Leveraging social media news to predict stock index movement using RNN-boost

By Weiling Chen, Chai Kiat Yeo, Chiew Tong Lau, Bu-Sung Lee. 2018

101 citations recorded by the source corpus.

Abstract News from traditional media has been used to facilitate the prediction of stock movement for a long time. However, in recent times, online social networks (OSN) have played an increasing significant role as a platform for information sharing. News content posted on these OSN provides very useful insight about public moods. In this paper, we carefully select official accounts from China’s largest online social networks — Sina Weibo and analyze the news content crawled from these accounts by extracting sentiment features and Latent Dirichlet allocation (LDA) features. We then input these features together with technical indicators into a novel hybrid model called RNN-boost to predict the stock volatility in the Chinese stock market. The Shanghai-Shenzhen 300 Stock Index (HS300) is the use case for this research. Experimental results show that our model outperforms other prevalent methods and can achieve a good prediction performance.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research, Economics & Operations Research, Time-Series & Forecasting

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “The authors of [154] used social media news, LDA features and RNN model to predict the trend of the index price.”
Deep Learning for Financial Applications : A Survey (2020, methodology): “In [174], social media news were used to predict the index price and the index direction with RNN-Boost through Latent Dirichlet Allocation (LDA) features.”
Text Mining in Big Data Analytics (2020, methodology): “Moreover, they used the deep recurrent neural network [198].”
A Survey of Forex and Stock Price Prediction Using Deep Learning (2021, methodology): “And paper [29] had the highest order of magnitude of MAPE.”
Applications of deep learning in stock market prediction: recent progress (2020, background): “…Prices + Technical Indicators + Text Vargas et al. (2017); Liu et al. (2019c); Oncharoen & Vateekul (2018); Lien Minh et al. (2018); Lee & Soo (2017); Chen et al. (2018b) Text Liu et al. (2018); Hu et al. (2018b); Ding et al. (2015, 2014) Image Sezer & Ozbayoglu (2018); Sim et al. (2019); Lee…”