REST: Relational Event-driven Stock Trend Forecasting

Stock trend forecasting, aiming at predicting the stock future trends, is crucial for investors to seek maximized profits from the stock market. Many event-driven methods utilized the events extracted from news, social media, and discussion board to forecast the stock trend in recent years. However, existing event-driven methods have two main shortcomings: 1) overlooking the influence of event information differentiated by the stock-dependent properties; 2) neglecting the effect of event information from other related stocks. In this paper, we propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks. The experimental studies on the real-world data demonstrate the efficiency of our REST framework. The results of investment simulation show that our framework can achieve a higher return of investment than baselines.

[1]  Jingjing Xu,et al.  Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction , 2020, IJCAI.

[2]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[3]  Takashi Matsubara,et al.  Deep learning for stock prediction using numerical and textual information , 2016, 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS).

[4]  Manuel R. Vargas,et al.  Deep learning for stock market prediction from financial news articles , 2017, 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA).

[5]  Qiyuan Gao,et al.  Stock market forecasting using recurrent neural network , 2016 .

[6]  Gian Jyoti,et al.  Stock Market Forecasting Techniques: A Survey , 2014 .

[7]  Ken-ichi Kawarabayashi,et al.  Representation Learning on Graphs with Jumping Knowledge Networks , 2018, ICML.

[8]  Ke Xu,et al.  Can Online Emotions Predict the Stock Market in China? , 2016, WISE.

[9]  Xiaotie Deng,et al.  Exploiting Topic based Twitter Sentiment for Stock Prediction , 2013, ACL.

[10]  Max Welling,et al.  Modeling Relational Data with Graph Convolutional Networks , 2017, ESWC.

[11]  Barry Smyth,et al.  HTML: Hierarchical Transformer-based Multi-task Learning for Volatility Prediction , 2020, WWW.

[12]  Toyotaro Suzumura,et al.  Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis , 2019, ArXiv.

[13]  Jack L. Treynor,et al.  MUTUAL FUND PERFORMANCE* , 2007 .

[14]  Yulei Rao,et al.  A deep learning framework for financial time series using stacked autoencoders and long-short term memory , 2017, PloS one.

[15]  Sheung Yin Kevin Mo,et al.  Twitter financial community sentiment and its predictive relationship to stock market movement , 2015 .

[16]  Yiqun Liu,et al.  Temporal Relational Ranking for Stock Prediction , 2018, ACM Trans. Inf. Syst..

[17]  Jonathan L. Ticknor.  A Bayesian regularized artificial neural network for stock market forecasting , 2013, Expert Syst. Appl..

[18]  Chris Gale,et al.  Technical Analysis of Stock Trends , 2012 .

[19]  Maosong Sun,et al.  Enhancing Stock Movement Prediction with Adversarial Training , 2018, IJCAI.

[20]  Huajun Chen,et al.  Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network , 2019, WWW.

[21]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[22]  Wei Zhang,et al.  Hybrid Deep Sequential Modeling for Social Text-Driven Stock Prediction , 2018, CIKM.

[23]  Ling Liu,et al.  The effect of news and public mood on stock movements , 2014, Inf. Sci..

[24]  Yue Zhang,et al.  Learning Target-Specific Representations of Financial News Documents For Cumulative Abnormal Return Prediction , 2018, COLING.

[25]  Xuanjing Huang,et al.  Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction , 2018, CIKM.

[26]  Shay B. Cohen,et al.  Stock Movement Prediction from Tweets and Historical Prices , 2018, ACL.

[27]  Barbara Pfeffer,et al.  Smoothing Forecasting And Prediction Of Discrete Time Series , 2016 .

[28]  Yu Qin,et al.  What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues , 2019, ACL.

[29]  E. Fama.  EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[30]  Yan Chen,et al.  Web Media and Stock Markets : A Survey and Future Directions from a Big Data Perspective , 2018, IEEE Transactions on Knowledge and Data Engineering.

[31]  Ying Wah Teh,et al.  Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment , 2015, Expert Syst. Appl..

[32]  Charu C. Aggarwal,et al.  Stock Price Prediction via Discovering Multi-Frequency Trading Patterns , 2017, KDD.

[33]  Jun Yang,et al.  Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression , 2016 .

[34]  Tie-Yan Liu,et al.  Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction , 2017, WSDM.

[35]  Hsinchun Chen,et al.  Stakeholder Analyses of Firm-Related Web Forums: Applications in Stock Return Prediction , 2015, TMIS.

[36]  Jaewoo Kang,et al.  HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction , 2019, ArXiv.

[37]  Aderemi Oluyinka Adewumi,et al.  Stock Price Prediction Using the ARIMA Model , 2014, 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation.

[38]  Yue Zhang,et al.  Knowledge-Driven Event Embedding for Stock Prediction , 2016, COLING.

[39]  Julien Velcin,et al.  Sentiment analysis on social media for stock movement prediction , 2015, Expert Syst. Appl..

[40]  Dacheng Tao,et al.  Multi-task Recurrent Neural Networks and Higher-order Markov Random Fields for Stock Price Movement Prediction: Multi-task RNN and Higer-order MRFs for Stock Price Classification , 2019, KDD.

[41]  Xiang Cheng,et al.  Hierarchical Complementary Attention Network for Predicting Stock Price Movements with News , 2018, CIKM.

[42]  Sahil Shah,et al.  Predicting stock market index using fusion of machine learning techniques , 2015, Expert Syst. Appl..

[43]  D. Logue,et al.  The Total Cost of Transactions on the NYSE , 1988 .

[44]  Arun Agarwal,et al.  Recurrent neural network and a hybrid model for prediction of stock returns , 2015, Expert Syst. Appl..