7,905 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
CentralNet: a Multilayer Approach for Multimodal Fusion
This paper proposes a novel multimodal fusion approach, aiming to produce
best possible decisions by integrating information coming from multiple media.
While most of the past multimodal approaches either work by projecting the
features of different modalities into the same space, or by coordinating the
representations of each modality through the use of constraints, our approach
borrows from both visions. More specifically, assuming each modality can be
processed by a separated deep convolutional network, allowing to take decisions
independently from each modality, we introduce a central network linking the
modality specific networks. This central network not only provides a common
feature embedding but also regularizes the modality specific networks through
the use of multi-task learning. The proposed approach is validated on 4
different computer vision tasks on which it consistently improves the accuracy
of existing multimodal fusion approaches
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