12,376 research outputs found
Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models
Volatility prediction--an essential concept in financial markets--has
recently been addressed using sentiment analysis methods. We investigate the
sentiment of annual disclosures of companies in stock markets to forecast
volatility. We specifically explore the use of recent Information Retrieval
(IR) term weighting models that are effectively extended by related terms using
word embeddings. In parallel to textual information, factual market data have
been widely used as the mainstream approach to forecast market risk. We
therefore study different fusion methods to combine text and market data
resources. Our word embedding-based approach significantly outperforms
state-of-the-art methods. In addition, we investigate the characteristics of
the reports of the companies in different financial sectors
Efficient Correlated Topic Modeling with Topic Embedding
Correlated topic modeling has been limited to small model and problem sizes
due to their high computational cost and poor scaling. In this paper, we
propose a new model which learns compact topic embeddings and captures topic
correlations through the closeness between the topic vectors. Our method
enables efficient inference in the low-dimensional embedding space, reducing
previous cubic or quadratic time complexity to linear w.r.t the topic size. We
further speedup variational inference with a fast sampler to exploit sparsity
of topic occurrence. Extensive experiments show that our approach is capable of
handling model and data scales which are several orders of magnitude larger
than existing correlation results, without sacrificing modeling quality by
providing competitive or superior performance in document classification and
retrieval.Comment: KDD 2017 oral. The first two authors contributed equall
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