16,547 research outputs found
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
Economic Policy Uncertainty: A Review on Applications and Measurement Methods with Focus on Text Mining Methods
Economic Policy Uncertainty (EPU) represents the uncertainty realized by the
investors during economic policy alterations. EPU is a critical indicator in
economic studies to predict future investments, the unemployment rate, and
recessions. EPU values can be estimated based on financial parameters directly
or implied uncertainty indirectly using the text mining methods. Although EPU
is a well-studied topic within the economy, the methods utilized to measure it
are understudied. In this article, we define the EPU briefly and review the
methods used to measure the EPU, and survey the areas influenced by the changes
in EPU level. We divide the EPU measurement methods into three major groups
with respect to their input data. Examples of each group of methods are
enlisted, and the pros and cons of the groups are discussed. Among the EPU
measures, text mining-based ones are dominantly studied. These methods measure
the realized uncertainty by taking into account the uncertainty represented in
the news and publicly available sources of financial information. Finally, we
survey the research areas that rely on measuring the EPU index with the hope
that studying the impacts of uncertainty would attract further attention of
researchers from various research fields. In addition, we propose a list of
future research approaches focusing on measuring EPU using textual material.Comment: JEL Classification: C53, C38, A13, O38, H5
Understanding the Relationship between Online Discussions and Bitcoin Return and Volume: Topic Modeling and Sentiment Analysis
This thesis examines Bitcoin related discussions on Bitcointalk.com over the 2013-2022 period. Using Latent Dirichlet Allocation (LDA) topic modeling algorithm, we discover eight distinct topics: Mining, Regulation, Investment/trading, Public perception, Bitcoin’s nature, Wallet, Payment, and Other. Importantly, we find differences in relations between different topics’ sentiment, disagreement (proxy for uncertainty) and hype (proxy for attention) on one hand and Bitcoin return and trading volume on the other hand. Specifically, among all topics, only the sentiment and disagreement of Investment/trading topic have significant contemporaneous relation with Bitcoin return. In addition, sentiment and disagreement of several topics, such as Mining and Wallet, show significant relationships with Bitcoin return only on the tails of the return distribution (bullish and bearish markets). In contrast, sentiment, disagreement, and hype of each topic show significant relation with Bitcoin volume across the entire distribution. In addition, whereas hype has a positive relation with trading volume in a low-volume market, this relation becomes negative in a high-volume market
Impact of Social Media on Dubai Stock Market using Sentiment Analysis
One of the main objectives of having securities stock markets is to ensure fair trading. Our analysis of this study will show how sentiment analysis and text mining techniques can help stock markets to sense wipes in the market participants\u27 behaviors and how the market community can benefit from it. The ability to detect potential insiders and investors\u27 mood would help the stock market to take necessary actions to protect the trading environment and enhance investors’ trust in the market. In this project, we will be building a pilot proof of concept utilizing sentiment analysis on Twitter, one of the most popular social media applications, and Dubai Financial Market, one of the most active stock markets in the United Arab Emirates (UAE), in the English language. The project can grow in sophistication and coverage in the future. In this project, I am using R as a primary development tool where a statistical and visual analysis will be carried out utilizing its rich open community libraries
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