Michal Tkac, Robert Verner


Purpose: Primary bond markets represent an interesting investment opportunity not only for banks, insurance companies, and other institutional investors, but also for individuals looking for capital gains. Since offered securities vary in terms of their rating, industrial classification, coupon, or maturity, demand of buyers for particular offerings often overcomes issued volume and price of given bond on secondary market consequently rises. Investors might be regarded as consumers purchasing required service according to their specific preferences at desired price. This paper aims at analysis of demand for bonds on primary market using artificial neural networks.

Design/methodology: We design a multilayered feedforward neural network trained by Levenberg-Marquardt algorithm in order to estimate demand for individual bonds based on parameters of particular offerings. Outcomes obtained by artificial neural network are compared with conventional econometric methods.

Findings: Our results indicate that artificial neural network significantly outperformed standard econometric techniques and on examined sample of primary bond offerings achieved considerably better performance in terms of prediction accuracy and mean squared error.

Originality: We show that proposed neural network is able to successfully predict demand for primary obligation offerings based on their specifications. Moreover, we identify relevant parameters of issues which are able to considerably affect total demand for given security.  Our findings might not only help investors to detect marketable securities, but also enable issuing entities to increase demand for their bonds in order to decrease their offering price.



Altı, A., 2005. IPO market timing. Review of Financial Studies, 18(3), pp.1105-1138.

Altunbaş, Y., Kara, A. and Marqués-Ibáñez, D., 2010. Large debt financing: syndicated loans versus corporate bonds. The European Journal of Finance, 16(5), pp.437-458.

An, H.H. and Chan, K.C., 2008. Credit ratings and IPO pricing. Journal of Corporate Finance, 14(5), pp.584-595.

Andres, C., Betzer, A. and Limbach, P., 2014. Underwriter reputation and the quality of certification: Evidence from high-yield bonds. Journal of Banking & Finance, 40, pp.97-115.

Baker, H.K. and Mansi, S.A., 2002. Assessing credit rating agencies by bond issuers and institutional investors. Journal of Business Finance & Accounting, 29(9‐10), pp.1367-1398.

Baker, M. and Wurgler, J., 2002. Market timing and capital structure. The journal of finance, 57(1), pp.1-32.

Beckman, J., Garnerb, J., Marshallc, B. and Okamurad, H., 2001. The influence of underwriter reputation, keiretsu affiliation, and financial health on the underpricing of Japanese IPOs. Pacific-Basin Finance Journal, 9(5), pp.513-534.

Booth, J. R. and Chua, L., 1996. Ownership dispersion, costly information, and IPO underpricing. Journal of Financial Economics, 41(2), pp.291-310.

Brennan, M.J. and Franks, J., 1997. Underpricing, ownership and control in initial public offerings of equity securities in the UK. Journal of Financial Economics, 45(3), pp.391-413.

Butler, A.W., O'Connor Keefe, M. and Kieschnick, R.L., 2013. Robust Determinants of IPO Underpricing and Their Implications for IPO Research. Journal of Corporate Finance, 27, pp.367-383.

Carter, R. and Manaster, S., 1990. Initial public offerings and underwriter reputation. The Journal of Finance, 45(4), pp.1045-1067.

Chen, Z., Wilhelm Jr. and W.J., 2008. A theory of the transition to secondary market trading of IPOs. Journal of Financial Economics, 90(3), pp.219-236.

Chua, A., 2014. Market conditions, underwriter reputation and first day return of IPOs. Journal of Financial Markets, 19, pp.131-153.

Cook, D.O., Kieschnick, R. and Van Ness, R.A., 2006. On the marketing of IPOs. Journal of Financial Economics, 82(1), pp.35-61.

Cybenko, G., 1989. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2(4), pp.303-314.

Danielova, A.N., Smart, S.B. and Boquist, J. 2010. What motivates exchangeable debt offerings? Journal of Corporate Finance, 16(2), pp.159-169.

Davydov, D., Nikkinen, J. and Vähämaa, S., 2013. Does the Decision to Issue Public Debt Affect Firm Valuation? Russian Evidence. Emerging Markets Review, 20 (September), pp.136-151.

Demers, E. and Lewellen, K., 2003. The marketing role of IPOs: evidence from internet stocks. Journal of Financial Economics, 68(3), pp.413-437.

Dutordoir, M. and Van de Gucht, L., 2007. Are there windows of opportunity for convertible debt issuance? Evidence for Western Europe. Journal of Banking & Finance, 31(9), pp.2828-2846.

Eckbo, B.E., 1986. Valuation effects of corporate debt offerings. Journal of Financial economics, 15(1), pp.119-151.

Ellul, A. and Pagano, M., 2006. IPO underpricing and after-market liquidity. Review of Financial Studies, 19(2), pp.381-421.

Fang, L. H., 2005. Investment bank reputation and the price and quality of underwriting services. The Journal of Finance, 60(6), pp.2729-2761.

Francis, B. B. and Hasan, I., 2001. The underpricing of venture and nonventure capital IPOs: An empirical investigation. Journal of Financial Services Research, 19(2-3), pp.99-113.

Garay, U. and Molina, C.A., 2014. The public bond offering of Petróleos de Venezuela SA. Journal of Business Research, 67(4), pp.582-590.

Gupta, M.G., Jin, L. and Homma, N., 2003. Static and dynamic neural networks: from fundamentals to advanced theory. New Jersey: Wiley.

Habib, M.A. and Ljungqvist, A.P., 2001. Underpricing and entrepreneurial wealth losses in IPOs: Theory and evidence. Review of Financial Studies, 14(2), pp.433-458.

Hagan, M.T. and Menhaj, M.B., 1994. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6), pp.989-993.

Hale, G. and Santos, J.A., 2008. The decision to first enter the public bond market: The role of firm reputation, funding choices, and bank relationships. Journal of Banking & Finance, 32(9), pp.1928-1940.

Hanley, K.W., 1993. The underpricing of initial public offerings and the partial adjustment phenomenon. Journal of financial economics, 34(2), pp.231-250.

Hayashi, Y., Hsiehb M-H. and Setiono, R., 2010. Understanding consumer heterogeneity: A business intelligence application of neural networks. Knowledge-Based Systems, 23(8), pp.856-863.

Hornik, K., Stinchcombe, M. and White, H., 1989. Multilayer feedforward network are universal approximators. Neural Networks, 2(5), pp.359-366.

Jain, B.A. and Nag, B.N., 1995. Artificial neural network models for pricing initial public offerings. Decision Sciences, 26(3), pp.283-302.
Kang, J.K. and Lee, Y.W., 1996. The pricing of convertible debt offerings. Journal of Financial Economics, 41(2), pp.231-248.

Ke, M.C., Liang Liao, T. and Hsu, H.M., 2007. Some new evidence on bond initial public offerings in the Taiwan Stock Exchange: An industrial perspective. Physica A: Statistical Mechanics and its Applications, 378(2), pp.357-373.

Krigman, L., Shaw, W.H. and Womack, K.L., 2001. Why do firms switch underwriters?. Journal of Financial Economics, 60(2), pp.245-284.

Lee, T.S. and Chen, I.F., 2005. A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications, 28(4), pp.743-752.

Lee, P.M. and Wahal, S., 2004. Grandstanding, certification and the underpricing of venture capital backed IPOs. Journal of Financial Economics, 73(2), pp.375-407.

Levenberg, K., 1944. A method for the solution of certain non-linear problems in least squares. The Quarterly of Applied Mathematics, 2, pp.164-168.

Lewis, C.M., Rogalski, R.J. and Seward, J.K., 2002. Risk changes around convertible debt offerings. Journal of Corporate Finance, 8(1), pp.67-80.

Loureiro, G., 2010. The reputation of underwriters: A test of the bonding hypothesis. Journal of Corporate Finance, 16(4), pp.516-532.

Marquardt, D.W., 1963. An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), pp.431-441.

McKenzie, C.R. and Takaoka, S., 2008. Underwriter reputation and switching. Mathematics and Computers in Simulation, 78(2), pp.215-222.

Moosmayer, D.C., Chonga, A. Y-L, Liua M.J. and Schupparb, B., 2013. A neural network approach to predicting price negotiation outcomes in business-to-business contexts. Expert Systems with Applications, 40(8), pp.3028-3035.

Neupane, S. and Thapa, C., 2013. Underwriter reputation and the underwriter–investor relationship in IPO markets. Journal of International Financial Markets, Institutions and Money, 24, pp.105-126.

Purnanandam, A.K. and Swaminathan, B., 2004. Are IPOs really underpriced? Review of financial studies, 17(3), pp.811-848.

Robertson, S.J., Golden, B.L., Runger, G.C. and Wasil, E.A., 1998. Neural network models for initial public offerings. Neurocomputing, 18(1), pp.165-182.

Rock, K., 1986. Why new issues are underpriced. Journal of financial economics, 15(1), pp.187-212.

Roten, I.C. and Mullineaux, D.J., 2002. Debt underwriting by commercial bank-affiliated firms and investment banks: More evidence. Journal of banking & finance, 26(4), pp.689-718.

Schultz, P., 2003. Pseudo market timing and the long‐run underperformance of IPOs. the Journal of Finance, 58(2), pp.483-518.

Sontag, E.D., 1992. Feedback stabilization using two-hidden-layer nets. IEEE Transactions on Neural Networks, 3, pp.981-990.
Spiess, D.K. and Affleck-Graves, J., 1999. The long-run performance of stock returns following debt offerings. Journal of Financial Economics, 54(1), pp.45-73.

Tang, T.C. and Chi, L.C., 2005. Neural networks analysis in business failure prediction of Chinese importers: A between-countries approach. Expert Systems with Applications, 29(2), pp.244-255.

Wang, W. and Yung, C., 2011. IPO Information Aggregation and Underwriter Quality. Review of Finance, 15(2), pp.301-325.
West, D., 2000. Neural network credit scoring models. Computers & Operations Research, 27(11-12), pp.1131-1152.

Zheng, S.X. and Li, M., 2008. Underpricing, ownership dispersion, and aftermarket liquidity of IPO stocks. Journal of Empirical Finance, 15(3), pp.436-454.


Michal Tkac
Robert Verner (Primary Contact)
Tkac, M., & Verner, R. (2014). PREDICTION OF DEMAND FOR PRIMARY BOND OFFERINGS USING ARTIFICIAL NEURAL NETWORKS. Quality Innovation Prosperity, 18(2), 116–129.
Copyright and license info is not available

Article Details

Improving Team Collaboration in Patient Transfer Processes by Co-Workers’ Perceptions and Suggestions

Lilly-Mari Sten, Pernilla Ingelsson, Ingela Bäckström, Marie Häggström
Abstract View : 259
Download :79

Effective TQM Implementation in the Service Industry: A Proposed Framework

Hesham Magd, Saurav Negi, Mohammad Sultan Ahmad Ansari
Abstract View : 319
Download :129

The Analysis of Total Quality Management Critical Success Factors

Mirza Kulenović, Martin Folta, Ljiljan Veselinović
Abstract View : 453
Download :242