2,736
Views
7
CrossRef citations to date
0
Altmetric
Articles

Knowledge mapping of credit risk research: scientometrics analysis using CiteSpace

ORCID Icon, &
Pages 3457-3484 | Received 18 Mar 2019, Accepted 05 Jul 2019, Published online: 20 Sep 2019

References

  • Adler, M., & Song, J. (2010). The behavior of emerging market sovereigns’ credit default swap premiums and bond yield spreads. International Journal of Finance & Economics, 15, 31–58. doi:10.1002/ijfe.408
  • Ammer, J., & Cai, F. (2011). Sovereign CDS and bond pricing dynamics in emerging markets: Does the cheapest-to-deliver option matter? Journal of International Financial Markets Institutions and Money, 21, 369–387. doi:10.1016/j.intfin.2011.01.001
  • Ang, A., & Longstaff, F. A. (2013). Systemic sovereign credit risk: Lessons from the US and Europe. Journal of Monetary Economics, 60, 493–510. doi:10.1016/j.jmoneco.2013.04.009
  • Angilella, S., & Mazzu, S. (2015). The financing of innovative SMEs: A multicriteria credit rating model. European Journal of Operational Research, 244, 540–554. doi:10.1016/j.ejor.2015.01.033
  • Annaert, J., Ceuster, M. D., Roy, P. V., & Vespro, C. (2013). What determines Euro area bank CDS spreads? Journal of International Money and Finance, 32, 444–461. doi:10.1016/j.jimonfin.2012.05.029
  • Arce, O., Mayordomo, S., & Pena, J. I. (2013). Credit-risk valuation in the sovereign CDS and bonds markets: Evidence from the euro area crisis. Journal of International Money and Finance, 35, 124–145. doi:10.1016/j.jimonfin.2013.01.006
  • Arnold, M., Wagner, A. F., & Westermann, R. (2013). Growth options, macroeconomic conditions, and the cross section of credit risk. Journal of Financial Economics, 107, 350–385. doi:10.1016/j.jfineco.2012.08.017
  • Arora, N. (2013). Discussion of “Financial statement comparability and credit risk”. Review of Accounting Studies, 18, 824–832. doi:10.1007/s11142-013-9239-6
  • Arora, N., Gandhi, P., & Longstaff, F. A. (2012). Counterparty credit risk and the credit default swap market. Journal of Financial Economics, 103, 280–293. doi:10.1016/j.jfineco.2011.10.001
  • Azizpour, S., Giesecke, K., & Kim, B. (2011). Premia for correlated default risk. Journal of Economic Dynamics and Control, 35, 1340–1357. doi:10.1016/j.jedc.2011.03.010
  • Baesens, B., Setiono, R., Mues, C., & Vanthienen, J. (2003). Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science, 49, 312–329. doi:10.1287/mnsc.49.3.312.12739
  • Bakshi, G., Madan, D., & Zhang, F. X. L. (2006). Investigating the role of systematic and firm-specific factors in default risk: Lessons from empirically evaluating credit risk models. The Journal of Business, 79, 1955–1987. doi:10.1086/503653
  • Bao, J., Pan, J., & Wang, J. (2011). The illiquidity of corporate bonds. The Journal of Finance, 66, 911–946. doi:10.1111/j.1540-6261.2011.01655.x
  • Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417. doi:10.1016/j.eswa.2017.04.006
  • Batta, G. (2011). The direct relevance of accounting information for credit default swap pricing. Journal of Business Finance & Accounting, 38, 1096–1122. doi:10.1111/j.1468-5957.2011.02264.x
  • Beber, A., Brandt, M. W., & Kavajecz, K. A. (2009). Flight-to-quality or flight-to-liquidity? Evidence from the euro-area bond market. Review of Financial Studies, 22, 925–957. doi:10.1093/rfs/hhm088
  • Beque, A., & Lessmann, S. (2017). Extreme learning machines for credit scoring: An empirical evaluation. Expert Systems with Applications, 86, 42–53. doi:10.1016/j.eswa.2017.05.050
  • Beraldi, P., Simone, F. D., Violi, A., Consigli, G., & Iaquinta, G. (2012). Scenario-based dynamic corporate bond portfolio management. IMA Journal of Management Mathematics, 23, 341–364. doi:10.1093/imaman/dps017
  • Berg, T. (2010). From actual to risk-neutral default probabilities: Merton and beyond. The Journal of Credit Risk, 6, 55–86. doi:10.21314/JCR.2010.105
  • Bhamra, H. S., Kuehn, L. A., & Strebulaev, I. A. (2010). The levered equity risk premium and credit spreads: A unified framework. Review of Financial Studies, 23, 645–703. doi:10.1093/rfs/hhp082
  • Bharath, S. T., & Shumway, T. (2008). Forecasting default with the merton distance to default model. Review of Financial Studies, 21, 1339–1369. doi:10.1093/rfs/hhn044
  • Blanco, R., Brennan, S., & Marsh, I. W. (2005). An empirical analysis of the dynamic relation between investment-grade bonds and credit default swaps. The Journal of Finance, 60, 2255–2281. doi:10.1111/j.1540-6261.2005.00798.x
  • Brunnermeier, M. K. (2009). Deciphering the liquidity and credit crunch 2007-2008. Journal of Economic Perspectives, 23, 77–100. doi:10.1257/jep.23.1.77
  • Brunnermeier, M. K., & Pedersen, L. H. (2009). Market liquidity and funding liquidity. Review of Financial Studies, 22, 2201–2238. doi:10.1093/rfs/hhn098
  • Calice, G., Ioannidis, C., & Williams, J. (2012). Credit derivatives and the default risk of large complex financial institutions. Journal of Financial Services Research, 42, 85–107. doi:10.1007/s10693-011-0121-z
  • Callen, J. L., Livnat, J., & Segal, D. (2009). The impact of earnings on the pricing of credit default swaps. The Accounting Review, 84, 1363–1394. doi:10.2308/accr.2009.84.5.1363
  • Carr, P., & Wu, L. R. (2010). Stock options and credit default swaps: A joint framework for valuation and estimation. Journal of Financial Econometrics, 8, 409–449. doi:10.1093/jjfinec/nbp010
  • Cerrato, M., Crosby, J., Kim, M., & Zhao, Y. (2017). The joint credit risk of UK global-systemically important banks. Journal of Futures Markets, 37, 964–988. doi:10.1002/fut.21855
  • Chen, L., Lesmond, D. A., & Wei, J. (2007). Corporate yield spreads and bond liquidity. The Journal of Finance, 62, 119–149. doi:10.1111/j.1540-6261.2007.01203.x
  • Chen, R. R., Cheng, X. L., & Wu, L. R. (2013). Dynamic interactions between interest-rate and credit risk: Theory and evidence on the credit default swap term structure. Review of Finance, 17, 403–441. doi:10.1093/rof/rfr032
  • Chen, T. Q., He, J. M., & Li, X. D. (2016). An evolving network model of credit risk contagion in the financial market. Technological and Economic Development of Economy, 23, 22–37. doi:10.3846/20294913.2015.1095808
  • Crouhy, M., Galai, D., & Mark, R. (2000). A comparative analysis of current credit risk models. Journal of Banking & Finance, 24, 59–117. doi:10.1016/S0378-4266(99)00053-9
  • Dahiya, S., Handa, S. S., & Singh, N. P. (2017). A feature selection enabled hybrid-bagging algorithm for credit risk evaluation. Expert Systems, 34, e12217. doi:10.1111/exsy.12217
  • Darrell, D., & Singleton, K. J. (2003). Credit risk: Pricing, measurement, and management. New York: Princeton University Press.
  • Das, S. R., Duffie, D., Kapadia, N., & Saita, L. (2007). Common failings: How corporate defaults are correlated. The Journal of Finance, 62, 93–117. doi:10.1111/j.1540-6261.2007.01202.x
  • Duffie, D., Eckner, A., Horel, G., & Saita, L. (2009). Frailty correlated default. The Journal of Finance, 64, 2089–2123. doi:10.1111/j.1540-6261.2009.01495.x
  • Duffie, D., Saita, L., & Wang, K. (2007). Multi-period corporate default prediction with stochastic covariates. Journal of Financial Economics, 83, 635–665. doi:10.1016/j.jfineco.2005.10.011
  • Duffie, D., & Singleton, K. J. (1999). Modeling term structures of defaultable bonds. Review of Financial Studies, 12, 687–720. doi:10.1093/rfs/12.4.687
  • Emekter, R., Tu, Y. B., Jirasakuldech, B., & Lu, M. (2015). Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending. Applied Economics, 47, 54–70. doi:10.1080/00036846.2014.962222
  • Eom, Y. H., Helwege, J., & Huang, J. Z. (2004). Structural models of corporate bond pricing: An empirical analysis. Review of Financial Studies, 17, 499–544. doi:10.1093/rfs/hhg053
  • Ericsson, J., Jacobs, K., & Oviedo, R. (2009). The determinants of credit default swap premia. Journal of Financial and Quantitative Analysis, 44, 109–132. doi:10.1017/S0022109009090061
  • Fang, Y., Yin, J., & Wu, B. H. (2017). Climate change and tourism: A scientometric analysis using CiteSpace. Journal of Sustainable Tourism, 26, 108–126. doi:10.1080/09669582.2017.1329310
  • García-Céspedes, R., & Moreno, M. (2017). An approximate multi-period Vasicek credit risk model. Journal of Banking & Finance, 81, 105–113. doi:10.1016/j.jbankfin.2017.05.002
  • Gordy, M. B. (2000). A comparative anatomy of credit risk models. Journal of Banking & Finance, 24, 119–149. doi:10.1016/S0378-4266(99)00054-0
  • Gordy, M. B. (2003). A risk-factor model foundation for ratings-based bank capital rules. Journal of Financial Intermediation, 12, 199–232. doi:10.1016/S1042-9573(03)00040-8
  • Grobys, K., & Haga, J. (2015). The market price of credit risk and economic states. Empirical Economics, 50, 1111–1134. doi:10.1007/s00181-015-0952-9
  • Guo, Y. H., Zhou, W. J., Luo, C. Y., Liu, C. R., & Xiong, H. (2016). Instance-based credit risk assessment for investment decisions in P2P lending. European Journal of Operational Research, 249, 417–426. doi:10.1016/j.ejor.2015.05.050
  • Harris, T. (2015). Credit scoring using the clustered support vector machine. Expert Systems with Applications, 42, 741–750. doi:10.1016/j.eswa.2014.08.029
  • Hatchett, J. P. L., Li, X. D., & Wang, J. N. (2009). Credit contagion and credit risk. Quantitative Finance, 9, 373–382. doi:10.1080/14697680802464162
  • Iturriaga, F. J. L., & Sanz, I. P. (2015). Bankruptcy visualization and prediction using neural networks: A study of US commercial banks. Expert Systems with Applications, 42, 2857–2869. doi:10.1016/j.eswa.2014.11.025
  • Jarrow, R. A., Lando, D., & Turnbull, S. M. (1997). A markov model for the term structure of credit risk spreads. Review of Financial Studies, 10, 481–523. doi:10.1093/rfs/10.2.481
  • Jiang, Y., Hou, L., Shi, T., & Gui, Q. (2017). A review of urban planning research for climate change. Sustainability, 9, 2224. doi:10.3390/su9122224
  • Kim, M. C., & Chen, C. M. (2015). A scientometric review of emerging trends and new developments in recommendation systems. Scientometrics, 104, 239–263. doi:10.1007/s11192-015-1595-5
  • Kolbel, J. F., Busch, T., & Jancso, L. M. (2017). How media coverage of corporate social irresponsibility increases financial risk. Strategic Management Journal, 38, 2266–2284. doi:10.1002/smj.2647
  • Kou, G., Peng, Y., & Wang, G. X. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275(11), 1–12. doi:10.1016/j.ins.2014.02.137
  • Li, K., Niskanen, J., Kolehmainen, M., & Niskanen, M. (2016). Financial innovation: Credit default hybrid model for SME lending. Expert Systems with Applications, 61, 343–355. doi:10.1016/j.eswa.2016.05.029
  • Li, X. J., Ma, E., & Qu, H. L. (2017). Knowledge mapping of hospitality research–A visual analysis using CiteSpace. International Journal of Hospitality Management, 60, 77–93. doi:10.1016/j.ijhm.2016.10.006
  • Lin, W. Y., Hu, Y. H., & Tsai, C. F. (2012). Machine learning in financial crisis prediction: A survey. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, 42, 421–436.
  • Longstaff, F. A., Mithal, S., & Neis, E. (2005). Corporate yield spreads: Default risk or liquidity? New evidence from the credit default swap market. The Journal of Finance, 60, 2213–2253. doi:10.1111/j.1540-6261.2005.00797.x
  • Longstaff, F. A., Pan, J., Pedersen, L. H., & Singleton, K. J. (2011). How sovereign is sovereign credit risk? American Economic Journal: Macroeconomics, 3, 75–103. doi:10.1257/mac.3.2.75
  • Onan, A., Korukoğlu, S., & Bulut, H. (2016). A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification. Expert Systems with Applications, 62(15), 1–16. doi:10.1016/j.eswa.2016.06.005
  • Pan, J., & Singleton, K. J. (2008). Default and recovery implicit in the term structure of sovereign CDS spreads. The Journal of Finance, 63, 2345–2384. doi:10.1111/j.1540-6261.2008.01399.x
  • Petersen, M. A. (2009). Estimating standard errors in finance panel data sets: Comparing approaches. Review of Financial Studies, 22, 435–480. doi:10.1093/rfs/hhn053
  • Ruan, J. H., Chan, F. T. S., Zhu, F. W., Wang, X. P., & Yang, J. (2016). A visualization review of cloud computing algorithms in the last decade. Sustainability, 8, 1008. doi:10.3390/su8101008
  • Salim, R., Arjomandi, A., & Dakpo, K. H. (2016). Banks’ efficiency and credit risk analysis using by-production approach: The case of Iranian banks. Applied Economics, 49, 2974–2988. doi:10.1080/00036846.2016.1251567
  • Serrano-Cinca, C., Gutierrez-Nieto, B., & Lopez-Palacios, L. (2015). Determinants of default in P2P lending. PLos One, 10(10), e0139427. doi:10.1371/journal.pone.0139427
  • Small, H., Boyack, K. W., & Klavans, R. (2014). Identifying emerging topics in science and technology. Research Policy, 43, 1450–1467. doi:10.1016/j.respol.2014.02.005
  • Sun, J., Li, H., Huang, Q. H., & He, K. Y. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41–56. doi:10.1016/j.knosys.2013.12.006
  • Wang, X. L., Nathwani, J., & Wu, C. Y. (2016). Visualization of international energy policy research. Energies, 9, 72. doi:10.3390/en9020072
  • Woźniak, M., Graña, M., & Corchado, E. (2014). A survey of multiple classifier systems as hybrid systems. Information Fusion, 16, 3–17. doi:10.1016/j.inffus.2013.04.006
  • Wu, D. S. D., Olson, D. L., & Luo, C. C. (2014). A decision support approach for accounts receivable risk management. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44, 1624–1632. doi:10.1109/TSMC.2014.2318020
  • Xiang, C. Y., Wang, Y., & Liu, H. W. (2017). A scientometrics review on nonpoint source pollution research. Ecological Engineering, 99, 400–408. doi:10.1016/j.ecoleng.2016.11.028
  • Yu, D. J. (2015). A scientometrics review on aggregation operator research. Scientometrics, 105, 115–133. doi:10.1007/s11192-015-1695-2
  • Yu, D. J., & Xu, C. (2017). Mapping research on carbon emissions trading: A co-citation analysis. Renewable and Sustainable Energy Reviews, 74, 1314–1322. doi:10.1016/j.rser.2016.11.144
  • Yu, D. J., Xu, Z. S., Kao, Y. S., & Lin, C. T. (2018). The structure and citation landscape of IEEE transactions on fuzzy systems (1994-2015). IEEE Transactions on Fuzzy Systems, 26, 430–442. doi:10.1109/TFUZZ.2017.2672732
  • Zhang, B. Y., Zhou, H., & Zhu, H. B. (2009). Explaining credit default swap spreads with the equity volatility and jump risks of individual firms. Review of Financial Studies, 22, 5099–5131. doi:10.1093/rfs/hhp004
  • Zhu, Y., Xie, C., Sun, B., Wang, G. J., & Yan, X. G. (2016). Predicting China’s SME credit risk in supply chain financing by logistic regression, artificial neural network and hybrid models. Sustainability, 8, 433. doi:10.3390/su8050433
  • Zhu, Y., Xie, C., Wang, G. J., & Yan, X. G. (2016). Predicting China’s SME credit risk in supply chain finance based on machine learning methods. Entropy, 18, 195. doi:10.3390/e18050195
  • Zopounidis, C., Galariotis, E., Doumpos, M., Sarri, S., & Andriosopoulo, S. K. (2015). Multiple criteria decision aiding for finance: An updated bibliographic survey. European Journal of Operational Research, 247, 339–348. doi:10.1016/j.ejor.2015.05.032