Skip to main content
SpringerLink
Log in

The role of supervised learning in the decision process to fair trade US municipal debt

EURO Journal on Decision Processes volume 6, pages 139–168 (2018)Cite this article

Abstract

Determining a fair price and an appropriate timescale to trade municipal debt is a complex decision. This research uses data informatics to explore transaction characteristics and trading activity of investment grade US municipal bonds. Using the relatively recent data stream distributed by the Municipal Securities Rulemaking Board, we provide an institutional summary of market participants and their trading behavior. Subsequently, we focus on a sample of AAA bonds to derive a new methodology to estimate a trade-weighted benchmark municipal yield curve. The methodology integrates the study of ridge regression, artificial neural networks, and support vector regression. We find an enhanced radial basis function artificial neural network outperforms alternate methods used to estimate municipal term structure. This result forms the foundation for establishing a decision theory on optimal municipal bond trading. Using multivariate modeling of a liquidity domain measured across three dependent variables, we investigate the proposed decision theory by estimating weekly production-theoretic bond liquidity returns to scale. Across the three liquidity measures and for almost all weeks investigated, bond trading liquidity is elastic with respect to the modeled factors. This finding leads us to conclude that an optimal trading policy for municipal debt can be implemented on a weekly timescale using the elasticity estimates of bond price, trade size, risk, days-to-maturity, and the macroeconomic influences of labor in the workforce and building activity.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Notes

  1. Creal and Wu (2014) provide an alternative research strain based on affine models to demonstrate how spanned stochastic volatility captures either the cross section of yields or the fitted volatility.

  2. For a complete listing, see: http://bit.ly/2eZvyY5.

  3. All term structure curves are available at: http://bit.ly/2eZvyY5.

  4. B-spline or basis spline is a spline function that has minimal support with respect to a given degree, smoothness, and domain partition (Wikipedia). It is provided here only as a base comparative method.

References

  • Aguilar LA (2015) Statement on making the municipal securities market more transparent, liquid, and fair US securities and exchange commission, http://bit.ly/2uOsamN

  • Almeida C, Ardison K, Kubudi D, Simonsen A, Vicente J (2017) Forecasting bond yields with segmented term structure models. J Financ Econ 16(1):1–33

    Google Scholar 

  • Amihud Y (2002) Illiquidity and stock returns: cross-section and time-series effects. J Financ Mark 5:31–56

    Article  Google Scholar 

  • Ang A, Piazzesi M (2003) A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables. J Mon Econ 50:745–787

    Article  Google Scholar 

  • Annaert J, Claes AGP, De Ceuster MJK, Zhang H (2010) Estimating the yield curve using the nelson-siegel model. European Financial Management Association, Aarhus

    Google Scholar 

  • Bao J, Pan J, Wang J (2011) The illiquidity of corporate bonds. J Financ 66:911–946

    Article  Google Scholar 

  • Bowsher CG, Meeks R (2013) Stationarity and the term structure of interest rates: a characterization of stationary and unit root yield curves

  • Cobb CW, Douglas PH (1928) A theory of production. Am Econ Rev 18:139–165

    Google Scholar 

  • Cox JC, Ingersoll JE, Ross SA (1985) A theory of the term structure of interest rates. Econometrica 53:385–408

    Article  Google Scholar 

  • Creal DD, Wu JC (2015) Estimation of affine term structure models with spanned or unspanned stochastic volatility. J Econ 85(1):60–81

    Article  Google Scholar 

  • Dai Q, Singleton KJ (2000) Specification analysis of affine term structure models. J Financ 55:1943–1978

    Article  Google Scholar 

  • Dai Q, Singleton KJ, Yang W (2007) Regime shifts in a dynamic term structure model of U.S treasury bond yields. Rev Financ Stud Soc Financ Stud 20:1669–1706

    Article  Google Scholar 

  • Daniels K, Ejara DD (2009) Impact of information asymmetry on municipal bond yields: an empirical analysis. Am J Econ Bus Admin 1:11–20

    Google Scholar 

  • Dash GH Jr, Kajiji N (2003) New evidence on the predictability of South African Fx volatility in heterogenous bilateral markets. Afr Financ J 5:1–15

    Google Scholar 

  • Dash GH Jr, Kajiji N (2008) Engineering a generalized neural network mapping of volatility spillovers in european government bond markets. In: Zopounidis C, Doumpos M, Pardalos PM (eds) Handbook of financial engineering, optimization and its applications, vol 18. Springer, Berlin

    Google Scholar 

  • Dash Jr. GH, Kajiji N Modeling FX (2002) Volatility: a comparative analysis of the rbf neural network topology. In: 9th international conference on forecasting financial markets, London, England

  • Dash Jr. GH, Hanumara C, Kajiji N (2003) Neural network architectures for modeling fx futures options volatility. In: Annual Meetings of the Northeast Decision Sciences Institute, Providence, Rhode Island

  • De Pooter M (2007) Examining the Nelson-Siegel class of term structure models. Tinbergen Institute, Amsterdam

    Google Scholar 

  • De Pooter M, Ravazzolo F, van Dijk D (2010) Term structure forecasting using macro factors and forecast combination. Board of governors of the federal reserve system, Discussion paper 993

  • Diebold FX, Li C (2006) Forecasting the term structure of government bond yields. J Econ 130:337–364

    Article  Google Scholar 

  • Diebold FX, Rudebusch GD, Boragan AS (2006) The macroeconomy and the yield curve: a dynamic latent factor approach. J Econ 131:309–338

    Article  Google Scholar 

  • Drucker H, Burges CJ, Kaufam L, Smola A, Vapnik V (1996) Support vector regression machines. In: NIPS’96 proceedings of the 9th international conference on neural information processing systems, Denver, CO, 1996. MIT Press, Cambridge, MA

  • Edwards AK, Harris LE, Piwowar MS (2007) Corporate bond market transaction costs and transparency. J Financ 62:1421–1451

    Article  Google Scholar 

  • Fabozzi FJ, Martellini L, Priaulet P (2005) Predictability in the Shape of the Term Structure of Interest Rates. J Fixed Income 15:40–53

    Article  Google Scholar 

  • Feldhütter P (2012) The same bond at different prices: identifying search frictions and selling pressures. Rev Financ Stud 25:1155–1206

    Article  Google Scholar 

  • Geldera LV, Dasb P, Janssena H, Roelsa S (2014) Comparative study of metamodelling techniques in building energy simulation: guidelines for practitioners. Simul Model Pract Theory 49:245–257

    Article  Google Scholar 

  • Goldstein M, Hotchkiss ES (2015) Dealer behavior in highly illiquid risky assets. Queens University, Belfast

    Google Scholar 

  • Gonzalez-Rozada M, Sola M, Hevia C, Spagnolo F (2012) Estimating and forecasting the yield curve using a markov switching dynamic nelson and siegel model. Universidad Torcuato Di Tella, Buenos Aires

    Google Scholar 

  • Harris LE, Piwowar MS (2006) Secondary trading costs in the municipal bond market. J Financ 61:1361–1397

    Article  Google Scholar 

  • Iglewicz B, Hoaglin D (1993) How to detect and handle outliers. In: Mykytka EF (ed) The ASQC basic reference in quality control: statistical techniques, vol 16. American Society for Quality Control Statistics Division, Milwaukee

    Google Scholar 

  • Juillard M, Villemot S (2011) Multi-country real business cycle models: accuracy tests and test bench. J Econ Dyn Control 35:178–185

    Article  Google Scholar 

  • Kajiji N (2001) Adaptation of alternative closed form regularization parameters with prior information to the radial basis function neural network for high frequency financial time series. University of Rhode Island

  • Kajiji N, Dash GH Jr (2013) On the behavioral specification and multivariate neural network estimation of cognitive scale economies. J Appl Operat Res 5:30–40

    Google Scholar 

  • Kalotay AJ, Dorigan MP (2009) What makes the municipal yield curve rise? J Fixed Income 18(3):65

    Article  Google Scholar 

  • Lin H, Liu S, Wang J, Wu C (2010) Liquidity and the pricing of municipal bonds. In: Proceedings of the 2010 China international conference in finance, Beijing, China

  • Mizrach B (2015) Analysis of corporate bond liquidity. FINRA Office of the Chief Economist. http://bit.ly/2wbO9Dw

  • Moench E (2008) Forecasting the yield curve in a data-rich environment: a no-arbitrage factor-augmented var approach. J Econ 146:26–43

    Article  Google Scholar 

  • Moraux F, Pakulyak O (2016) Which term structure of interest rates model performs the best to price the government bonds in euro area? Paper presented at the forecasting financial markets, Hannover, Germany

  • Nelson CR, Siegel A (1987) Parismoious modeling of yield curves. J Bus 60:473–489

    Article  Google Scholar 

  • Olej V, Hájek P (2009) Municipal creditworthiness modelling by radial basis function neural networks and sensitive analysis of their input parameters. In: Alippi C, Polycarpou M, Panayiotou C, Ellinas G (eds) Artificial neural networks—ICANN 2009. Lecture notes in computer science. Springer, Berlin, pp 505–514

    Chapter  Google Scholar 

  • Piazzesi M (2010) Affine term structure models. In: Ait-Sahalia Y, Hansen L (eds) Handbook of financial econometrics: tools and techniques, vol 1. Handbooks in Finance, Elesvier

    Google Scholar 

  • Ratrout NT, Gazder U (2014) Factors affecting performance of parametric and non-parametric models for daily traffic forecasting. In: 5th international conference on ambient systems, networks and technologies, Procedia Computer Science 32:285-292

  • Steenbarger BN (2003) The psychology of trading: tools and techniques for minding the markets. Wiley, Hoboken

    Google Scholar 

  • Vapnik V (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  • Vasicek O (1977) An equilibrium characterization of the term structure. J Financ Econ 5:177–188

    Article  Google Scholar 

  • Xiang J, Zhu X (2013) A regime-switching Nelson-Siegel term structure model and interest rate forecasts. J Financ Econ 11:522–555

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Finance and Decision Sciences Area, College of Business Administration, University of Rhode Island, South Kingstown, USA

    Gordon H. Dash

  2. Faculty, Interdisciplinary Neuroscience Program, University of Rhode Island, South Kingstown, USA

    Gordon H. Dash

  3. Department of Computer Science and Statistics, University of Rhode Island, South Kingstown, USA

    Nina Kajiji

  4. The NKD Group, Inc., Providence, RI, USA

    Nina Kajiji

  5. Municipal Market Division, Thomson Reuters, Boston, USA

    Domenic Vonella

Authors
  1. Gordon H. Dash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nina Kajiji
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Domenic Vonella
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gordon H. Dash.

Additional information

Submitted for publication consideration in the Feature Issue on “Financial Decision Support”, Euro Journal on Decision Processes, 30 July 2017.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 914 kb)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dash, G.H., Kajiji, N. & Vonella, D. The role of supervised learning in the decision process to fair trade US municipal debt. EURO J Decis Process 6, 139–168 (2018). https://doi.org/10.1007/s40070-018-0079-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40070-018-0079-2

Keywords

JEL Classification

Search

Navigation