, Jun Wang, Yuanzhu Chen, and Hsinchun Chen. 2017. Web media and stock markets: A survey and future directions from a big data perspective. IEEE Transactions on Knowledge and Data Engineering 30, 2 (2017), 381–399 [Google Scholar]
  24. Frank Z Xing, Erik Cambria, and Roy E Welsch. 2018. Natural language based financial forecasting: a survey. Artificial Intelligence Review 50, 1 (2018), 49–73. [CrossRef] [Google Scholar]
  25. Isaac Kofi Nti, Adebayo Felix Adekoya, and Benjamin Asubam Weyori. 2020. A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review 53, 4 (2020), 3007–3057 [CrossRef] [Google Scholar]
  26. Deniz Ersan, Chifumi Nishioka, and Ansgar Scherp. 2020. Comparison of machine learning methods for financial time series forecasting at the examples of over 10 years of daily and hourly data of DAX 30 and S&P 500. Journal of Computational Social Science 3, 1 (2020), 103–133. [CrossRef] [Google Scholar]
  27. Weiwei Jiang. 2021. Applications of deep learning in stock market prediction: recent progress. Expert Systems with Applications 184 (2021), 115537. [CrossRef] [Google Scholar]
  28. Ankit Thakkar and Kinjal Chaudhari. 2021. A comprehensive survey on deep neural networks for the stock market: the need, challenges, and future directions. Expert Systems with Applications 177 (2021), 114800. [CrossRef] [Google Scholar]
  29. Mahinda Mailagaha Kumbure, Christoph Lohrmann, Pasi Luukka, and Jari Porras. 2022. Machine learning techniques and data for stock market forecasting: a literature review. Expert Systems with Applications (2022), 116659. [CrossRef] [Google Scholar]
  30. Zou, Jinan, et al. “Stock Market Prediction via Deep Learning Techniques: A Survey.” arXiv preprint arXiv:2212.12717 (2022). [Google Scholar]
  31. Abraham A., Philip N.S., Nath B. and Saratchandran P, Performance Analysis of Connectionist Paradigms for Modeling Chaotic Behavior of Stock Indices, Second International Workshop on Intelligent Systems Design and Applications, Computational Intelligence and Applications, Dynamic Publishers Inc., USA, pp. 181–186, 2002. [Google Scholar]
  32. Chen, Yingjun, and Yongtao Hao. “A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction.” Expert Systems with Applications 80 (2017): 340–355. [CrossRef] [Google Scholar]
  33. Chen, Yingjun, and Yongtao Hao. “A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction.” Expert Systems with Applications 80 (2017): 340–355. [CrossRef] [Google Scholar]
  34. Shen, Shunrong, Haomiao Jiang, and Tongda Zhang. “Stock market forecasting using machine learning algorithms.” Department of Electrical Engineering, Stanford University, Stanford, CA (2012): 1–5. [Google Scholar]
  35. Qian, Bo, and Khaled Rasheed. “Stock market prediction with multiple classifiers.” Applied Intelligence 26 (2007): 25–33. [CrossRef] [Google Scholar]
  36. Yoshihara, Akira, et al. “Predicting stock market trends by recurrent deep neural networks.” PRICAI 2014: Trends in Artificial Intelligence: 13th Pacific Rim International Conference on Artificial Intelligence, Gold Coast, QLD, Australia, December 1-5, 2014. Proceedings 13. Springer International Publishing, 2014. [Google Scholar]
  37. Gandhmal, Dattatray P., and K. Kumar. “Systematic analysis and review of stock market prediction techniques.” Computer Science Review 34 (2019): 100190. [CrossRef] [Google Scholar]
  38. Althelaya, Khaled A., El-Sayed M. El-Alfy, and Salahadin Mohammed. “Stock market forecast using multivariate analysis with bidirectional and stacked (LSTM, GRU).” 2018 21st Saudi Computer Society National Computer Conference (NCC). IEEE, 2018. [Google Scholar]
  39. Sethia, Akhil, and Purva Raut. “Application of LSTM, GRU and ICA for stock price prediction.” Information and Communication Technology for Intelligent Systems: Proceedings of ICTIS 2018, Volume 2. Springer Singapore, 2019. [Google Scholar]
  40. Rather, Akhter Mohiuddin, V. N. Sastry, and Arun Agarwal. “Stock market prediction and Portfolio selection models: a survey.” Opsearch 54 (2017): 558–579. [CrossRef] [Google Scholar]
  41. Zhang, Liheng, Charu Aggarwal, and Guo-Jun Qi. “Stock price prediction via discovering multi-frequency trading patterns.” Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. 2017. [Google Scholar]
  42. Nelson, David MQ, Adriano CM Pereira, and Renato A. De Oliveira. “Stock market’s price movement prediction with LSTM neural networks.” 2017 International joint conference on neural networks (IJCNN). Ieee, 2017. [Google Scholar]
  43. Fuli Feng, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, and Tat-Seng Chua. 2018. Enhancing stock movement prediction with adversarial training. arXiv preprint arXiv:1810.09936 (2018). [Google Scholar]
  44. Guangyu Ding and Liangxi Qin. 2020. Study on the prediction of stock price based on the associated network model of LSTM. International Journal of Machine Learning and Cybernetics 11, 6 (2020), 1307–1317. [CrossRef] [Google Scholar]
  45. Ehsan Hoseinzade, Saman Haratizadeh, and Arash Khoeini. 2019. U-cnnpred: A universal cnn-based predictor for stock markets. arXiv preprint arXiv:1911.12540 (2019). [Google Scholar]
  46. Wenjie Lu, Jiazheng Li, Yifan Li, Aijun Sun, and Jingyang Wang. 2020. A CNN-LSTM-based model to forecast stock prices. Complexity 2020 (2020) [Google Scholar]
  47. Scarselli, Franco, et al. “The graph neural network model.” IEEE transactions on neural networks 20.1 (2008): 61–80. [Google Scholar]
  48. Daiki Matsunaga, Toyotaro Suzumura, and Toshihiro Takahashi. 2019. Exploring graph neural networks for stock market predictions with rolling window analysis. arXiv preprint arXiv:1909.10660 (2019) [Google Scholar]
  49. Cong Xu, Huiling Huang, Xiaoting Ying, Jianliang Gao, Zhao Li, Peng Zhang, Jie Xiao, Jiarun Zhang, and Jiangjian Luo. 2022. HGNN: Hierarchical Graph Neural Network for Predicting the Classification of Price-Limit-Hitting Stocks. Information Sciences (2022). [Google Scholar]
  50. DJimmy Ming-Tai Wu, Zhongcui Li, Norbert Herencsar, Bay Vo, and Jerry Chun-Wei Lin. 2021. A graph-based CNN-LSTM stock price prediction algorithm with leading indicators. Multimedia Systems (2021), 1–20. [Google Scholar]
  51. Yingmei Chen, Zhongyu Wei, and Xuanjing Huang. 2018. Incorporating corporation relationship via graph convolutional neural networks for stock price prediction. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 1655–1658 [Google Scholar]
  52. Changhai Wang, Hui Liang, Bo Wang, Xiaoxu Cui, and Yuwei Xu. 2022. MG-Conv: A spatiotemporal multi-graph convolutional neural network for stock market index trend prediction. Computers and Electrical Engineering 103 (2022), 108285. [CrossRef] [Google Scholar]
  53. Raehyun Kim, Chan Ho So, Minbyul Jeong, Sanghoon Lee, Jinkyu Kim, and Jaewoo Kang. 2019. Hats: A hierarchical graph attention network for stock movement prediction. arXiv preprint arXiv:1908.07999 (2019) [Google Scholar]
  54. Ryo Akita, Akira Yoshihara, Takashi Matsubara, and Kuniaki Uehara. 2016. Deep learning for stock prediction using numerical and textual information. In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS). IEEE, 1–6. [Google Scholar]
  55. Ye Ma, Lu Zong, Yikang Yang, and Jionglong Su. 2019. News2vec: News network embedding with subnode information. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 4843–4852. [Google Scholar]
  56. Deli Chen, Yanyan Zou, Keiko Harimoto, Ruihan Bao, Xuancheng Ren, and Xu Sun. 2019. Incorporating fine-grained events in stock movement prediction. arXiv preprint arXiv:1910.05078 (2019). [Google Scholar]
  57. Ramos-Pérez, Eduardo, Pablo J. Alonso-González, and José Javier Núñez-Velázquez. “Multi-transformer: A new neural network-based architecture for forecasting S&P volatility.” Mathematics 9.15 (2021): 1794. [CrossRef] [Google Scholar]
  58. Ding, Qianggang, et al. “Hierarchical Multi-Scale Gaussian Transformer for Stock Movement Prediction.” IJCAI. 2020. [Google Scholar]
  59. Dong, Yingzhe, et al. “Belt: A pipeline for stock price prediction using news.” 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. [Google Scholar]
  60. Sonkiya, Priyank, Vikas Bajpai, and Anukriti Bansal. “Stock price prediction using BERT and GAN.” arXiv preprint arXiv:2107.09055 (2021) [Google Scholar]
  61. Sawhney, Ramit, et al. “Deep attentive learning for stock movement prediction from social media text and company correlations.” Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020. [Google Scholar]
  62. Cheng, Rui, and Qing Li. “Modeling the momentum spillover effect for stock prediction via attribute-driven graph attention networks.” Proceedings of the AAAI conference on artificial intelligence. Vol. 35. No. 1. 2021. [Google Scholar]
  63. Wang, Heyuan, Tengjiao Wang, and Yi Li. “Incorporating expert-based investment opinion signals in stock prediction: A deep learning framework.” Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 01. 2020. [Google Scholar]
  64. Li, Qing, et al. “A multimodal event-driven lstm model for stock prediction using online news.” IEEE Transactions on Knowledge and Data Engineering 33.10 (2020): 3323–3337. [Google Scholar]
  65. He, Shwai, and Shi Gu. “Multi-modal Attention Network for Stock Movements Prediction.” arXiv preprint arXiv:2112.13593 (2021). [Google Scholar]
  66. Yang Li and Yi Pan. 2022. A novel ensemble deep learning model for stock prediction based on stock prices and news. International Journal of Data Science and Analytics 13, 2 (2022), 139–149 [CrossRef] [Google Scholar]
  67. Shumin, et al. “Knowledge-driven stock trend prediction and explanation via temporal convolutional network.” Companion Proceedings of The 2019 World Wide Web Conference. 2019. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.