Learning-Based Stock Trending Prediction by Incorporating Technical Indicators and Social Media Sentiment

Bollen J, Mao H, Zeng X. Twitter mood predicts the stock market. J Comput Sci. 2011;2(1):1–8.

Article  Google Scholar 

Patel J, Shah S, Thakkar P, Kotecha K. Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Syst Appl. 2015;42(1):259–68.

Article  Google Scholar 

Ma Y, Mao R, Lin Q, Wu P, Cambria E. Multi-source aggregated classification for stock price movement prediction. Inf Fusion. 2023;91:515–28.

Article  Google Scholar 

Maini SS, Govinda K. Stock market prediction using data mining techniques. In: 2017 International Conference on Intelligent Sustainable Systems (ICISS) IEEE. 2017:654–61.

Varfis A, Versino C. Univariate economic time series forecasting by connectionist methods. In: 1990 International Conference on Neural Networks (ICNN) IEEE. 1990:342–5.

Rather AM, Agarwal A, Sastry V. Recurrent neural network and a hybrid model for prediction of stock returns. Expert Syst Appl. 2015;42(6):3234–41.

Article  Google Scholar 

Hafezi R, Shahrabi J, Hadavandi E. A bat-neural network multi-agent system (BNNMAS) for stock price prediction: case study of DAX stock price. Appl Soft Comput. 2015;29:196–210.

Article  Google Scholar 

Xiong L, Lu Y. Hybrid ARIMA-BPNN model for time series prediction of the Chinese stock market. In: 2017 3rd International Conference on Information Management (ICIM). IEEE; 2017. p. 93-7.

Lee SW, Um JY. Stock fluctuation prediction method and server. Google Patents; 2019. US Patent 10,185,996.

Kim KJ. Financial time series forecasting using support vector machines. Neurocomputing. 2003;55(1–2):307–19.

Bharathi S, Geetha A. Sentiment analysis for effective stock market prediction. Int J Intell Eng Syst. 2017;10(3):146–54.

Google Scholar 

Ichinose K, Shimada K. Stock market prediction using keywords from expert articles. In: International Conference on Soft Computing and Data Mining. Springer. 2018:409–17.

Zhang X, Zhang Y, Wang S, Yao Y, Fang B, Philip SY. Improving stock market prediction via heterogeneous information fusion. Knowl Based Syst. 2018;143:236–47.

Article  Google Scholar 

Si J, Mukherjee A, Liu B, Pan SJ, Li Q, Li H. Exploiting social relations and sentiment for stock prediction. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014:1139–45.

Wang Z, Ho S-B, Lin Z. Stock market prediction analysis by incorporating social and news opinion and sentiment. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW) IEEE. 2018:1375–80.

Nguyen TH, Shirai K, Velcin J. Sentiment analysis on social media for stock movement prediction. Expert Syst Appl. 2015;42(24):9603–11.

Article  Google Scholar 

Li B, Chan KC, Ou C, Ruifeng S. Discovering public sentiment in social media for predicting stock movement of publicly listed companies. Inform Syst. 2017;69:81–92.

Article  Google Scholar 

Hu H, Tang L, Zhang S, Wang H. Predicting the direction of stock markets using optimized neural networks with Google Trends. Neurocomputing. 2018;285:188–95.

Article  Google Scholar 

Hu Z. Crude oil price prediction using CEEMDAN and LSTM-attention with news sentiment index. Oil & Gas Science and Technology-Revue d’IFP Energies nouvelles. 2021;76:28.

Article  Google Scholar 

Malandri L, Xing FZ, Orsenigo C, Vercellis C, Cambria E. Public mood-driven asset allocation: the importance of financial sentiment in portfolio management. Cognit Comput. 2018;10(6):1167–76.

Article  Google Scholar 

Parray IR, Khurana SS, Kumar M, Altalbe AA. Time series data analysis of stock price movement using machine learning techniques. Soft Comput. 2020;24(21):16509–17.

Article  Google Scholar 

Dey PP, Nahar N, Hossain B. Forecasting stock market trend using machine learning algorithms with technical indicators. Int J Inform Technol Comput Sci. 2020;12(3):32–8.

Google Scholar 

Agrawal M, Shukla PK, Nair R, Nayyar A, Masud M. Stock prediction based on technical indicators using deep learning model. Comput Mater Continua. 2022;70(1):287–304.

Article  Google Scholar 

Li Y, Pan Y. A novel ensemble deep learning model for stock prediction based on stock prices and news. Int J Data Sci Anal. 2022;13(2):139–49.

Article  Google Scholar 

Picasso A, Merello S, Ma Y, Oneto L, Cambria E. Technical analysis and sentiment embeddings for market trend prediction. Expert Syst Appl. 2019;135:60–70.

Article  Google Scholar 

Fischer T, Krauss C. Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res. 2018;270(2):654–69.

Article  MathSciNet  MATH  Google Scholar 

Nelson DM, Pereira AC, de Oliveira RA, Stock market’s price movement prediction with LSTM neural networks. In,. International joint conference on neural networks (IJCNN). IEEE. 2017:1419–26.

Stoean C, Paja W, Stoean R, Sandita A. Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations. PloS one. 2019;14(10).

Article  Google Scholar 

Kim T, Kim HY. Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data. PloS one. 2019;14(2).

Article  Google Scholar 

Sezer OB, Ozbayoglu AM. Financial trading model with stock bar chart image time series with deep convolutional neural networks. Intell Autom Soft Comput. 2020;26(2):323–34.

Google Scholar 

Huang W, Nakamori Y, Wang SY. Forecasting stock market movement direction with support vector machine. Comput Oper Res. 2005;32(10):2513–22.

Article  MATH  Google Scholar 

Naeini MP, Taremian H, Hashemi HB, Stock market value prediction using neural networks. International conference on computer information systems and industrial management applications (CISIM). IEEE. 2010:132–6.

Huang TT, Chang CH. Intelligent stock selecting via Bayesian naive classifiers on the hybrid use of scientific and humane attributes. In: 2008 Eighth International Conference on Intelligent Systems Design and Applications. vol.1. IEEE. 2008:617–21.

Wang Z, Jiao R, Jiang H. Emotion recognition using WT-SVM in human-computer interaction. J New Media. 2020;2(3):121.

Article  Google Scholar 

Henrique BM, Sobreiro VA, Kimura H. Stock price prediction using support vector regression on daily and up to the minute prices. J Finance Data Sci. 2018;4(3):183–201.

Article  Google Scholar 

Marković I, Stojanović M, Stanković J, Stanković M. Stock market trend prediction using AHP and weighted kernel LS-SVM. Soft Comput. 2017;21(18):5387–98.

Article  Google Scholar 

Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T. Artificial neural network methods for the solution of second order boundary value problems. Comput Mater Continua. 2019;59(1):345–59.

Article  Google Scholar 

Göçken M, Özçalıcı M, Boru A, Dosdoğru AT. Integrating metaheuristics and artificial neural networks for improved stock price prediction. Expert Syst Appl. 2016;44:320–31.

Article  Google Scholar 

Zhu K, Zhang N, Ying S, Wang X. Within-project and cross-project software defect prediction based on improved transfer naive Bayes algorithm. Comput Mater Continua. 2020;63(2):891–910.

Google Scholar 

Khaidem L, Saha S, Dey SR. Predicting the direction of stock market prices using random forest. arXiv preprint http://arxiv.org/abs/1605.00003. 2016.

Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining 2016:785–94.

Wang Z, Chong CS, Lan L, Yang Y, Ho S-B, Tong JC, Fine-grained sentiment analysis of social media with emotion sensing. In Future Technologies Conference (FTC). IEEE. 2016:1361–4.

Wang Z, Ho S-B, Cambria E. A review of emotion sensing: categorization models and algorithms. Multimed Tools Appl. 2020;79:35553–82.

Article  Google Scholar 

Xing FZ, Cambria E, Welsch RE. Natural language based financial forecasting: a survey. Artif Intell Rev. 2018;50(1):49–73.

Article  Google Scholar 

Hu Z, Wang Z, Ho S-B, Tan A-H. Stock market trend forecasting based on multiple textual features: a deep learning method. In: 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE 2021:1002–7.

Xing FZ, Cambria E, Zhang Y. Sentiment-aware volatility forecasting. Knowledge-Based Syst. 2019;176:68–76.

Article  Google Scholar 

Merello S, Ratto AP, Oneto L, Cambria E. Ensemble application of transfer learning and sample weighting for stock market prediction. In: 2019 International Joint Conference on Neural Networks (IJCNN). IEEE. 2019:1–8.

Gupta R, Chen M. Sentiment analysis for stock price prediction. In: 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE. 2020:213–8.

Khan W, Malik U, Ghazanfar MA, Azam MA, Alyoubi KH, Alfakeeh AS. Predicting stock market trends using machine learning algorithms via public sentiment and political situation analysis. Soft Comput 2019:1–25.

Cambria E, Liu Q, Decherchi S, Xing F, Kwok K. SenticNet 7: a commonsense-based neurosymbolic AI framework for explainable sentiment analysis. In: LREC 2022: 3829–39.

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