Semantic Frames to Predict Stock Price Movement

By Boyi Xie, Rebecca J. Passonneau, Leon Wu, Germán G. Creamer. 2013

134 citations recorded by the source corpus.

Subfields: Natural Language Processing, Time-Series & Forecasting

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Citations in the corpus

Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, methodology): “Xie et al. [27] introduced a novel tree representation, and use it to train predictive models with tree kernels using support vector machines.”
Improving stock market prediction via heterogeneous information fusion (2017, methodology): “Various studies have found that ﬁnancial news can dramatically aﬀect the stock price [14, 15, 16, 17, 18, 19].”
Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks (2015, background): “For example, (Xie et al., 2013) has proposed to use semantic frame parsers to generalize from sentences to scenarios to detect the roles of specific companies (positive or negative), where support vector machines with tree kernels are used as predictive models.”
Using Structured Events to Predict Stock Price Movement: An Empirical Investigation (2014, methodology): “Previous work (Xie et al., 2013) uses an additional evaluation metric, which relies on the Matthews Correlation Cofﬁcient (MCC) to avoid bias due to data skew (our data are rather large and not severely skewed, but we also use MCC for comparison with previous work).”
Measuring the Information Content of Financial News (2016, background): “In terms of corpora, ﬁnancial news (Leinweber and Sisk, 2011; Xie et al., 2013; Luss and d’Aspremont, 2015; Ding et al., 2015), ﬁrm reports (Kogan et al., 2009; Li, 2010; Lee et al., 2014; Qiu et al., 2014) and web content, such as tweets (Bollen et al., 2011; Vu et al., 2012) and forum posts (Das…”
A New Model for Stock Price Movements Prediction Using Deep Neural Network (2017, methodology): “Many new methods have been proposed to explore additional information (mainly online text data) for the stock forecasting such as ﬁnancial news [9, 26], twitters sentiments [23], micro blogs [3].”
A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls (2014, methodology): “We have extracted lexical, named entity, syntactic, and frame-semantics features, most of which have been shown to perform well in previous work (Xie et al., 2013).”
Knowledge-Driven Event Embedding for Stock Prediction (2016, methodology): “Stock Market Prediction There has been a line of work predicting stock markets using text information from daily news (Lavrenko et al., 2000; Schumaker and Chen, 2009; Xie et al., 2013; Peng and Jiang, 2015; Li et al., 2016).”
Topic Modeling based Sentiment Analysis on Social Media for Stock Market Prediction (2015, background): “A novel tree representation based on semantic frame parsers is proposed (Xie et al., 2013).”
DeepClue: Visual Interpretation of Text-Based Deep Stock Prediction (2019, background): “According to the finding in [5], [23], [48], the predictive performance with only the previous day’s information is better than the weekly and monthly prediction because of the efficient market hypothesis [11].”
Stock Movement Prediction from Tweets and Historical Prices (2018, methodology): “In the rest of this price is widely used for predicting stock price movement (Xie et al., 2013) or financial volatility (Rekabsaz et al., 2017).”
On the Importance of Text Analysis for Stock Price Prediction (2014, methodology): “Xie et al. (2013) introduced tree representations of information in news, Bollen et al. (2010) used Twitter data, Bar-Haim et al. (2011) focused on identifying better expert investors, and Leinweber and Sisk (2011) studied the effect of news and the time needed to process the news in event-driven…”