A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls
By William Yang Wang, Zhenhao Hua. 2014
64 citations recorded by the source corpus.
Earnings call summarizes the financial performance of a company, and it is an important indicator of the future financial risks of the company. We quantitatively study how earnings calls are correlated with the financial risks, with a special focus on the financial crisis of 2009. In particular, we perform a text regression task: given the transcript of an earnings call, we predict the volatility of stock prices from the week after the call is made. We propose the use of copula: a powerful statistical framework that separately models the uniform marginals and their complex multivariate stochastic dependencies, while not requiring any prior assumptions on the distributions of the covariate and the dependent variable. By performing probability integral transform, our approach moves beyond the standard count-based bag-ofwords models in NLP, and improves previous work on text regression by incorporating the correlation among local features in the form of semiparametric Gaussian copula. In experiments, we show that our model significantly outperforms strong linear and non-linear discriminative baselines on three datasets under various settings.
Subfields: Artificial Intelligence, Management Science and Operations Research
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In Artificial Intelligence
- Jiang Bian
- Philip S. Yu
- Tie-Yan Liu
- Yuntao Yao
- Weiqing Liu
- Samuel Rönnqvist
- Xuanzhe Liu
- Peter Sarlin
In Management Science and Operations Research
Citations in the corpus
- Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, background): “Wang et al. [26] performed a text regression task to predict the volatility of stock prices.”
- Improving stock market prediction via heterogeneous information fusion (2017, background): “Various studies have found that financial news can dramatically affect the stock price [14, 15, 16, 17, 18, 19].”
- Bank distress in the news: Describing events through deep learning (2016, methodology): “Data-driven approaches, such as Wang & Hua [42] predicting volatility of company stocks from earning calls, may avoid the issues of handcrafted features and manually annotated corpora.”
- Detect & Describe: Deep Learning of Bank Stress in the News (2015, methodology): “Data-driven approaches, such as Wang & Hua [23] predicting volatility of company stocks from earning calls, may avoid these issues.”