Deep Learning for Event-Driven Stock Prediction

By Xiao Ding, Ting-Wei Liu, Junwen Duan. 2015

832 citations recorded by the source corpus.

Subfields: 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 [123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 114] used S&P500 as their dataset.”
Deep Learning for Financial Applications : A Survey (2020, background): “In [169], events were detected from Reuters and Bloomberg news through text mining.”
Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks (2019, background): “Ding et al. [7] proposed a deep learning model to predict stock prices by extracting texts from newspapers and internet.”
Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market? (2018, background): “Over against the limitations of existing models DL can process high frequency big data, analyze the ﬁnancial market, and predict stock returns [23], [24].”
Forecasting Economics and Financial Time Series: ARIMA vs. LSTM (2018, background): “2001; Huck, 2009; Xiong, 2015] and measuring the impact of incorporating news for selected companies [Siah and Myers, Joshi et al. 2013; Ding et al. 2015].”
Sentiment Predictability for Stocks (2017, critical): “As in [DZLD15], we create corrupted event tuples E r where we replace the actor embedding vector with the word embedding of a random possible word, which we also pass through our network to produce corrupted event embeddings U r .”
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, methodology): “Ding et al. [5] proposed a deep learning method for event-driven stock market prediction.”
Improving stock market prediction via heterogeneous information fusion (2017, methodology): “Deep learning-based method is proposed to learn event representations to capture syntactic and semantic information based on word embeddings [5].”
Financial Time Series Prediction Using Deep Learning (2017, background): “Ding [27] studied a similar topic by using structured information extracted from headlines to predict daily S&P 500 movements.”
Decision support from financial disclosures with deep neural networks and transfer learning (2017, background): “…that utilize deep learning for ﬁnancial text mining; yet we found a two-stage approach that extracts speciﬁc word triples consisting of an actor, an action and an object from more than 400,000 headlines of Bloomberg news and then applies deep learning in order to predict stock price movements [35].”
NSE Stock Market Prediction Using Deep-Learning Models (2018, background): “al in [22] con- ducted a study on combination of Natural language processing (NLP) and financial time series analysis.”
Recurrent Neural Networks Approach to the Financial Forecast of Google Assets (2017, neutral): “Even taking into account only the NNS ones, we can applications belonging to the MLPs methods, see, e.g., [16], convolutional neural networks (CNNs), see, e.g.,[6], Elman neural networks, see, e.g., [10], etc.”
Mind the Gap: Assessing Temporal Generalization in Neural Language Models (2021, background): “…training period (Thorne and Vlachos, 2018; Zellers et al., 2019; Augenstein et al., 2019), forecasting stock prices from the latest news articles (Ding et al., 2015), and answering knowledge-intensive questions like “How many people have been infected by COVID-19?” and “Has the USA ever had a…”
Knowledge-Driven Event Embedding for Stock Prediction (2016, methodology): “Ding et al. (2015) propose to address this issue by representing structured events using event embeddings, which are dense vectors.”
A Review of Deep Learning Models for Time Series Prediction (2019, background): “One recent work adapted the dilated CNN architecture to the stock market prediction problem [77].”
Stock price prediction using LSTM, RNN and CNN-sliding window model (2017, background): “In [12], a combination of financial time series analysis and NLP have been introduced.”
AccPar: Tensor Partitioning for Heterogeneous Deep Learning Accelerators (2020, background): “Advances in deep learning (DL) have become the main drivers of revolutions in various commercial and enterprise applications, such as computer vision [1–3], social network [4– 6], financial data analysis [7–9], healthcare [10–12] and scientific computing [13–15].”