Call for Papers April 2024
The Journal of Economic Theory and Econometrics (JETEM) announces a plan for a special issue under the following title:
Economic Analysis through Artificial Intelligence and Big Data.
With the rapid advancements in technologies for artificial intelligence and big data, we are witnessing the emergence of new economic analyses on a daily basis. Recent publications in JETEM, along with prevailing trends in economic analysis, reflect this ongoing evolution. Examples include macroeconomic or stock market analyses conducted through text mining and novel econometric approaches applied to big data.
In order to align with the current trends in economic analysis, the editorial board has decided to publish a special issue dedicated to collecting papers on artificial intelligence and big data.
Plan:
- Submission due day : We will accept submissions until a sufficient number of papers have been collected for an issue.
- Paper format : Papers written in English will receive priority consideration.
- Paper topics : The topic is not restricted solely to data analysis and econometric theory papers; it is also open to economic theory papers. As long as the paper's topic aligns with the theme of the special issue, it is suitable for submission.
- Submission fee : None.
- Submission : submitjetem@gmail.com
Jin Seo Cho Managing Editor
References:
Bholat, D., S. Hansen, P. Santos, and C. Schonhardt-Bailey. (2015). "Text mining for central banks," Centre for Central Banking Studies Handbook, 1019.
Choi, G. and C. Kim. (2024). "Forecasting stock market volatility: A sentiment based approach," Journal of Economic Theory and Econometrics, 35, 29-58.
Kim, B. and H. Han. (2022). "Multi-step-ahead forecasting of the CBOE volatility index in a data-rich environment: Application of random forecast with Boruta Algorithm," JKorean Economic Review, 38, 541-569.
Kim, S. and D. Cho. (2022). "Forecasting Crude Oil Prices with Google Trends Data Based on Machine Learning Methods," Korean Journal of Economics, 29, 175-193.
Lee, Y., S. Kim, and K. Park. (2019). "Deciphering Monetary Policy Board Minutes with Text Mining," Korean Economic Review, 35, 471-511.
Loughran, T. and B. McDonald (2011). "When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks," Journal of Finance, 66, 35-65.
Seo, S. and J. Kim. (2015). "The information content of option-implied information for volatility forecasting with investor sentiment," Journal of Banking and Finance, 50, 106-120.
Tibshirani, R. (1996). "Regression shrinkage and selection via the Lasso," Journal of the Royal Statistical Society: Series B, 58, 267-288.
Won, J., Son, W., Moon, H., and Lee, H. (2017). "Text mining techniques for classification of economic sentiment," BOK Quarterly Bulletin.