1,863 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
Econometrics meets sentiment : an overview of methodology and applications
The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software
Statistical Inferences for Polarity Identification in Natural Language
Information forms the basis for all human behavior, including the ubiquitous
decision-making that people constantly perform in their every day lives. It is
thus the mission of researchers to understand how humans process information to
reach decisions. In order to facilitate this task, this work proposes a novel
method of studying the reception of granular expressions in natural language.
The approach utilizes LASSO regularization as a statistical tool to extract
decisive words from textual content and draw statistical inferences based on
the correspondence between the occurrences of words and an exogenous response
variable. Accordingly, the method immediately suggests significant implications
for social sciences and Information Systems research: everyone can now identify
text segments and word choices that are statistically relevant to authors or
readers and, based on this knowledge, test hypotheses from behavioral research.
We demonstrate the contribution of our method by examining how authors
communicate subjective information through narrative materials. This allows us
to answer the question of which words to choose when communicating negative
information. On the other hand, we show that investors trade not only upon
facts in financial disclosures but are distracted by filler words and
non-informative language. Practitioners - for example those in the fields of
investor communications or marketing - can exploit our insights to enhance
their writings based on the true perception of word choice
- …