Exploring Accounting Research Topic Evolution: An Unsupervised Machine Learning Approach
June Cao, Zhanzhong Gu, Iftekhar Hasan
Journal of International Accounting Research,
No. 3,
2023
Abstract
This study explores the evolution of accounting research by utilizing an unsupervised machine learning approach. We aim to identify the latent topics of accounting from the 1980s up to 2018, the dynamics and emerging topics of accounting research, and the economic reasons behind those changes. First, based on 23,220 articles from 46 accounting journals, we identify 55 topics using the latent Dirichlet allocation model. To illustrate the connection between topics, we use HistCite to generate a citation map along a timeline. The citation clusters demonstrate the “tribalism” phenomenon in accounting research. We then implement the dynamic topic model to reveal the dynamics of topics to show changes in accounting research. The emerging research trends are identified from the topic analytics. We further explore the economic reasons and in-depth insights into the topic evolution, indicating the economic development embeddedness nature of accounting research.
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Media Response
Media Response September 2024 IWH: Drei Minus-Jahre in Folge ‒ die Wirtschaft schrumpft weiter in: Handelsblatt, 20.09.2024 Reint Gropp: Intel outside in: Wirtschaftswoche,…
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People
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Safety Net or Helping Hand? The Effect of Job Search Assistance and Compensation on Displaced Workers
Daniel Fackler, Jens Stegmaier, Richard Upward
IWH Discussion Papers,
No. 18,
2023
Abstract
We provide the first systematic evidence on the effectiveness of a contested policy in Germany to help displaced workers. So-called “transfer companies” (Transfergesellschaften) employ displaced workers for a fixed period, during which time workers are provided with job-search assistance and are paid a wage which is a substantial fraction of their pre-displacement wage. Using rich and accurate data on workers’ employment patterns before and after displacement, we compare the earnings and employment outcomes of displaced workers who entered transfer companies with those that did not. Workers can choose whether or not to accept a position in a transfer company, and therefore we use the availability of a transfer company at the establishment level as an IV in a model of one-sided compliance. Using an event study, we find that workers who enter a transfer company have significantly worse post-displacement outcomes, but we show that this is likely to be the result of negative selection: workers who lack good outside opportunities are more likely to choose to enter the transfer company. In contrast, ITT and IV estimates indicate that the use of a transfer company has a positive and significant effect on employment rates five years after job loss, but no significant effect on earnings. In addition, the transfer company provides significant additional compensation to displaced workers in the first 12 months after job loss.
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Herding Behavior and Systemic Risk in Global Stock Markets
Iftekhar Hasan, Radu Tunaru, Davide Vioto
Journal of Empirical Finance,
September
2023
Abstract
This paper provides new evidence of herding due to non- and fundamental information in global equity markets. Using quantile regressions applied to daily data for 33 countries, we investigate herding during the Eurozone crisis, China’s market crash in 2015–2016, in the aftermath of the Brexit vote and during the Covid-19 Pandemic. We find significant evidence of herding driven by non-fundamental information in case of negative tail market conditions for most countries. This study also investigates the relationship between herding and systemic risk, suggesting that herding due to fundamentals increases when systemic risk increases more than when driven by non-fundamentals. Granger causality tests and Johansen’s vector error-correction model provide solid empirical evidence of a strong interrelationship between herding and systemic risk, entailing that herding behavior may be an ex-ante aspect of systemic risk, with a more relevant role played by herding based on fundamental information in increasing systemic risk.
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