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Mapping Of The Publications On Financial Sentiment: A Bibliometric Analysis

Yıl 2025, Sayı: 70, 134 - 151, 21.12.2025
https://doi.org/10.53568/yyusbed.1609112

Öz

The aim of this study is to comprehensively examine the academic literature on sentiment analysis in financial markets using bibliometric analysis techniques. The study is based on 26,084 research articles on financial sentiment, obtained from the Web of Science database on November 17, 2024. VOSviewer bibliometric software was used for data analysis, and co-authorship, authors’ citation relationships, inter-country publication and citation relationships, inter-institutional publication and citation relationships, frequently used author keywords and their relationships, bibliographic citation networks between publications, and scientific dissemination and citation relationships of academic journals were investigated. The findings have shown that the United States, China, and European countries led in academic contributions to financial sentiment, with China's influence in this field increasing in recent years. Institutions such as Harvard University and the Chinese Academy of Sciences have stood out as the most influential academic organizations, while authors such as Biswajit Sarkar, Rangan Gupta, Ship Sankar Sana, and Wang Wei were at the center of scientific collaboration networks. Journals such as the Journal of Financial Economics, Applied Energy, Journal of Cleaner Production, and PLOS ONE have been emerged as leading publication platforms in financial sentiment research. The findings have highlighted the importance of research focusing on crisis periods and behavioral finance theories, shedding light on the growing academic impact and interdisciplinary collaboration in the field of financial sentiment. Studies which reveal the bibliographic profile of research on financial sentiment, are expected to contribute to the future researches.

Kaynakça

  • Akyüz, N. E. (2025). Yönetim ve organizasyon literatürünün bibliyometrik analizi: Akademik eğilimler ve gelecek perspektifleri. In S. Çavuşoğlu (Ed.), Yönetim ve organizasyon alanında uluslararası araştırmalar–IV (pp. 257–340). Eğitim Yayınevi.
  • Bahoo, S. (2020). Corruption in banks: A bibliometric review and agenda. Finance Research Letters, 35, 101499. https://doi.org/10.1016/j.frl.2020.101499
  • Baker, H. K., Kumar, S., & Pandey, N. (2021). Thirty years of the Global Finance Journal: A bibliometric analysis. Global Finance Journal, 47, 100492. https://doi.org/10.1016/j.gfj.2019.100492
  • Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The journal of Finance, 61(4), 1645-1680. https://doi.org/10.1111/j.1540-6261.2006.00885.
  • Blasco, N., Corredor, P., & Ferrer, E. (2018). Analysts herding: When does sentiment matter?. Applied Economics, 50(51), 5495-5509. https://doi.org/10.1080/00036846.2018.1486999
  • Bui, T. D., Ali, M. H., Tsai, F. M., Iranmanesh, M., Tseng, M. L., & Lim, M. K. (2020). Challenges and trends in sustainable corporate finance: A bibliometric systematic review. Journal of Risk and Financial Management, 13(11), 264. https://doi.org/10.3390/jrfm13110264
  • Chen, X., & Xie, H. (2020). A structural topic modeling-based bibliometric study of sentiment analysis literature. Cognitive Computation, 12, 1097-1129. https://doi.org/10.1007/s12559-020-09745-1
  • Choijil, E., Méndez, C. E., Wong, W. K., Vieito, J. P., & Batmunkh, M. U. (2022). Thirty years of herd behavior in financial markets: A bibliometric analysis. Research in International Business and Finance, 59, 101506. https://doi.org/10.1016/j.ribaf.2021.101506
  • Cobo, M. J., López‐Herrera, A. G., Herrera‐Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for information Science and Technology, 62(7), 1382-1402. https://doi.org/10.1002/asi.21525
  • Costa, D. F., de Melo Carvalho, F., de Melo Moreira, B. C., & do Prado, J. W. (2017). Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias. Scientometrics, 111, 1775-1799. https://doi.org/10.1007/s11192-017-2371-5
  • Da, Z., Engelberg, J., & Gao, P. (2015). The sum of all FEARS investor sentiment and asset prices. The Review of Financial Studies, 28(1), 1-32. https://doi.org/10.1093/rfs/hhu072
  • Demir, G., Chatterjee, P., & Pamucar, D. (2024). Sensitivity analysis in multi-criteria decision making: A state-of-the-art research perspective using bibliometric analysis. Expert Systems with Applications, 237, 121660. https://doi.org/10.1016/j.eswa.2023.121660
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of business research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
  • Donthu, N., Kumar, S., Pattnaik, D., & Campagna, C. (2020). Journal of marketing theory and practice: A retrospective of 2005–2019. Journal of Marketing Theory and Practice, 28(2), 117-137. https://doi.org/10.1080/10696679.2020.1723424
  • Drago, C., Gatto, A., & Ruggeri, M. (2023). Telemedicine as technoinnovation to tackle COVID-19: A bibliometric analysis. Technovation, 120, 102417. https://doi.org/10.1016/j.technovation.2021.102417
  • Eachempati, P., & Srivastava, P. R. (2021). Accounting for unadjusted news sentiment for asset pricing. Qualitative Research in Financial Markets, 13(3), 383-422. https://doi.org/10.1108/QRFM-11-2019-0130
  • Ferretti, F., Saltelli, A., & Tarantola, S. (2016). Trends in sensitivity analysis practice in the last decade. Science of the total environment, 568, 666-670. https://doi.org/10.1016/j.scitotenv.2016.02.133
  • Garcia-Machado, J. J. (2018). The latest streams in finance research: An updated bibliometric mapping based on co-occurrence data. In Forum Scientiae Oeconomia (Vol. 6, No. 3, pp. 7-25). Wydawnictwo Naukowe Akademii WSB.
  • Garg, D., & Tiwari, P. (2021). Impact of social media sentiments in stock market predictions: A bibliometric analysis. Business Information Review, 38(4), 170-182. https://doi.org/10.1177/0266382121105866
  • Gong, X., Zhang, W., Wang, J., & Wang, C. (2022). Investor sentiment and stock volatility: New evidence. International Review of Financial Analysis, 80, 102028. https://doi.org/10.1016/j.irfa.2022.102028
  • Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577
  • Goyal, K., & Kumar, S. (2021). Financial literacy: A systematic review and bibliometric analysis. International Journal of Consumer Studies, 45(1), 80-105. https://doi.org/10.1111/ijcs.12605
  • Goyal, K., & Kumar, S. (2021). Financial literacy: A systematic review and bibliometric analysis. International Journal of Consumer Studies, 45(1), 80-105. https://doi.org/10.1111/ijcs.12605
  • Gupta, R., Nel, J., & Pierdzioch, C. (2023). Investor confidence and forecastability of US stock market realized volatility: Evidence from machine learning. Journal of Behavioral Finance, 24(1), 111-122. https://doi.org/10.1080/15427560.2021.1949719
  • He, Y., Qu, L., Wei, R., & Zhao, X. (2022). Media-based investor sentiment and stock returns: a textual analysis based on newspapers. Applied economics, 54(7), 774-792. https://doi.org/10.1080/00036846.2021.1966369
  • Hirshleifer, D. (2015). Behavioral finance. Annual Review of Financial Economics, 7(1), 133-159. https://doi.org/10.1146/annurev-financial-092214-043752
  • Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4), 330-338. https://doi.org/10.1016/j.jksues.2016.04.002
  • Jiang, S., & Jin, X. (2021). Effects of investor sentiment on stock return volatility: A spatio-temporal dynamic panel model. Economic Modelling, 97, 298-306. https://doi.org/10.1016/j.econmod.2020.04.002
  • Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., & Antuchevičienė, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. 50, 25-44. https://etalpykla.vilniustech.lt/handle/123456789/116529
  • Khan, A., Goodell, J. W., Hassan, M. K., & Paltrinieri, A. (2022). A bibliometric review of finance bibliometric papers. Finance Research Letters, 47, 102520. https://doi.org/10.1016/j.frl.2021.102520
  • Khan, A., Hassan, M. K., Paltrinieri, A., Dreassi, A., & Bahoo, S. (2020). A bibliometric review of takaful literature. International Review of Economics & Finance, 69, 389-405. https://doi.org/10.1016/j.iref.2020.05.013
  • Kumar, S., Pandey, N., Burton, B., & Sureka, R. (2021). Research patterns and intellectual structure of Managerial Auditing Journal: a retrospective using bibliometric analysis during 1986-2019. Managerial Auditing Journal, 36(2), 280-313. https://doi.org/10.1108/MAJ-12-2019-2517
  • Kumar, S., Rao, S., Goyal, K., & Goyal, N. (2022). Journal of Behavioral and Experimental Finance: A bibliometric overview. Journal of Behavioral and Experimental Finance, 34, 100652. https://doi.org/10.1016/j.jbef.2022.100652
  • Levine-Clark, M., & Gil, E. L. (2008). A comparative citation analysis of Web of Science, Scopus, and Google Scholar. Journal of Business & Finance Librarianship, 14(1), 32-46. https://doi.org/10.1080/08963560802176348
  • Malathy, S., & Kantha, P. (2015). Journal of Spacecraft Technology: A Bibliometric Study. Journal of Information and Knowledge, 141-151. https://doi.org/10.17821/srels/2015/v52i2/61969
  • Massa, M., & Yadav, V. (2015). Investor sentiment and mutual fund strategies. Journal of Financial and Quantitative Analysis, 50(4), 699-727. https://doi.org/10.1017/S0022109015000253
  • Mukherjee, D., Kumar, S., Mukherjee, D., & Goyal, K. (2022). Mapping five decades of international business and management research on India: A bibliometric analysis and future directions. Journal of Business Research, 145, 864-891. https://doi.org/10.1016/j.jbusres.2022.03.011
  • Obaid, K., & Pukthuanthong, K. (2022). A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news. Journal of Financial Economics, 144(1), 273-297. https://doi.org/10.1016/j.jfineco.2021.06.002
  • Patel, R., Goodell, J. W., Oriani, M. E., Paltrinieri, A., & Yarovaya, L. (2022). A bibliometric review of financial market integration literature. International Review of Financial Analysis, 80, 102035. https://doi.org/10.1016/j.irfa.2022.102035
  • Paul, J., & Criado, A. R. (2020). The art of writing literature review: What do we know and what do we need to know?. International business review, 29(4), 101717. https://doi.org/10.1016/j.ibusrev.2020.101717
  • Prasad, S., Mohapatra, S., Rahman, M. R., & Puniyani, A. (2022). Investor sentiment index: a systematic review. International Journal of Financial Studies, 11(1), 6. https://doi.org/10.3390/ijfs11010006
  • Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of documentation, 25, 348.
  • Qazi, A., Raj, R. G., Hardaker, G., & Standing, C. (2017). A systematic literature review on opinion types and sentiment analysis techniques: Tasks and challenges. Internet Research, 27(3), 608-630. https://doi.org/10.1108/IntR-04-2016-0086
  • Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-based systems, 89, 14-46. https://doi.org/10.1016/j.knosys.2015.06.015
  • Rialp, A., Merigó, J. M., Cancino, C. A., & Urbano, D. (2019). Twenty-five years (1992–2016) of the International Business Review: A bibliometric overview. International Business Review, 28(6), 101587. https://doi.org/10.1016/j.ibusrev.2019.101587
  • Strydom, N., & Els, G. (2016). A bibliometric analysis of the Journal of Economic and Financial Sciences (2007-2016). Journal of Economic and Financial Sciences, 9(3), 951-974. https://hdl.handle.net/10520/EJC198835
  • Sun, Y., Zeng, X., Zhou, S., Zhao, H., Thomas, P., & Hu, H. (2021). What investors say is what the market says: measuring China’s real investor sentiment. Personal and Ubiquitous Computing, 25, 587-599. https://doi.org/10.1007/s00779-021-01542-3
  • Sureka, R., Kumar, S., Colombage, S., & Abedin, M. Z. (2022). Five decades of research on capital budgeting–A systematic review and future research agenda. Research in International Business and Finance, 60, 101609. https://doi.org/10.1016/j.ribaf.2021.101609
  • Syed, A. M., & Bawazir, H. S. (2021). Recent trends in business financial risk–A bibliometric analysis. Cogent Economics & Finance, 9(1), 1913877. https://doi.org/10.1080/23322039.2021.1913877
  • Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of finance, 62(3), 1139-1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x
  • Viglia, G., Kumar, S., Pandey, N., & Joshi, Y. (2022). Forty years of The Service Industries Journal: a bibliometric review. The Service Industries Journal, 42(1-2), 1-20. https://doi.org/10.1080/02642069.2021.2003341
  • Wen, M. (2024). Determinants of Financing Constraints: A Bibliometric Analysis. European Journal of Business and Management Research, 9(4), 37-41. Doi: 10.24018/ejbmr.2024.9.4.2386
  • Xiong, X., Meng, Y., Li, X., & Shen, D. (2020). Can overnight return really serve as a proxy for firm-specific investor sentiment? Cross-country evidence. Journal of International Financial Markets, Institutions and Money, 64, 101173. https://doi.org/10.1016/j.intfin.2019.101173
  • Xu, X., Chen, X., Jia, F., Brown, S., Gong, Y., & Xu, Y. (2018). Supply chain finance: A systematic literature review and bibliometric analysis. International Journal of Production Economics, 204, 160-173. https://doi.org/10.1016/j.ijpe.2018.08.003

Yıl 2025, Sayı: 70, 134 - 151, 21.12.2025
https://doi.org/10.53568/yyusbed.1609112

Öz

Kaynakça

  • Akyüz, N. E. (2025). Yönetim ve organizasyon literatürünün bibliyometrik analizi: Akademik eğilimler ve gelecek perspektifleri. In S. Çavuşoğlu (Ed.), Yönetim ve organizasyon alanında uluslararası araştırmalar–IV (pp. 257–340). Eğitim Yayınevi.
  • Bahoo, S. (2020). Corruption in banks: A bibliometric review and agenda. Finance Research Letters, 35, 101499. https://doi.org/10.1016/j.frl.2020.101499
  • Baker, H. K., Kumar, S., & Pandey, N. (2021). Thirty years of the Global Finance Journal: A bibliometric analysis. Global Finance Journal, 47, 100492. https://doi.org/10.1016/j.gfj.2019.100492
  • Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The journal of Finance, 61(4), 1645-1680. https://doi.org/10.1111/j.1540-6261.2006.00885.
  • Blasco, N., Corredor, P., & Ferrer, E. (2018). Analysts herding: When does sentiment matter?. Applied Economics, 50(51), 5495-5509. https://doi.org/10.1080/00036846.2018.1486999
  • Bui, T. D., Ali, M. H., Tsai, F. M., Iranmanesh, M., Tseng, M. L., & Lim, M. K. (2020). Challenges and trends in sustainable corporate finance: A bibliometric systematic review. Journal of Risk and Financial Management, 13(11), 264. https://doi.org/10.3390/jrfm13110264
  • Chen, X., & Xie, H. (2020). A structural topic modeling-based bibliometric study of sentiment analysis literature. Cognitive Computation, 12, 1097-1129. https://doi.org/10.1007/s12559-020-09745-1
  • Choijil, E., Méndez, C. E., Wong, W. K., Vieito, J. P., & Batmunkh, M. U. (2022). Thirty years of herd behavior in financial markets: A bibliometric analysis. Research in International Business and Finance, 59, 101506. https://doi.org/10.1016/j.ribaf.2021.101506
  • Cobo, M. J., López‐Herrera, A. G., Herrera‐Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for information Science and Technology, 62(7), 1382-1402. https://doi.org/10.1002/asi.21525
  • Costa, D. F., de Melo Carvalho, F., de Melo Moreira, B. C., & do Prado, J. W. (2017). Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias. Scientometrics, 111, 1775-1799. https://doi.org/10.1007/s11192-017-2371-5
  • Da, Z., Engelberg, J., & Gao, P. (2015). The sum of all FEARS investor sentiment and asset prices. The Review of Financial Studies, 28(1), 1-32. https://doi.org/10.1093/rfs/hhu072
  • Demir, G., Chatterjee, P., & Pamucar, D. (2024). Sensitivity analysis in multi-criteria decision making: A state-of-the-art research perspective using bibliometric analysis. Expert Systems with Applications, 237, 121660. https://doi.org/10.1016/j.eswa.2023.121660
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of business research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
  • Donthu, N., Kumar, S., Pattnaik, D., & Campagna, C. (2020). Journal of marketing theory and practice: A retrospective of 2005–2019. Journal of Marketing Theory and Practice, 28(2), 117-137. https://doi.org/10.1080/10696679.2020.1723424
  • Drago, C., Gatto, A., & Ruggeri, M. (2023). Telemedicine as technoinnovation to tackle COVID-19: A bibliometric analysis. Technovation, 120, 102417. https://doi.org/10.1016/j.technovation.2021.102417
  • Eachempati, P., & Srivastava, P. R. (2021). Accounting for unadjusted news sentiment for asset pricing. Qualitative Research in Financial Markets, 13(3), 383-422. https://doi.org/10.1108/QRFM-11-2019-0130
  • Ferretti, F., Saltelli, A., & Tarantola, S. (2016). Trends in sensitivity analysis practice in the last decade. Science of the total environment, 568, 666-670. https://doi.org/10.1016/j.scitotenv.2016.02.133
  • Garcia-Machado, J. J. (2018). The latest streams in finance research: An updated bibliometric mapping based on co-occurrence data. In Forum Scientiae Oeconomia (Vol. 6, No. 3, pp. 7-25). Wydawnictwo Naukowe Akademii WSB.
  • Garg, D., & Tiwari, P. (2021). Impact of social media sentiments in stock market predictions: A bibliometric analysis. Business Information Review, 38(4), 170-182. https://doi.org/10.1177/0266382121105866
  • Gong, X., Zhang, W., Wang, J., & Wang, C. (2022). Investor sentiment and stock volatility: New evidence. International Review of Financial Analysis, 80, 102028. https://doi.org/10.1016/j.irfa.2022.102028
  • Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577
  • Goyal, K., & Kumar, S. (2021). Financial literacy: A systematic review and bibliometric analysis. International Journal of Consumer Studies, 45(1), 80-105. https://doi.org/10.1111/ijcs.12605
  • Goyal, K., & Kumar, S. (2021). Financial literacy: A systematic review and bibliometric analysis. International Journal of Consumer Studies, 45(1), 80-105. https://doi.org/10.1111/ijcs.12605
  • Gupta, R., Nel, J., & Pierdzioch, C. (2023). Investor confidence and forecastability of US stock market realized volatility: Evidence from machine learning. Journal of Behavioral Finance, 24(1), 111-122. https://doi.org/10.1080/15427560.2021.1949719
  • He, Y., Qu, L., Wei, R., & Zhao, X. (2022). Media-based investor sentiment and stock returns: a textual analysis based on newspapers. Applied economics, 54(7), 774-792. https://doi.org/10.1080/00036846.2021.1966369
  • Hirshleifer, D. (2015). Behavioral finance. Annual Review of Financial Economics, 7(1), 133-159. https://doi.org/10.1146/annurev-financial-092214-043752
  • Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4), 330-338. https://doi.org/10.1016/j.jksues.2016.04.002
  • Jiang, S., & Jin, X. (2021). Effects of investor sentiment on stock return volatility: A spatio-temporal dynamic panel model. Economic Modelling, 97, 298-306. https://doi.org/10.1016/j.econmod.2020.04.002
  • Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., & Antuchevičienė, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. 50, 25-44. https://etalpykla.vilniustech.lt/handle/123456789/116529
  • Khan, A., Goodell, J. W., Hassan, M. K., & Paltrinieri, A. (2022). A bibliometric review of finance bibliometric papers. Finance Research Letters, 47, 102520. https://doi.org/10.1016/j.frl.2021.102520
  • Khan, A., Hassan, M. K., Paltrinieri, A., Dreassi, A., & Bahoo, S. (2020). A bibliometric review of takaful literature. International Review of Economics & Finance, 69, 389-405. https://doi.org/10.1016/j.iref.2020.05.013
  • Kumar, S., Pandey, N., Burton, B., & Sureka, R. (2021). Research patterns and intellectual structure of Managerial Auditing Journal: a retrospective using bibliometric analysis during 1986-2019. Managerial Auditing Journal, 36(2), 280-313. https://doi.org/10.1108/MAJ-12-2019-2517
  • Kumar, S., Rao, S., Goyal, K., & Goyal, N. (2022). Journal of Behavioral and Experimental Finance: A bibliometric overview. Journal of Behavioral and Experimental Finance, 34, 100652. https://doi.org/10.1016/j.jbef.2022.100652
  • Levine-Clark, M., & Gil, E. L. (2008). A comparative citation analysis of Web of Science, Scopus, and Google Scholar. Journal of Business & Finance Librarianship, 14(1), 32-46. https://doi.org/10.1080/08963560802176348
  • Malathy, S., & Kantha, P. (2015). Journal of Spacecraft Technology: A Bibliometric Study. Journal of Information and Knowledge, 141-151. https://doi.org/10.17821/srels/2015/v52i2/61969
  • Massa, M., & Yadav, V. (2015). Investor sentiment and mutual fund strategies. Journal of Financial and Quantitative Analysis, 50(4), 699-727. https://doi.org/10.1017/S0022109015000253
  • Mukherjee, D., Kumar, S., Mukherjee, D., & Goyal, K. (2022). Mapping five decades of international business and management research on India: A bibliometric analysis and future directions. Journal of Business Research, 145, 864-891. https://doi.org/10.1016/j.jbusres.2022.03.011
  • Obaid, K., & Pukthuanthong, K. (2022). A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news. Journal of Financial Economics, 144(1), 273-297. https://doi.org/10.1016/j.jfineco.2021.06.002
  • Patel, R., Goodell, J. W., Oriani, M. E., Paltrinieri, A., & Yarovaya, L. (2022). A bibliometric review of financial market integration literature. International Review of Financial Analysis, 80, 102035. https://doi.org/10.1016/j.irfa.2022.102035
  • Paul, J., & Criado, A. R. (2020). The art of writing literature review: What do we know and what do we need to know?. International business review, 29(4), 101717. https://doi.org/10.1016/j.ibusrev.2020.101717
  • Prasad, S., Mohapatra, S., Rahman, M. R., & Puniyani, A. (2022). Investor sentiment index: a systematic review. International Journal of Financial Studies, 11(1), 6. https://doi.org/10.3390/ijfs11010006
  • Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of documentation, 25, 348.
  • Qazi, A., Raj, R. G., Hardaker, G., & Standing, C. (2017). A systematic literature review on opinion types and sentiment analysis techniques: Tasks and challenges. Internet Research, 27(3), 608-630. https://doi.org/10.1108/IntR-04-2016-0086
  • Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-based systems, 89, 14-46. https://doi.org/10.1016/j.knosys.2015.06.015
  • Rialp, A., Merigó, J. M., Cancino, C. A., & Urbano, D. (2019). Twenty-five years (1992–2016) of the International Business Review: A bibliometric overview. International Business Review, 28(6), 101587. https://doi.org/10.1016/j.ibusrev.2019.101587
  • Strydom, N., & Els, G. (2016). A bibliometric analysis of the Journal of Economic and Financial Sciences (2007-2016). Journal of Economic and Financial Sciences, 9(3), 951-974. https://hdl.handle.net/10520/EJC198835
  • Sun, Y., Zeng, X., Zhou, S., Zhao, H., Thomas, P., & Hu, H. (2021). What investors say is what the market says: measuring China’s real investor sentiment. Personal and Ubiquitous Computing, 25, 587-599. https://doi.org/10.1007/s00779-021-01542-3
  • Sureka, R., Kumar, S., Colombage, S., & Abedin, M. Z. (2022). Five decades of research on capital budgeting–A systematic review and future research agenda. Research in International Business and Finance, 60, 101609. https://doi.org/10.1016/j.ribaf.2021.101609
  • Syed, A. M., & Bawazir, H. S. (2021). Recent trends in business financial risk–A bibliometric analysis. Cogent Economics & Finance, 9(1), 1913877. https://doi.org/10.1080/23322039.2021.1913877
  • Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of finance, 62(3), 1139-1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x
  • Viglia, G., Kumar, S., Pandey, N., & Joshi, Y. (2022). Forty years of The Service Industries Journal: a bibliometric review. The Service Industries Journal, 42(1-2), 1-20. https://doi.org/10.1080/02642069.2021.2003341
  • Wen, M. (2024). Determinants of Financing Constraints: A Bibliometric Analysis. European Journal of Business and Management Research, 9(4), 37-41. Doi: 10.24018/ejbmr.2024.9.4.2386
  • Xiong, X., Meng, Y., Li, X., & Shen, D. (2020). Can overnight return really serve as a proxy for firm-specific investor sentiment? Cross-country evidence. Journal of International Financial Markets, Institutions and Money, 64, 101173. https://doi.org/10.1016/j.intfin.2019.101173
  • Xu, X., Chen, X., Jia, F., Brown, S., Gong, Y., & Xu, Y. (2018). Supply chain finance: A systematic literature review and bibliometric analysis. International Journal of Production Economics, 204, 160-173. https://doi.org/10.1016/j.ijpe.2018.08.003

Yıl 2025, Sayı: 70, 134 - 151, 21.12.2025
https://doi.org/10.53568/yyusbed.1609112

Öz

Kaynakça

  • Akyüz, N. E. (2025). Yönetim ve organizasyon literatürünün bibliyometrik analizi: Akademik eğilimler ve gelecek perspektifleri. In S. Çavuşoğlu (Ed.), Yönetim ve organizasyon alanında uluslararası araştırmalar–IV (pp. 257–340). Eğitim Yayınevi.
  • Bahoo, S. (2020). Corruption in banks: A bibliometric review and agenda. Finance Research Letters, 35, 101499. https://doi.org/10.1016/j.frl.2020.101499
  • Baker, H. K., Kumar, S., & Pandey, N. (2021). Thirty years of the Global Finance Journal: A bibliometric analysis. Global Finance Journal, 47, 100492. https://doi.org/10.1016/j.gfj.2019.100492
  • Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The journal of Finance, 61(4), 1645-1680. https://doi.org/10.1111/j.1540-6261.2006.00885.
  • Blasco, N., Corredor, P., & Ferrer, E. (2018). Analysts herding: When does sentiment matter?. Applied Economics, 50(51), 5495-5509. https://doi.org/10.1080/00036846.2018.1486999
  • Bui, T. D., Ali, M. H., Tsai, F. M., Iranmanesh, M., Tseng, M. L., & Lim, M. K. (2020). Challenges and trends in sustainable corporate finance: A bibliometric systematic review. Journal of Risk and Financial Management, 13(11), 264. https://doi.org/10.3390/jrfm13110264
  • Chen, X., & Xie, H. (2020). A structural topic modeling-based bibliometric study of sentiment analysis literature. Cognitive Computation, 12, 1097-1129. https://doi.org/10.1007/s12559-020-09745-1
  • Choijil, E., Méndez, C. E., Wong, W. K., Vieito, J. P., & Batmunkh, M. U. (2022). Thirty years of herd behavior in financial markets: A bibliometric analysis. Research in International Business and Finance, 59, 101506. https://doi.org/10.1016/j.ribaf.2021.101506
  • Cobo, M. J., López‐Herrera, A. G., Herrera‐Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for information Science and Technology, 62(7), 1382-1402. https://doi.org/10.1002/asi.21525
  • Costa, D. F., de Melo Carvalho, F., de Melo Moreira, B. C., & do Prado, J. W. (2017). Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias. Scientometrics, 111, 1775-1799. https://doi.org/10.1007/s11192-017-2371-5
  • Da, Z., Engelberg, J., & Gao, P. (2015). The sum of all FEARS investor sentiment and asset prices. The Review of Financial Studies, 28(1), 1-32. https://doi.org/10.1093/rfs/hhu072
  • Demir, G., Chatterjee, P., & Pamucar, D. (2024). Sensitivity analysis in multi-criteria decision making: A state-of-the-art research perspective using bibliometric analysis. Expert Systems with Applications, 237, 121660. https://doi.org/10.1016/j.eswa.2023.121660
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of business research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
  • Donthu, N., Kumar, S., Pattnaik, D., & Campagna, C. (2020). Journal of marketing theory and practice: A retrospective of 2005–2019. Journal of Marketing Theory and Practice, 28(2), 117-137. https://doi.org/10.1080/10696679.2020.1723424
  • Drago, C., Gatto, A., & Ruggeri, M. (2023). Telemedicine as technoinnovation to tackle COVID-19: A bibliometric analysis. Technovation, 120, 102417. https://doi.org/10.1016/j.technovation.2021.102417
  • Eachempati, P., & Srivastava, P. R. (2021). Accounting for unadjusted news sentiment for asset pricing. Qualitative Research in Financial Markets, 13(3), 383-422. https://doi.org/10.1108/QRFM-11-2019-0130
  • Ferretti, F., Saltelli, A., & Tarantola, S. (2016). Trends in sensitivity analysis practice in the last decade. Science of the total environment, 568, 666-670. https://doi.org/10.1016/j.scitotenv.2016.02.133
  • Garcia-Machado, J. J. (2018). The latest streams in finance research: An updated bibliometric mapping based on co-occurrence data. In Forum Scientiae Oeconomia (Vol. 6, No. 3, pp. 7-25). Wydawnictwo Naukowe Akademii WSB.
  • Garg, D., & Tiwari, P. (2021). Impact of social media sentiments in stock market predictions: A bibliometric analysis. Business Information Review, 38(4), 170-182. https://doi.org/10.1177/0266382121105866
  • Gong, X., Zhang, W., Wang, J., & Wang, C. (2022). Investor sentiment and stock volatility: New evidence. International Review of Financial Analysis, 80, 102028. https://doi.org/10.1016/j.irfa.2022.102028
  • Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577
  • Goyal, K., & Kumar, S. (2021). Financial literacy: A systematic review and bibliometric analysis. International Journal of Consumer Studies, 45(1), 80-105. https://doi.org/10.1111/ijcs.12605
  • Goyal, K., & Kumar, S. (2021). Financial literacy: A systematic review and bibliometric analysis. International Journal of Consumer Studies, 45(1), 80-105. https://doi.org/10.1111/ijcs.12605
  • Gupta, R., Nel, J., & Pierdzioch, C. (2023). Investor confidence and forecastability of US stock market realized volatility: Evidence from machine learning. Journal of Behavioral Finance, 24(1), 111-122. https://doi.org/10.1080/15427560.2021.1949719
  • He, Y., Qu, L., Wei, R., & Zhao, X. (2022). Media-based investor sentiment and stock returns: a textual analysis based on newspapers. Applied economics, 54(7), 774-792. https://doi.org/10.1080/00036846.2021.1966369
  • Hirshleifer, D. (2015). Behavioral finance. Annual Review of Financial Economics, 7(1), 133-159. https://doi.org/10.1146/annurev-financial-092214-043752
  • Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4), 330-338. https://doi.org/10.1016/j.jksues.2016.04.002
  • Jiang, S., & Jin, X. (2021). Effects of investor sentiment on stock return volatility: A spatio-temporal dynamic panel model. Economic Modelling, 97, 298-306. https://doi.org/10.1016/j.econmod.2020.04.002
  • Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., & Antuchevičienė, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. 50, 25-44. https://etalpykla.vilniustech.lt/handle/123456789/116529
  • Khan, A., Goodell, J. W., Hassan, M. K., & Paltrinieri, A. (2022). A bibliometric review of finance bibliometric papers. Finance Research Letters, 47, 102520. https://doi.org/10.1016/j.frl.2021.102520
  • Khan, A., Hassan, M. K., Paltrinieri, A., Dreassi, A., & Bahoo, S. (2020). A bibliometric review of takaful literature. International Review of Economics & Finance, 69, 389-405. https://doi.org/10.1016/j.iref.2020.05.013
  • Kumar, S., Pandey, N., Burton, B., & Sureka, R. (2021). Research patterns and intellectual structure of Managerial Auditing Journal: a retrospective using bibliometric analysis during 1986-2019. Managerial Auditing Journal, 36(2), 280-313. https://doi.org/10.1108/MAJ-12-2019-2517
  • Kumar, S., Rao, S., Goyal, K., & Goyal, N. (2022). Journal of Behavioral and Experimental Finance: A bibliometric overview. Journal of Behavioral and Experimental Finance, 34, 100652. https://doi.org/10.1016/j.jbef.2022.100652
  • Levine-Clark, M., & Gil, E. L. (2008). A comparative citation analysis of Web of Science, Scopus, and Google Scholar. Journal of Business & Finance Librarianship, 14(1), 32-46. https://doi.org/10.1080/08963560802176348
  • Malathy, S., & Kantha, P. (2015). Journal of Spacecraft Technology: A Bibliometric Study. Journal of Information and Knowledge, 141-151. https://doi.org/10.17821/srels/2015/v52i2/61969
  • Massa, M., & Yadav, V. (2015). Investor sentiment and mutual fund strategies. Journal of Financial and Quantitative Analysis, 50(4), 699-727. https://doi.org/10.1017/S0022109015000253
  • Mukherjee, D., Kumar, S., Mukherjee, D., & Goyal, K. (2022). Mapping five decades of international business and management research on India: A bibliometric analysis and future directions. Journal of Business Research, 145, 864-891. https://doi.org/10.1016/j.jbusres.2022.03.011
  • Obaid, K., & Pukthuanthong, K. (2022). A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news. Journal of Financial Economics, 144(1), 273-297. https://doi.org/10.1016/j.jfineco.2021.06.002
  • Patel, R., Goodell, J. W., Oriani, M. E., Paltrinieri, A., & Yarovaya, L. (2022). A bibliometric review of financial market integration literature. International Review of Financial Analysis, 80, 102035. https://doi.org/10.1016/j.irfa.2022.102035
  • Paul, J., & Criado, A. R. (2020). The art of writing literature review: What do we know and what do we need to know?. International business review, 29(4), 101717. https://doi.org/10.1016/j.ibusrev.2020.101717
  • Prasad, S., Mohapatra, S., Rahman, M. R., & Puniyani, A. (2022). Investor sentiment index: a systematic review. International Journal of Financial Studies, 11(1), 6. https://doi.org/10.3390/ijfs11010006
  • Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of documentation, 25, 348.
  • Qazi, A., Raj, R. G., Hardaker, G., & Standing, C. (2017). A systematic literature review on opinion types and sentiment analysis techniques: Tasks and challenges. Internet Research, 27(3), 608-630. https://doi.org/10.1108/IntR-04-2016-0086
  • Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-based systems, 89, 14-46. https://doi.org/10.1016/j.knosys.2015.06.015
  • Rialp, A., Merigó, J. M., Cancino, C. A., & Urbano, D. (2019). Twenty-five years (1992–2016) of the International Business Review: A bibliometric overview. International Business Review, 28(6), 101587. https://doi.org/10.1016/j.ibusrev.2019.101587
  • Strydom, N., & Els, G. (2016). A bibliometric analysis of the Journal of Economic and Financial Sciences (2007-2016). Journal of Economic and Financial Sciences, 9(3), 951-974. https://hdl.handle.net/10520/EJC198835
  • Sun, Y., Zeng, X., Zhou, S., Zhao, H., Thomas, P., & Hu, H. (2021). What investors say is what the market says: measuring China’s real investor sentiment. Personal and Ubiquitous Computing, 25, 587-599. https://doi.org/10.1007/s00779-021-01542-3
  • Sureka, R., Kumar, S., Colombage, S., & Abedin, M. Z. (2022). Five decades of research on capital budgeting–A systematic review and future research agenda. Research in International Business and Finance, 60, 101609. https://doi.org/10.1016/j.ribaf.2021.101609
  • Syed, A. M., & Bawazir, H. S. (2021). Recent trends in business financial risk–A bibliometric analysis. Cogent Economics & Finance, 9(1), 1913877. https://doi.org/10.1080/23322039.2021.1913877
  • Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of finance, 62(3), 1139-1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x
  • Viglia, G., Kumar, S., Pandey, N., & Joshi, Y. (2022). Forty years of The Service Industries Journal: a bibliometric review. The Service Industries Journal, 42(1-2), 1-20. https://doi.org/10.1080/02642069.2021.2003341
  • Wen, M. (2024). Determinants of Financing Constraints: A Bibliometric Analysis. European Journal of Business and Management Research, 9(4), 37-41. Doi: 10.24018/ejbmr.2024.9.4.2386
  • Xiong, X., Meng, Y., Li, X., & Shen, D. (2020). Can overnight return really serve as a proxy for firm-specific investor sentiment? Cross-country evidence. Journal of International Financial Markets, Institutions and Money, 64, 101173. https://doi.org/10.1016/j.intfin.2019.101173
  • Xu, X., Chen, X., Jia, F., Brown, S., Gong, Y., & Xu, Y. (2018). Supply chain finance: A systematic literature review and bibliometric analysis. International Journal of Production Economics, 204, 160-173. https://doi.org/10.1016/j.ijpe.2018.08.003

Yıl 2025, Sayı: 70, 134 - 151, 21.12.2025
https://doi.org/10.53568/yyusbed.1609112

Öz

Kaynakça

  • Akyüz, N. E. (2025). Yönetim ve organizasyon literatürünün bibliyometrik analizi: Akademik eğilimler ve gelecek perspektifleri. In S. Çavuşoğlu (Ed.), Yönetim ve organizasyon alanında uluslararası araştırmalar–IV (pp. 257–340). Eğitim Yayınevi.
  • Bahoo, S. (2020). Corruption in banks: A bibliometric review and agenda. Finance Research Letters, 35, 101499. https://doi.org/10.1016/j.frl.2020.101499
  • Baker, H. K., Kumar, S., & Pandey, N. (2021). Thirty years of the Global Finance Journal: A bibliometric analysis. Global Finance Journal, 47, 100492. https://doi.org/10.1016/j.gfj.2019.100492
  • Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The journal of Finance, 61(4), 1645-1680. https://doi.org/10.1111/j.1540-6261.2006.00885.
  • Blasco, N., Corredor, P., & Ferrer, E. (2018). Analysts herding: When does sentiment matter?. Applied Economics, 50(51), 5495-5509. https://doi.org/10.1080/00036846.2018.1486999
  • Bui, T. D., Ali, M. H., Tsai, F. M., Iranmanesh, M., Tseng, M. L., & Lim, M. K. (2020). Challenges and trends in sustainable corporate finance: A bibliometric systematic review. Journal of Risk and Financial Management, 13(11), 264. https://doi.org/10.3390/jrfm13110264
  • Chen, X., & Xie, H. (2020). A structural topic modeling-based bibliometric study of sentiment analysis literature. Cognitive Computation, 12, 1097-1129. https://doi.org/10.1007/s12559-020-09745-1
  • Choijil, E., Méndez, C. E., Wong, W. K., Vieito, J. P., & Batmunkh, M. U. (2022). Thirty years of herd behavior in financial markets: A bibliometric analysis. Research in International Business and Finance, 59, 101506. https://doi.org/10.1016/j.ribaf.2021.101506
  • Cobo, M. J., López‐Herrera, A. G., Herrera‐Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for information Science and Technology, 62(7), 1382-1402. https://doi.org/10.1002/asi.21525
  • Costa, D. F., de Melo Carvalho, F., de Melo Moreira, B. C., & do Prado, J. W. (2017). Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias. Scientometrics, 111, 1775-1799. https://doi.org/10.1007/s11192-017-2371-5
  • Da, Z., Engelberg, J., & Gao, P. (2015). The sum of all FEARS investor sentiment and asset prices. The Review of Financial Studies, 28(1), 1-32. https://doi.org/10.1093/rfs/hhu072
  • Demir, G., Chatterjee, P., & Pamucar, D. (2024). Sensitivity analysis in multi-criteria decision making: A state-of-the-art research perspective using bibliometric analysis. Expert Systems with Applications, 237, 121660. https://doi.org/10.1016/j.eswa.2023.121660
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of business research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
  • Donthu, N., Kumar, S., Pattnaik, D., & Campagna, C. (2020). Journal of marketing theory and practice: A retrospective of 2005–2019. Journal of Marketing Theory and Practice, 28(2), 117-137. https://doi.org/10.1080/10696679.2020.1723424
  • Drago, C., Gatto, A., & Ruggeri, M. (2023). Telemedicine as technoinnovation to tackle COVID-19: A bibliometric analysis. Technovation, 120, 102417. https://doi.org/10.1016/j.technovation.2021.102417
  • Eachempati, P., & Srivastava, P. R. (2021). Accounting for unadjusted news sentiment for asset pricing. Qualitative Research in Financial Markets, 13(3), 383-422. https://doi.org/10.1108/QRFM-11-2019-0130
  • Ferretti, F., Saltelli, A., & Tarantola, S. (2016). Trends in sensitivity analysis practice in the last decade. Science of the total environment, 568, 666-670. https://doi.org/10.1016/j.scitotenv.2016.02.133
  • Garcia-Machado, J. J. (2018). The latest streams in finance research: An updated bibliometric mapping based on co-occurrence data. In Forum Scientiae Oeconomia (Vol. 6, No. 3, pp. 7-25). Wydawnictwo Naukowe Akademii WSB.
  • Garg, D., & Tiwari, P. (2021). Impact of social media sentiments in stock market predictions: A bibliometric analysis. Business Information Review, 38(4), 170-182. https://doi.org/10.1177/0266382121105866
  • Gong, X., Zhang, W., Wang, J., & Wang, C. (2022). Investor sentiment and stock volatility: New evidence. International Review of Financial Analysis, 80, 102028. https://doi.org/10.1016/j.irfa.2022.102028
  • Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577
  • Goyal, K., & Kumar, S. (2021). Financial literacy: A systematic review and bibliometric analysis. International Journal of Consumer Studies, 45(1), 80-105. https://doi.org/10.1111/ijcs.12605
  • Goyal, K., & Kumar, S. (2021). Financial literacy: A systematic review and bibliometric analysis. International Journal of Consumer Studies, 45(1), 80-105. https://doi.org/10.1111/ijcs.12605
  • Gupta, R., Nel, J., & Pierdzioch, C. (2023). Investor confidence and forecastability of US stock market realized volatility: Evidence from machine learning. Journal of Behavioral Finance, 24(1), 111-122. https://doi.org/10.1080/15427560.2021.1949719
  • He, Y., Qu, L., Wei, R., & Zhao, X. (2022). Media-based investor sentiment and stock returns: a textual analysis based on newspapers. Applied economics, 54(7), 774-792. https://doi.org/10.1080/00036846.2021.1966369
  • Hirshleifer, D. (2015). Behavioral finance. Annual Review of Financial Economics, 7(1), 133-159. https://doi.org/10.1146/annurev-financial-092214-043752
  • Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4), 330-338. https://doi.org/10.1016/j.jksues.2016.04.002
  • Jiang, S., & Jin, X. (2021). Effects of investor sentiment on stock return volatility: A spatio-temporal dynamic panel model. Economic Modelling, 97, 298-306. https://doi.org/10.1016/j.econmod.2020.04.002
  • Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., & Antuchevičienė, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. 50, 25-44. https://etalpykla.vilniustech.lt/handle/123456789/116529
  • Khan, A., Goodell, J. W., Hassan, M. K., & Paltrinieri, A. (2022). A bibliometric review of finance bibliometric papers. Finance Research Letters, 47, 102520. https://doi.org/10.1016/j.frl.2021.102520
  • Khan, A., Hassan, M. K., Paltrinieri, A., Dreassi, A., & Bahoo, S. (2020). A bibliometric review of takaful literature. International Review of Economics & Finance, 69, 389-405. https://doi.org/10.1016/j.iref.2020.05.013
  • Kumar, S., Pandey, N., Burton, B., & Sureka, R. (2021). Research patterns and intellectual structure of Managerial Auditing Journal: a retrospective using bibliometric analysis during 1986-2019. Managerial Auditing Journal, 36(2), 280-313. https://doi.org/10.1108/MAJ-12-2019-2517
  • Kumar, S., Rao, S., Goyal, K., & Goyal, N. (2022). Journal of Behavioral and Experimental Finance: A bibliometric overview. Journal of Behavioral and Experimental Finance, 34, 100652. https://doi.org/10.1016/j.jbef.2022.100652
  • Levine-Clark, M., & Gil, E. L. (2008). A comparative citation analysis of Web of Science, Scopus, and Google Scholar. Journal of Business & Finance Librarianship, 14(1), 32-46. https://doi.org/10.1080/08963560802176348
  • Malathy, S., & Kantha, P. (2015). Journal of Spacecraft Technology: A Bibliometric Study. Journal of Information and Knowledge, 141-151. https://doi.org/10.17821/srels/2015/v52i2/61969
  • Massa, M., & Yadav, V. (2015). Investor sentiment and mutual fund strategies. Journal of Financial and Quantitative Analysis, 50(4), 699-727. https://doi.org/10.1017/S0022109015000253
  • Mukherjee, D., Kumar, S., Mukherjee, D., & Goyal, K. (2022). Mapping five decades of international business and management research on India: A bibliometric analysis and future directions. Journal of Business Research, 145, 864-891. https://doi.org/10.1016/j.jbusres.2022.03.011
  • Obaid, K., & Pukthuanthong, K. (2022). A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news. Journal of Financial Economics, 144(1), 273-297. https://doi.org/10.1016/j.jfineco.2021.06.002
  • Patel, R., Goodell, J. W., Oriani, M. E., Paltrinieri, A., & Yarovaya, L. (2022). A bibliometric review of financial market integration literature. International Review of Financial Analysis, 80, 102035. https://doi.org/10.1016/j.irfa.2022.102035
  • Paul, J., & Criado, A. R. (2020). The art of writing literature review: What do we know and what do we need to know?. International business review, 29(4), 101717. https://doi.org/10.1016/j.ibusrev.2020.101717
  • Prasad, S., Mohapatra, S., Rahman, M. R., & Puniyani, A. (2022). Investor sentiment index: a systematic review. International Journal of Financial Studies, 11(1), 6. https://doi.org/10.3390/ijfs11010006
  • Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of documentation, 25, 348.
  • Qazi, A., Raj, R. G., Hardaker, G., & Standing, C. (2017). A systematic literature review on opinion types and sentiment analysis techniques: Tasks and challenges. Internet Research, 27(3), 608-630. https://doi.org/10.1108/IntR-04-2016-0086
  • Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-based systems, 89, 14-46. https://doi.org/10.1016/j.knosys.2015.06.015
  • Rialp, A., Merigó, J. M., Cancino, C. A., & Urbano, D. (2019). Twenty-five years (1992–2016) of the International Business Review: A bibliometric overview. International Business Review, 28(6), 101587. https://doi.org/10.1016/j.ibusrev.2019.101587
  • Strydom, N., & Els, G. (2016). A bibliometric analysis of the Journal of Economic and Financial Sciences (2007-2016). Journal of Economic and Financial Sciences, 9(3), 951-974. https://hdl.handle.net/10520/EJC198835
  • Sun, Y., Zeng, X., Zhou, S., Zhao, H., Thomas, P., & Hu, H. (2021). What investors say is what the market says: measuring China’s real investor sentiment. Personal and Ubiquitous Computing, 25, 587-599. https://doi.org/10.1007/s00779-021-01542-3
  • Sureka, R., Kumar, S., Colombage, S., & Abedin, M. Z. (2022). Five decades of research on capital budgeting–A systematic review and future research agenda. Research in International Business and Finance, 60, 101609. https://doi.org/10.1016/j.ribaf.2021.101609
  • Syed, A. M., & Bawazir, H. S. (2021). Recent trends in business financial risk–A bibliometric analysis. Cogent Economics & Finance, 9(1), 1913877. https://doi.org/10.1080/23322039.2021.1913877
  • Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of finance, 62(3), 1139-1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x
  • Viglia, G., Kumar, S., Pandey, N., & Joshi, Y. (2022). Forty years of The Service Industries Journal: a bibliometric review. The Service Industries Journal, 42(1-2), 1-20. https://doi.org/10.1080/02642069.2021.2003341
  • Wen, M. (2024). Determinants of Financing Constraints: A Bibliometric Analysis. European Journal of Business and Management Research, 9(4), 37-41. Doi: 10.24018/ejbmr.2024.9.4.2386
  • Xiong, X., Meng, Y., Li, X., & Shen, D. (2020). Can overnight return really serve as a proxy for firm-specific investor sentiment? Cross-country evidence. Journal of International Financial Markets, Institutions and Money, 64, 101173. https://doi.org/10.1016/j.intfin.2019.101173
  • Xu, X., Chen, X., Jia, F., Brown, S., Gong, Y., & Xu, Y. (2018). Supply chain finance: A systematic literature review and bibliometric analysis. International Journal of Production Economics, 204, 160-173. https://doi.org/10.1016/j.ijpe.2018.08.003

Finansal Duyarlılık Konusundaki Yayınların Haritalaması: Bibliyometrik Bir Analiz

Yıl 2025, Sayı: 70, 134 - 151, 21.12.2025
https://doi.org/10.53568/yyusbed.1609112

Öz

Bu çalışmanın amacı, finansal piyasalarda duyarlılık analizine yönelik akademik literatürü bibliyometrik analiz tekniği ile kapsamlı bir şekilde incelemektir. Çalışma, 17.11.2024 tarihinde Web of Science veri tabanı üzerinden elde edilen finansal duyarlılık konusundaki 26.084 araştırma makalesi üzerinden gerçekleştirilmiştir. Veri analizi için VOSviewer bibliyometrik yazılımı kullanılmış olup ortak yazarlık, yazar atıf ilişkileri, ülkeler arası yayın ve atıf ilişkileri, kurumlar arası yayın ve atıf ilişkileri, yazarların yaygın olarak kullandığı anahtar kelimeler ve ilişkileri yayınlar arası bibliyografik atıf ağları, akademik dergilerin bilimsel yayılımı ve atıf ilişkileri incelenmiştir. Çalışma bulguları ABD, Çin ve Avrupa ülkelerinin finansal duyarlılık konusundaki akademik katkılarda lider olduğunu, Çin’in son yıllarda bu alandaki etkisinin giderek arttığını göstermektedir. Harvard Üniversitesi ve Çin Bilim Akademisi gibi kurumlar en etkili akademik kuruluşlar olarak öne çıkarken Biswajit Sarkar, Rangan Gupta, Ship Sankar Sana ve Wang Wei gibi yazarlar bilimsel iş birliği ağlarının merkezinde yer almıştır. Journal of Financial Economics, Applied Energy, Journal of Cleaner Production ve PLOS ONE gibi dergiler finansal duyarlılık konusundaki çalışmalarda öne çıkan yayın platformlarıdır. Bulgular, kriz dönemlerine odaklanan araştırmaların ve davranışsal finans teorilerinin bu alandaki önemini vurgulamakta, finansal duyarlılık alanında artan akademik etkileri ve disiplinler arası iş birliğini ortaya koymaktadır. Finansal duyarlılık konusundaki araştırmaların bibliyografik profilini ortaya koyan bu tür çalışmaların gelecekte yapılacak araştırmalara katkı sağlayacağı düşünülmektedir.

Kaynakça

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  • Syed, A. M., & Bawazir, H. S. (2021). Recent trends in business financial risk–A bibliometric analysis. Cogent Economics & Finance, 9(1), 1913877. https://doi.org/10.1080/23322039.2021.1913877
  • Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of finance, 62(3), 1139-1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x
  • Viglia, G., Kumar, S., Pandey, N., & Joshi, Y. (2022). Forty years of The Service Industries Journal: a bibliometric review. The Service Industries Journal, 42(1-2), 1-20. https://doi.org/10.1080/02642069.2021.2003341
  • Wen, M. (2024). Determinants of Financing Constraints: A Bibliometric Analysis. European Journal of Business and Management Research, 9(4), 37-41. Doi: 10.24018/ejbmr.2024.9.4.2386
  • Xiong, X., Meng, Y., Li, X., & Shen, D. (2020). Can overnight return really serve as a proxy for firm-specific investor sentiment? Cross-country evidence. Journal of International Financial Markets, Institutions and Money, 64, 101173. https://doi.org/10.1016/j.intfin.2019.101173
  • Xu, X., Chen, X., Jia, F., Brown, S., Gong, Y., & Xu, Y. (2018). Supply chain finance: A systematic literature review and bibliometric analysis. International Journal of Production Economics, 204, 160-173. https://doi.org/10.1016/j.ijpe.2018.08.003
Toplam 54 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İktisat Sosyolojisi
Bölüm Araştırma Makalesi
Yazarlar

Nuray Yuzbaşıoğlu 0000-0001-7409-4263

Gönderilme Tarihi 28 Aralık 2024
Kabul Tarihi 4 Kasım 2025
Yayımlanma Tarihi 21 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Sayı: 70

Kaynak Göster

APA Yuzbaşıoğlu, N. (2025). Finansal Duyarlılık Konusundaki Yayınların Haritalaması: Bibliyometrik Bir Analiz. Yüzüncü Yıl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(70), 134-151. https://doi.org/10.53568/yyusbed.1609112

Yüzüncü Yıl Üniversitesi Sosyal Bilimler Dergisi Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.