19 research outputs found
The Role of Twitter in Cryptocurrency Pump-and-Dumps
We examine the influence of Twitter promotion on cryptocurrency pump-and-dump
events. By analyzing abnormal returns, trading volume, and tweet activity, we
uncover that Twitter effectively garners attention for pump-and-dump schemes,
leading to notable effects on abnormal returns before the event. Our results
indicate that investors relying on Twitter information exhibit delayed selling
behavior during the post-dump phase, resulting in significant losses compared
to other participants. These findings shed light on the pivotal role of Twitter
promotion in cryptocurrency manipulation, offering valuable insights into
participant behavior and market dynamics
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
Questioning the news about economic growth : sparse forecasting using thousands of news-based sentiment values
The modern calculation of textual sentiment involves a myriad of choices as to the actual calibration. We introduce a general sentiment engineering framework that optimizes the design for forecasting purposes. It includes the use of the elastic net for sparse data-driven selection and the weighting of thousands of sentiment values. These values are obtained by pooling the textual sentiment values across publication venues, article topics, sentiment construction methods, and time. We apply the framework to the investigation of the value added by textual analysis-based sentiment indices for forecasting economic growth in the US. We find that the additional use of optimized news-based sentiment values yields significant accuracy gains for forecasting the nine-month and annual growth rates of the US industrial production, compared to the use of high-dimensional forecasting techniques based on only economic and financial indicators. (C) 2018 The Author(s). Published by Elsevier B.V. on behalf of International Institute of Forecasters
Markov-switching GARCH models in R : the MSGARCH package
We describe the package MSGARCH, which implements Markov-switching GARCH (generalized autoregressive conditional heteroscedasticity) models in R with efficient C++ object-oriented programming. Markov-switching GARCH models have become popular methods to account for regime changes in the conditional variance dynamics of time series. The package MSGARCH allows the user to perform simulations as well as maximum likelihood and Bayesian Markov chain Monte Carlo estimations of a very large class of Markov-switching GARCH-type models. The package also provides methods to make single-step and multi-step ahead forecasts of the complete conditional density of the variable of interest. Risk management tools to estimate conditional volatility, value-at-risk, and expected-shortfall are also available. We illustrate the broad functionality of the MSGARCH package using exchange rate and stock market return data
Media abnormal tone, earnings announcements, and the stock market
We conduct a tone-based event study to examine the aggregate abnormal tone
dynamics in media articles around earnings announcements. We test whether they
convey incremental information that is useful for price discovery for
nonfinancial S&P 500 firms. The relation we find between the abnormal tone and
abnormal returns suggests that media articles provide incremental information
relative to the information contained in earnings press releases and earnings
calls.Comment: Forthcoming in Journal of Financial Market
Landscape of Academic Finance with the Structural Topic Model
Using the structural topic model, we present a landscape of academic finance.
We analyze more than 40,000 titles and abstracts published in 32 finance
journals over a period ranging from 1992 to 2020. We identify the research
topics and explore their relation and prevalence over time and across journals.
Our analyses reveal that most journals have covered more topics over time, thus
becoming more generalist
How easy is it for investment managers to deploy their talent in green and brown stocks?
We explore the realized alpha-performance heterogeneity in green and brown
stocks' universes using the peer performance ratios of Ardia and Boudt (2018).
Focusing on S&P 500 index firms over 2014-2020 and defining peer groups in
terms of firms' greenhouse gas emission levels, we find that, on average, about
20% of the stocks differentiate themselves from their peers in terms of future
performance. We see a much higher time-variation in this opportunity set within
brown stocks. Furthermore, the performance heterogeneity has decreased over
time, especially for green stocks, implying that it is now more difficult for
investment managers to deploy their skills when choosing among low-GHG
intensity stocks