Skip to main content
SpringerLink
Log in

Empowering Cash Managers Through Compromise Programming

  • Chapter
  • First Online:
Financial Decision Aid Using Multiple Criteria

Part of the book series: Multiple Criteria Decision Making ((MCDM))

Abstract

Typically, the cash management literature focuses on optimizing cost, hence neglecting risk analysis. In this chapter, we address the cash management problem from a multiobjective perspective by considering not only the cost but also the risk of cash policies. We propose novel measures to incorporate risk analysis as an additional goal in cash management. Next, we rely on compromise programming as a method to minimize the sum of weighted distances to an ideal point where both cost and risk are minimum. These weights reflect the particular preferences of cash managers when selecting the best policies that solve the multiobjective cash management problem. As a result, we suggest three alternative solvers to cover a wide range of possible situations: Monte Carlo methods, linear programming, and quadratic programming. We also provide a Python software library with an implementation of the proposed solvers ready to be embedded in cash management decision support systems. We finally describe a framework to assess the utility of cash management models when considering multiple objectives.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Abdelaziz, F. B., Aouni, B., & El Fayedh, R. (2007). Multi-objective stochastic programming for portfolio selection. European Journal of Operational Research, 177(3), 1811–1823.

    Google Scholar 

  • Aouni, B., Colapinto, C., & La Torre, D. (2014). Financial portfolio management through the goal programming model: Current state-of-the-art. European Journal of Operational Research, 234(2), 536–545.

    Google Scholar 

  • Artzner, P., Delbaen, F., Eber, J.-M., & Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9(3), 203–228.

    Google Scholar 

  • Baccarin, S. (2002). Optimal impulse control for cash management with quadratic holding-penalty costs. Decisions in Economics and Finance, 25(1), 19–32.

    Google Scholar 

  • Baccarin, S. (2009). Optimal impulse control for a multidimensional cash management system with generalized cost functions. European Journal of Operational Research, 196(1), 198–206.

    Google Scholar 

  • Ballestero, E. (1998). Approximating the optimum portfolio for an investor with particular preferences. Journal of the Operational Research Society, 49, 998–1000.

    Google Scholar 

  • Ballestero, E. (2005). Mean-semivariance efficient frontier: A downside risk model for portfolio selection. Applied Mathematical Finance, 12(1), 1–15.

    Google Scholar 

  • Ballestero, E. (2007). Compromise programming: A utility-based linear-quadratic composite metric from the trade-off between achievement and balanced (non-corner) solutions. European Journal of Operational Research, 182(3), 1369–1382.

    Google Scholar 

  • Ballestero, E., & Pla-Santamaria, D. (2004). Selecting portfolios for mutual funds. Omega, 32(5), 385–394.

    Google Scholar 

  • Ballestero, E., & Romero, C. (1998). Multiple criteria decision making and its applications to economic problems. New York: Springer.

    Google Scholar 

  • Bar-Ilan, A., Perry, D., & Stadje, W. (2004). A generalized impulse control model of cash management. Journal of Economic Dynamics and Control, 28(6), 1013–1033.

    Google Scholar 

  • Baumol, W. J. (1952). The transactions demand for cash: An inventory theoretic approach. The Quarterly Journal of Economics, 66(4), 545–556.

    Google Scholar 

  • Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust optimization. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Bensoussan, A., Chutani, A., & Sethi, S. P. (2009). Optimal cash management under uncertainty. Operations Research Letters, 37(6), 425–429.

    Google Scholar 

  • Brealey, R. A. & Myers, S. C. (2003). Principles of corporate finance. New York: McGraw-Hill.

    Google Scholar 

  • Chen, X., & Simchi-Levi, D. (2009). A new approach for the stochastic cash balance problem with fixed costs. Probability in the Engineering and Informational Sciences, 23(4), 545–562.

    Google Scholar 

  • Constantinides, G. M., & Richard, S. F. (1978). Existence of optimal simple policies for discounted-cost inventory and cash management in continuous time. Operations Research, 26(4), 620–636.

    Google Scholar 

  • da Costa Moraes, M. B., & Nagano, M. S. (2014). Evolutionary models in cash management policies with multiple assets. Economic Modelling, 39, 1–7.

    Google Scholar 

  • da Costa Moraes, M. B., Nagano, M. S., & Sobreiro, V. A. (2015). Stochastic cash flow management models: A literature review since the 1980s. In Decision models in engineering and management (pp. 11–28). New York: Springer.

    Google Scholar 

  • Daellenbach, H. G. (1974). Are cash management optimization models worthwhile? Journal of Financial and Quantitative Analysis, 9(4), 607–626.

    Google Scholar 

  • Glasserman, P. (2003). Monte Carlo methods in financial engineering. New York: Springer.

    Google Scholar 

  • Gormley, F. M., & Meade, N. (2007). The utility of cash flow forecasts in the management of corporate cash balances. European Journal of Operational Research, 182(2), 923–935.

    Google Scholar 

  • Gregory, G. (1976). Cash flow models: A review. Omega, 4(6), 643–656.

    Google Scholar 

  • Herrera-Cáceres, C. A., & Ibeas, A. (2016). Model predictive control of cash balance in a cash concentration and disbursements system. Journal of the Franklin Institute, 353(18), 4885–4923.

    Google Scholar 

  • Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.

    Google Scholar 

  • McNeil, A. J., Frey, R., & Embrechts, P. (2005). Quantitative risk management: Concepts, techniques and tools. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Melo, M. A., & Bilich, F. (2013). Expectancy balance model for cash flow. Journal of Economics and Finance, 37(2), 240–252.

    Google Scholar 

  • Miller, M. H., & Orr, D. (1966). A model of the demand for money by firms. The Quarterly Journal of Economics, 80(3), 413–435.

    Google Scholar 

  • Pla-Santamaria, D., & Bravo, M. (2013). Portfolio optimization based on downside risk: A mean-semivariance efficient frontier from Dow Jones blue chips. Annals of Operations Research, 205(1), 189–201.

    Google Scholar 

  • Premachandra, I. (2004). A diffusion approximation model for managing cash in firms: An alternative approach to the Miller–Orr model. European Journal of Operational Research, 157(1), 218–226.

    Google Scholar 

  • Ringuest, J. L. (1992). Multiobjective optimization: Behavioral and computational considerations. New York: Springer.

    Google Scholar 

  • Rockafellar, R. T., & Uryasev, S. (2002). Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7), 1443–1471.

    Google Scholar 

  • Salas-Molina, F., Martin, F. J., Rodríguez-Aguilar, J. A., Serrà, J., & Arcos, J. L. (2017). Empowering cash managers to achieve cost savings by improving predictive accuracy. International Journal of Forecasting, 33(2), 403–415.

    Google Scholar 

  • Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2016). A multi-objective approach to the cash management problem. Annals of Operations Research, 1–15. https://doi.org/10.1007/s10479-016-2359-1

  • Soyster, A. L. (1973). Technical note—convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research, 21(5), 1154–1157.

    Article  Google Scholar 

  • Srinivasan, V., & Kim, Y. H. (1986). Deterministic cash flow management: State of the art and research directions. Omega, 14(2), 145–166.

    Article  Google Scholar 

  • Weibull, W. (1951). A statistical distribution function of wide applicability. Journal of Applied Mechanics, 18(3), 293–297.

    Google Scholar 

  • Yu, P.-L. (1985). Multiple criteria decision making: Concepts, techniques and extensions. New York: Plenum Press.

    Book  Google Scholar 

  • Zeleny, M. (1974). A concept of compromise solutions and the method of the displaced ideal. Computers & Operations Research, 1(3), 479–496.

    Article  Google Scholar 

  • Zeleny, M. (1982). Multiple criteria decision making. New York: McGraw-Hill.

    Google Scholar 

Download references

Acknowledgements

Work partially funded by projects 2014 SGR 118 and Collectiveware TIN2015-66863-C2-1-R (MINECO/FEDER).

Author information

Authors and Affiliations

  1. Hilaturas Ferre, S.A., Les Molines, 2, 03450, Banyeres de Mariola, Spain

    Francisco Salas-Molina

  2. Universitat Politècnica de València, Ferrándiz y Carbonell, 03801, Alcoy, Spain

    David Pla-Santamaria

  3. IIIA-CSIC, Campus UAB, 08913, Cerdanyola, Spain

    Juan A. Rodríguez-Aguilar

Authors
  1. Francisco Salas-Molina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Pla-Santamaria
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juan A. Rodríguez-Aguilar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Pla-Santamaria .

Editor information

Editors and Affiliations

  1. College of Business Administration, University of Bahrain, Sakhir, Bahrain

    Hatem Masri

  2. Department of Quantitative Economics, University of Oviedo, Oviedo, Asturias, Spain

    Blanca Pérez-Gladish

  3. School of Production Engineering and Management, Technical University of Crete, Chania, Greece

    Constantin Zopounidis

Appendix: Python for Cash Management

Appendix: Python for Cash Management

In an attempt to fill the gap between theory and practice in cash management and multiobjective decision-making, we next provide the link to a Python software library containing the three proposed MOCMP solvers. We used this library to perform the examples in Sects. 5.1, 5.2 and 5.3:

https://github.com/PacoSalas/Empowering-cash-managers-CP.git

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Salas-Molina, F., Pla-Santamaria, D., Rodríguez-Aguilar, J.A. (2018). Empowering Cash Managers Through Compromise Programming. In: Masri, H., Pérez-Gladish, B., Zopounidis, C. (eds) Financial Decision Aid Using Multiple Criteria. Multiple Criteria Decision Making. Springer, Cham. https://doi.org/10.1007/978-3-319-68876-3_7

Download citation

Publish with us

Policies and ethics

Search

Navigation