Global Financial Crisis and Trade Papers: Topic Analysis Via Latent Dirichlet Allocation Model

Öz

We examine the trends in the studies published in international trade journals indexed in Web of Science (WoS) before and after the 2008 Global Financial Crisis (GFC). This study investigates the 5001 abstracts of trade articles starting from the post-millennium period by addressing issues in WoS database trade journals using the Latent Dirichlet Allocation (LDA) topic model. The purpose of the study is to evaluate whether topics and words differ that have been discussed in international trade literature before and after GFC. Mostly, contain topics differ when two periods are compared, although trade agreements and disputes, stock markets, and growth issues are frequently mentioned in both periods. In the post-crisis period, volatility, banks, and firm-specific issues are more popular. It is also among the findings that Asian economies gained importance after GFC. Generally, it has been revealed that the topics and titles in the trade literature can reflect current developments in international economics. Therefore, probably for next years, advances in technology as blockchain, emerging economies –especially China and India – and firm-based micro-scale papers will dominate the trade journals.

Anahtar Kelimeler

• International Trade
• Latent Dirichlet Allocation
• Topic Modeling
• Trends of Trade

Global Finansal Kriz ve Ticaret Makaleleri: Gizil Dirichlet Ayrımı Yöntemiyle Başlık Analizi

Öz

Çalışmamızda; 2008 Global Finansal Kriz öncesinde ve sonrasında Web of Science’da uluslararası ticaret ile ilgili yayınlanan makalelerdeki trendler ele alınmaktadır. Bu çalışma, milenyum sonrası dönemden başlayarak Web of Science veri tabanında yer alan 5001 uluslararası ticaret makale özetini Gizli Dirichlet Ayrımı başlık analizini kullanarak incelemektedir. Çalışmanın amacı, global finansal kriz öncesi ve sonrası uluslararası ticaret literatüründe tartışılan konuların ve kelimelerin farklılaşıp farklılaşmadığını değerlendirmektir. Çoğunlukla, iki dönem kıyaslandığında konu başlıkları farklılık gösterse de; her iki dönemde de ticaret anlaşmaları ve anlaşmazlıklar, hisse senedi piyasaları ve büyüme sorunları sıklıkla dile getirilmektedir. Kriz sonrası dönemde istikrarsızlık, bankalar ve firmaya özgü konuların daha popüler olduğu görülmektedir. Asya ekonomilerinin global finansal krizden sonra önem kazandığı da bulgular arasındadır. Genel olarak ticaret literatüründe yer alan konu ve başlıkların uluslararası ekonomideki güncel gelişmeleri yansıtabildiği ortaya çıkmaktadır. Bu nedenle, muhtemelen önümüzdeki yıllarda gelişmekte olan ekonomiler - özellikle Çin ve Hindistan’da blockchain ve teknolojideki gelişmeler ve firma tabanlı mikro ölçekli makaleler ticaret dergilerine hâkim olacaktır.

Anahtar Kelimeler

• Uluslararası Ticaret
• Gizil Dirichlet Ayrımı
• Başlık Analizi
• Ticaret Eğilimleri

Kaynakça

1. Ambrosino, A., Cedrini, M., Davis, J. B., Fiori, S., Guerzoni, M., and Nuccio, M. (2018). What topic modeling could reveal about the evolution of economics. Journal of Economic Methodology, 25(4), 329-348. Doi: 10.1080/1350178X.2018.1529215
2. Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.
3. Bush, G. W., and Glassman, J. K. (2012). The 4% Solution: Unleashing the Economic Growth America Needs. Currency.
4. Calvo-González, O., Eizmendi, A., and Reyes, G. (2018). Winners never quit, quitters never grow: Using text mining to measure policy volatility and its link with long-term growth in Latin America. The World Bank.
5. Chandler, A. D., and Mazlish, B. (Eds.). (2005). Leviathans: Multinational corporations and the new global history. Cambridge University Press.
6. Chor, D., and Manova, K. (2012). Off the cliff and back? Credit conditions and international trade during the global financial crisis. Journal of international economics, 87(1), 117-133. Doi: 10.1016/j.jinteco.2011.04.001
7. Claveau, F., and Gingras, Y. (2016). Macrodynamics of economics: A bibliometric history. History of Political Economy, 48(4), 551-592. Doi: 10.1215/00182702-3687259
8. Edison, H., and Carcel, H. (2020). Text data analysis using Latent Dirichlet Allocation: an application to FOMC transcripts. Applied Economics Letters, 1-5. Doi: 10.1080/13504851.2020.1730748

9. Feuerriegel, S., and Pröllochs, N. (2018). Investor reaction to financial disclosures across topics: An application of latent Dirichlet allocation. Decision Sciences. Doi: 10.1111/deci.12346
10. Feuerriegel, S., Ratku, A., and Neumann, D. (2016, January). Analysis of how underlying topics in financial news affect stock prices using latent dirichlet allocation. In 2016 49th Hawaii International Conference on System Sciences (HICSS), 1072-1081.
11. Griffiths, T. L., and Steyvers, M. (2004). Finding scientific topics. Proceedings of the National academy of Sciences, 101(suppl 1), 5228-5235.
12. Guo, Y., Barnes, S. J., and Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59, 467-483. Doi: 10.1016/j.tourman.2016.09.009
13. Ianchovichina, E., and Walmsley, T. (2003). The impact of China’s WTO accession on East Asia. The World Bank.
14. Klopotan, I., Zoroja, J., and Meško, M. (2018). Early warning system in business, finance, and economics: Bibliometric and topic analysis. International Journal of Engineering Business Management, 10, 1847979018797013. Doi: 10.1177/1847979018797013
15. Kosnik, L. R. (2018). A survey of JEL codes: What do they mean and are they used consistently? Journal of Economic Surveys, 32(1), 249-272. Doi: 10.1111/joes.12189
16. Kosnik, L. R. D. (2015). What have economists been doing for the last 50 years? A text analysis of published academic research from 1960-2010. A Text Analysis of Published Academic Research from, 2010.
17. Kozlowski, D., Semeshenko, V., & Molinari, A. (2021). Latent Dirichlet allocation model for world trade analysis. PloS one, 16(2), e0245393. Doi: 10.1371/journal.pone.0245393.
18. Larsen, V. H., and Thorsrud, L. A. (2019). The value of news for economic developments. Journal of Econometrics, 210(1), 203-218. Doi: 10.1016/j.jeconom.2018.11.013
19. Levitt, T. (1993). The globalization of markets. Readings in international business: a decision approach, 249.
20. Meyn, M., and Kennan, J. (2009). The implications of the global financial crisis for developing countries' export volumes and values. Overseas Development Institute.
21. Moro, S., Cortez, P., and Rita, P. (2015). Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications, 42(3), 1314-1324. Doi: 10.1016/j.eswa.2014.09.024
22. Newton, K. (2019). International Relations and World Politics. Scientific e-Resources.
23. Puranam, D., Narayan, V., and Kadiyali, V. (2017). The effect of calorie posting regulation on consumer opinion: A flexible latent Dirichlet allocation model with informative priors. Marketing Science, 36(5), 726-746. Doi: 10.1287/mksc.2017.1048
24. Schwarz, C. (2018). ldagibbs: A command for topic modeling in Stata using latent Dirichlet allocation. The Stata Journal, 18(1), 101-117. Doi: 10.1177/1536867X1801800107
25. Shelburne, R. C. (2010). The global financial crisis and its impact on trade: the world and the european emerging economies. United Nations Economic Commission for Europe-Discussion Papers Series, 2.
26. Tian, X., Geng, Y., Sarkis, J., and Zhong, S. (2018). Trends and features of embodied flows associated with international trade based on bibliometric analysis. Resources, Conservation and Recycling, 131, 148-157. Doi: 10.1016/j.resconrec.2018.01.002
27. UNCTAD. (2008-2017). Evolution of the international trading system and its trends from a Development Perspective Report. Available at https://unctad.org/en/Pages/DITC/TNCD/Evolution-of-the-intl-trading-system.aspx.
28. Wehrheim, L. (2019). Economic history goes digital: topic modeling the Journal of Economic History. Cliometrica, 13(1), 83-125. Doi: 10.1007/s11698-018-0171-7
29. World Trade Organization. (2008). World Trade Organization Report. Available at https://www.wto.org/english/res_e/booksp_e/anrep_e/wtr08-2b_e.pdf.
30. Xing, D., and Girolami, M. (2007). Employing Latent Dirichlet allocation for fraud detection in telecommunications. Pattern Recognition Letters, 28(13), 1727-1734. Doi: 10.1016/j.patrec.2007.04.015
31. Zoghbi, S., Vulić, I., and Moens, M. F. (2016). Latent Dirichlet allocation for linking user-generated content and e-commerce data. Information Sciences, 367, 573-599. Doi: 10.1016/j.ins.2016.05.047.