Stock Price Prediction Leveraging Reddit: The Role of Trust Filter and Sliding Window
Abstract
The increasing attraction of high-risk investments for retail investors has fuelled speculation across social media platforms on stocks with a perceived likelihood for disproportionate returns. This paper investigates the capacity of one such forum’s (Reddit’s r/WallStreetBets) speculation and the prevailing literature on how value should be maximally extracted from a community’s insights. With comparative research focused on aggregating the sentiment of a community – we took an individualized approach – and sought to understand the role of forum member posts in stock price predictions. Utilizing a sentiment sliding window and trust filter enabled a 40% reduction in errors compared to not applying these techniques with the financial and Reddit data.
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Stock market's price movement prediction with LSTM neural networks
Nelson, D. M. Q., Pereira, A. C. M., & de Oliveira, R. A. (2017) · 2017 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966019 · Cited by 951 · see citing works
Stock market movement forecast: A Systematic review
Bustos, O., & Pomares, A. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113464 · Cited by 377 · see citing works
Stock market prediction using machine learning classifiers and social media, news
Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01839-w · Cited by 336 · see citing works
The impact of sentiment and attention measures on stock market volatility
Audrino, F., Sigrist, F., & Ballinari, D. (2019) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2019.05.010 · Cited by 314 · see citing works
Chou, J., & Nguyen, T.-K. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2794389 · Cited by 200 · see citing works
Financial news predicts stock market volatility better than close price
Atkins, A., Niranjan, M., & Gerding, E. (2018) · The Journal of Finance and Data Science · doi:10.1016/j.jfds.2018.02.002 · Cited by 166 · see citing works
Sentiment-aware stock market prediction: A deep learning method
Li, J., Bu, H., & Wu, J. (2017) · International Conference on Service Systems and Service Management · doi:10.1109/icsssm.2017.7996306 · Cited by 131 · see citing works
Short term stock price prediction using deep learning
Khare, K., Darekar, O., Gupta, P., & Attar, V. (2017) · IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology · doi:10.1109/rteict.2017.8256643 · Cited by 127 · see citing works
THE EFFICIENT MARKET HYPOTHESIS: A CRITICAL REVIEW OF LITERATURE AND METHODOLOGY
Degutis, A., & Novickytė, L. (2014) · Ekonomika · doi:10.15388/ekon.2014.2.3549 · Cited by 93 · see citing works
Applying long short term momory neural networks for predicting stock closing price
Gao, T., Chai, Y., & Liu, Y. (2017) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess.2017.8342981 · Cited by 72 · see citing works
The impact of media coverage on investor trading behavior and stock returns
Wu, C., & Lin, C. (2017) · Pacific-Basin Finance Journal · doi:10.1016/j.pacfin.2017.04.001 · Cited by 63 · see citing works
Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis
Liu, Y., Qin, Z., Li, P., & Wan, T. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-60042-0_22 · Cited by 48 · see citing works
Application of the Artifical Neural Network in Predicting the Direction of Stock Market Index
Qiu, M., Cheng, L., & Yu, S. (2016) · ... International Conference on Complex, Intelligent and Software Intensive Systems · doi:10.1109/cisis.2016.115 · Cited by 36 · see citing works
Nabi, R. M., Saeed, S. A. M., & Harron, H. (2020) · Kurdistan Journal of Applied Research · doi:10.24017/science.2020.1.3 · Cited by 24 · see citing works
The Impact of Sentiment and Attention Measures on Stock Market VolatilityPreprint
Audrino, F., Sigrist, F., & Ballinari, D. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3188941 · Cited by 9 · see citing works
Target Price Optimism, Investor Sentiment, and the Informativeness of Target PricesPreprint
Buxbaum, M., Schultze, W., & Tiras, S. L. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3398387 · Cited by 3 · see citing works
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Versatile time-window sliding machine learning techniques for stock market forecasting
Zhan, Z., & Kim, S.-K. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10851-x · Cited by 40 · see citing works
Ghosh, I., Alfaro, E., Gámez, M., & García, N. (2024) · International Review of Financial Analysis · doi:10.1016/j.irfa.2024.103215 · Cited by 20 · see citing works
A User-Centric Analysis of Social Media for Stock Market Prediction
Bouadjenek, M. R., Sanner, S., & Wu, G. (2022) · ACM Transactions on the Web · doi:10.1145/3532856 · Cited by 18 · see citing works
Large Language Model Agent in Financial Trading: A Survey
Ding, H., Li, Y., Wang, J., Chen, H., Guo, D., & Zhang, Y. (2026) · Proceedings of the 2026 9th International Conference on Computers in Management and Business · doi:10.1145/3802463.3802475 · Cited by 10 · see citing works
“This section discusses different sub-types of LLM as a trader agents, including news-driven, reflection-driven, debate-driven and reinforcement learning(RL)-driven agents.”
The Linguistic Landscape of “Controversial”: Sentiment and Theme Distribution Insights
Grishechko, E. G. (2024) · GEMA Online Journal of Language Studies · doi:10.17576/gema-2024-2401-05 · Cited by 4 · see citing works
Wang, K., Wong, B., Khoshkholghi, M. A., Shah, P., Naha, R. K., Mahanti, A., & Kim, J.-K. (2024) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-57853-3_22 · Cited by 3 · see citing works
Learning Dynamic Multimodal Implicit and Explicit Networks for Multiple Financial Tasks
Ang, G., & Lim, E. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020722 · Cited by 1 · see citing works
Social Media Dynamics of Shorted Companies
Terve, C., Erlingsson, M., Mohammadinodooshan, A., & Carlsson, N. F. (2022) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams58071.2022.10062796
ForumPFN: Online Forum Post Fusion Network for Volatility Index Movement Prediction
Ueda, K., Suwa, H., Umehara, E., Ogawa, Y., Yamashita, T., Tsubouchi, K., & Yasumoto, K. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825644
Enhancing user reliability using a contextual-based approach on heterogeneous graph
D’Amico, S., Sperlí, G., & Velyka, A. (2025) · ACM Symposium on Applied Computing · doi:10.1145/3672608.3707951
Hosokawa, R., Ueda, K., Suwa, H., Ogawa, Y., Umehara, E., Yamashita, T., Tsubouchi, K., Hattori, H., & Yasumoto, K. (2025) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.40-4_d-o94
- Authors
- Huynh, D., Audet, G., Alabi, N., & Tian, Y.
- Affiliations
- Queen's University (CA)
- Venue
- 2021 IEEE International Conference on Big Data (Big Data)
- Publisher
- IEEE
- Published
- 2021-12-15
- Pages
- 1054-1060
- OpenAlex
- W4205688737
- Cited by
- 10
- References
- 15
- Citations by year
- 2025: 2 · 2024: 5 · 2022: 3
- FWCI
- 2.91
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Big Data (BigData) (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- stock price prediction, Reddit, r/wallstreetbets, retail investors, sentiment analysis, financial forecasting
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