8,413 research outputs found

    On the formal foundations of cash management systems

    Full text link
    [EN] Cash management aims to find a balance between what is held in cash and what is allocated in other investments in exchange for a given return. Dealing with cash management systems with multiple accounts and different links between them is a complex task. Current cash management models provide analytic solutions without exploring the underlying structure of accounts and its main properties. There is a need for a formal definition of cash management systems. In this work, we introduce a formal approach to manage cash with multiple accounts based on graph theory. Our approach allows a formal reasoning on the relation between accounts in cash management systems. A critical part of this formal reasoning is the characterization of desirable and non-desirable cash management policies. Novel theoretical results guide cash managers in the analysis of complex cash management systems.This work is partially funded by projects Logistar (H2020-769142), AI4EU (H2020-825619) and 2017 SGR 172.Salas-Molina, F.; Rodriguez-Aguilar, JA.; Pla Santamaría, D.; Garcia-Bernabeu, A. (2021). On the formal foundations of cash management systems. Operational Research. 21(2):1081-1095. https://doi.org/10.1007/s12351-019-00464-6S10811095212Baccarin S (2009) Optimal impulse control for a multidimensional cash management system with generalized cost functions. Eur J Oper Res 196(1):198–206Bollobás B (2013) Modern graph theory, vol 184. Springer, BerlinBondy JA, Murty USR (1976) Graph theory with applications, vol 290. Macmillan, LondonChartrand G, Oellermann OR (1993) Applied and algorithmic graph theory, vol 993. McGraw-Hill, New YorkConstantinides GM, Richard SF (1978) Existence of optimal simple policies for discounted-cost inventory and cash management in continuous time. Oper Res 26(4):620–636da Costa Moraes MB, Nagano MS, Sobreiro VA (2015) Stochastic cash flow management models: a literature review since the 1980s. In: Guarnieri P (ed) Decision models in engineering and management. Springer, Berlin, pp 11–28de Avila Pacheco JV, Morabito R (2011) Application of network flow models for the cash management of an agribusiness company. Comput Ind Eng 61(3):848–857Golden B, Liberatore M, Lieberman C (1979) Models and solution techniques for cash flow management. Comput Oper Res 6(1):13–20Gormley FM, Meade N (2007) The utility of cash flow forecasts in the management of corporate cash balances. Eur J Oper Res 182(2):923–935Gregory G (1976) Cash flow models: a review. Omega 4(6):643–656Makridakis S, Wheelwright SC, Hyndman RJ (2008) Forecasting methods and applications. Wiley, New YorkRighetto GM, Morabito R, Alem D (2016) A robust optimization approach for cash flow management in stationery companies. Comput Ind Eng 99:137–152Salas-Molina F (2017) Risk-sensitive control of cash management systems. Oper Res. https://doi.org/10.1007/s12351-017-0371-0Salas-Molina F, Pla-Santamaria D, Rodriguez-Aguilar JA (2018) A multi-objective approach to the cash management problem. Ann Oper Res 267(1):515–529Srinivasan V, Kim YH (1986) Deterministic cash flow management: state of the art and research directions. Omega 14(2):145–166Valiente G (2013) Algorithms on trees and graphs. Springer, Berli

    A multi-objective approach to the cash management problem

    Full text link
    [EN] Cash management is concerned with optimizing costs of short-term cash policies of a company. Different optimization models have been proposed in the literature whose focus has been only placed on a single objective, namely, on minimizing costs. However, cash managers may also be interested in risk associated to cash policies. In this paper, we propose a multi-objective cash management model based on compromise programming that allows cash managers to select the best policies, in terms of cost and risk, according to their risk preferences. The model is illustrated through several examples using real data from an industrial company, alternative cost scenarios and two different measures of risk. As a result, we provide cash managers with a new tool to allow them deciding on the level of risk to take in daily decision-making.Work partially funded by projects Collectiveware TIN2015-66863-C2-1-R (MINECO/FEDER) and 2014 SGR 118.Salas-Molina, F.; Pla Santamaría, D.; Rodriguez Aguilar, JA. (2016). A multi-objective approach to the cash management problem. Annals of Operations Research. 1-15. https://doi.org/10.1007/s10479-016-2359-1S115Baccarin, S. (2009). Optimal impulse control for a multidimensional cash management system with generalized cost functions. European Journal of Operational Research, 196(1), 198–206.Ballestero, E. (1998). Approximating the optimum portfolio for an investor with particular preferences. Journal of the Operational Research Society, 49(9), 998–1000.Ballestero, E. (2005). Mean-semivariance efficient frontier: A downside risk model for portfolio selection. Applied Mathematical Finance, 12(1), 1–15.Ballestero, E., & Pla-Santamaria, D. (2004). Selecting portfolios for mutual funds. Omega, 32(5), 385–394.Ballestero, E., & Romero, C. (1998). Multiple criteria decision making and its applications to economic problems. Berlin: Springer.Bates, T. W., Kahle, K. M., & Stulz, R. M. (2009). Why do US firms hold so much more cash than they used to? The Journal of Finance, 64(5), 1985–2021.Baumol, W. J. (1952). The transactions demand for cash: An inventory theoretic approach. The Quarterly Journal of Economics, 66(4), 545–556.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(04), 545–562.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.da Costa Moraes, M. B., & Nagano, M. S. (2014). Evolutionary models in cash management policies with multiple assets. Economic Modelling, 39, 1–7.da Costa Moraes, M. B., Nagano, M.S., Sobreiro, V. A. (2015). Stochastic cash flow management models: A literature review since the 1980s. In P. Guarnieri (Ed.), Decision models in engineering and management. Decision engineering (pp. 11–28). Switzerland: Springer.Eppen, G. D., & Fama, E. F. (1969). Cash balance and simple dynamic portfolio problems with proportional costs. International Economic Review, 10(2), 119–133.Gao, H., Harford, J., & Li, K. (2013). Determinants of corporate cash policy: Insights from private firms. Journal of Financial Economics, 109(3), 623–639.Girgis, N. M. (1968). Optimal cash balance levels. Management Science, 15(3), 130–140.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.Gregory, G. (1976). Cash flow models: A review. Omega, 4(6), 643–656.Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91.McNeil, A. J., Frey, R., & Embrechts, P. (2005). Quantitative risk management: Concepts, techniques and tools. Princeton: Princeton University Press.Melo, M. A., & Bilich, F. (2013). Expectancy balance model for cash flow. Journal of Economics and Finance, 37(2), 240–252.Miller, M. H., & Orr, D. (1966). A model of the demand for money by firms. The Quarterly Journal of Economics, 80(3), 413–435.Myers, S. C., & Brealey, R. A. (2003). Principles of corporate finance (7th ed.). NewYork: McGraw-Hill.Neave, E. H. (1970). The stochastic cash balance problem with fixed costs for increases and decreases. Management Science, 16(7), 472–490.Penttinen, M. J. (1991). Myopic and stationary solutions for stochastic cash balance problems. European Journal of Operational Research, 52(2), 155–166.Pinkowitz, L., Stulz, R. M., & Williamson, R. (2016). Do US firms hold more cash than foreign firms do? Review of Financial Studies, 29(2), 309–348.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.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.Roijers, D. M., Vamplew, P., Whiteson, S., & Dazeley, R. (2013). A survey of multi-objective sequential decision-making. Journal of Artificial Intelligence Research, 48(1), 67–113.Ross, S. A., Westerfield, R., & Jordan, B. D. (2002). Fundamentals of corporate finance (6th ed.). NewYork: McGraw-Hill.Sharpe, W. F. (1966). Mutual fund performance. The Journal of Business, 39(1), 119–138.Sharpe, W. F. (1994). The sharpe ratio. The Journal of Portfolio Management, 21(1), 49–58.Srinivasan, V., & Kim, Y. H. (1986). Deterministic cash flow management: State of the art and research directions. Omega, 14(2), 145–166.Steuer, R. E., Qi, Y., & Hirschberger, M. (2007). Suitable-portfolio investors, nondominated frontier sensitivity, and the effect of multiple objectives on standard portfolio selection. Annals of Operations Research, 152(1), 297–317.Stone, B. K. (1972). The use of forecasts and smoothing in control-limit models for cash management. Financial Management, 1(1), 72–84.Whalen, E. L. (1966). A rationalization of the precautionary demand for cash. The Quarterly Journal of Economics, 80(2), 314–324.Yu, P. L. (2013). Multiple-criteria decision making: Concepts, techniques, and extensions. Berlin: Springer.Zeleny, M. (1982). Multiple criteria decision making. NewYork: McGraw-Hill.Zopounidis, C. (1999). Multicriteria decision aid in financial management. European Journal of Operational Research, 119(2), 404–415

    Multiple-criteria cash-management policies with particular liquidity terms

    Full text link
    [EN] Eliciting policies for cash management systems with multiple assets is by no means straightforward. Both the particular relationship between alternative assets and time delays from control decisions to availability of cash introduce additional difficulties. Here we propose a cash management model to derive short-term finance policies when considering multiple assets with different expected returns and particular liquidity terms for each alternative asset. In order to deal with the inherent uncertainty about the near future introduced by cash flows, we use forecasts as a key input to the model. We express uncertainty as lack of predictive accuracy and we derive a deterministic equivalent problem that depends on forecasting errors and preferences of cash managers. Since the assessment of the quality of forecasts is recommended, we describe a method to evaluate the impact of predictive accuracy in cash management policies. We illustrate this method through several numerical examples.Salas-Molina, F.; Pla Santamaría, D.; Garcia-Bernabeu, A.; Mayor-Vitoria, F. (2020). Multiple-criteria cash-management policies with particular liquidity terms. IMA Journal of Management Mathematics. 31(2):217-231. https://doi.org/10.1093/imaman/dpz010S217231312Abdelaziz, F. B., Aouni, B., & Fayedh, R. E. (2007). Multi-objective stochastic programming for portfolio selection. European Journal of Operational Research, 177(3), 1811-1823. doi:10.1016/j.ejor.2005.10.021Aouni, B., Ben Abdelaziz, F., & La Torre, D. (2012). The Stochastic Goal Programming Model: Theory and Applications. Journal of Multi-Criteria Decision Analysis, 19(5-6), 185-200. doi:10.1002/mcda.1466Aouni, 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. doi:10.1016/j.ejor.2013.09.040Baccarin, S. (2009). Optimal impulse control for a multidimensional cash management system with generalized cost functions. European Journal of Operational Research, 196(1), 198-206. doi:10.1016/j.ejor.2008.02.040Ballestero, E. (2001). Stochastic goal programming: A mean–variance approach. European Journal of Operational Research, 131(3), 476-481. doi:10.1016/s0377-2217(00)00084-9Ballestero, E., & Romero, C. (1998). Multiple Criteria Decision Making and its Applications to Economic Problems. doi:10.1007/978-1-4757-2827-9Bemporad, A., & Morari, M. (1999). Control of systems integrating logic, dynamics, and constraints. Automatica, 35(3), 407-427. doi:10.1016/s0005-1098(98)00178-2Cabello, J. G. (2013). Cash efficiency for bank branches. SpringerPlus, 2(1). doi:10.1186/2193-1801-2-334García Cabello, J., & Lobillo, F. J. (2017). Sound branch cash management for less: A low-cost forecasting algorithm under uncertain demand. Omega, 70, 118-134. doi:10.1016/j.omega.2016.09.005Charnes, A., & Cooper, W. W. (1959). Chance-Constrained Programming. Management Science, 6(1), 73-79. doi:10.1287/mnsc.6.1.73Charnes, A., & Cooper, W. W. (1977). Goal programming and multiple objective optimizations. European Journal of Operational Research, 1(1), 39-54. doi:10.1016/s0377-2217(77)81007-2Constantinides, 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. doi:10.1287/opre.26.4.620Moraes, M. B. da C., & Nagano, M. S. (2014). Evolutionary models in cash management policies with multiple assets. Economic Modelling, 39, 1-7. doi:10.1016/j.econmod.2014.02.010Da Costa Moraes, M. B., Nagano, M. S., & Sobreiro, V. A. (2015). Stochastic Cash Flow Management Models: A Literature Review Since the 1980s. Decision Engineering, 11-28. doi:10.1007/978-3-319-11949-6_2Eppen, G. D., & Fama, E. F. (1969). Cash Balance and Simple Dynamic Portfolio Problems with Proportional Costs. International Economic Review, 10(2), 119. doi:10.2307/2525547Gormley, 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. doi:10.1016/j.ejor.2006.07.041Gregory, G. (1976). Cash flow models: A review. Omega, 4(6), 643-656. doi:10.1016/0305-0483(76)90092-xHerrera-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. doi:10.1016/j.jfranklin.2016.09.007Higson, A., Yoshikatsu, S., & Tippett, M. (2009). Organization size and the optimal investment in cash. IMA Journal of Management Mathematics, 21(1), 27-38. doi:10.1093/imaman/dpp015Miller, M. H., & Orr, D. (1966). A Model of the Demand for Money by Firms. The Quarterly Journal of Economics, 80(3), 413. doi:10.2307/1880728Miller, T. W., & Stone, B. K. (1985). Daily Cash Forecasting and Seasonal Resolution: Alternative Models and Techniques for Using the Distribution Approach. The Journal of Financial and Quantitative Analysis, 20(3), 335. doi:10.2307/2331034Penttinen, M. J. (1991). Myopic and stationary solutions for stochastic cash balance problems. European Journal of Operational Research, 52(2), 155-166. doi:10.1016/0377-2217(91)90077-9Prékopa, A. (1995). Stochastic Programming. doi:10.1007/978-94-017-3087-7Salas-Molina, F. (2017). Risk-sensitive control of cash management systems. Operational Research, 20(2), 1159-1176. doi:10.1007/s12351-017-0371-0Salas-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. doi:10.1016/j.ijforecast.2016.11.002Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2016). A multi-objective approach to the cash management problem. Annals of Operations Research, 267(1-2), 515-529. doi:10.1007/s10479-016-2359-1Salas-Molina, F., Pla-Santamaria, D., & Rodríguez-Aguilar, J. A. (2017). Empowering Cash Managers Through Compromise Programming. Financial Decision Aid Using Multiple Criteria, 149-173. doi:10.1007/978-3-319-68876-3_7Salas-Molina, F., Rodríguez-Aguilar, J. A., & Pla-Santamaria, D. (2018). Boundless multiobjective models for cash management. The Engineering Economist, 63(4), 363-381. doi:10.1080/0013791x.2018.1456596Srinivasan, V., & Kim, Y. H. (1986). Deterministic cash flow management: State of the art and research directions. Omega, 14(2), 145-166. doi:10.1016/0305-0483(86)90017-4Stone, B. K. (1972). The Use of Forecasts and Smoothing in Control-Limit Models for Cash Management. Financial Management, 1(1), 72. doi:10.2307/3664955Stone, B. K., & Miller, T. W. (1987). Daily Cash Forecasting with Multiplicative Models of Cash Flow Patterns. Financial Management, 16(4), 45. doi:10.2307/366610

    The Valuation of Cash Flow Forecasts: An Empirical Analysis

    Get PDF
    This paper compares the market value of highly leveraged transactions (HLTs) to the discounted value of their corresponding cash flow forecasts. These forecasts are provided by management to investors and shareholders in 51 HLTs completed between 1983 and 1989. Our estimates of discounted cash flows are within 10%, on average, of the market values of the completed transactions. Our estimates perform at least as well as valuation methods using comparable companies and transactions. We also invert our analysis and estimate the risk premium implied by transaction values and forecast cash flows, and the relation of the implied risk premium to firm-level betas, industry-level betas, firm size, and firm book-to-market ratios.

    Boundless multiobjective models for cash management

    Full text link
    "This is an Accepted Manuscript of an article published by Taylor & Francis in Engineering Economist on 31-05-2018, available online: https://doi.org/10.1080/0013791X.2018.1456596"[EN] Cash management models are usually based on a set of bounds that complicate the selection of the optimal policies due to nonlinearity. We here propose to linearize cash management models to guarantee optimality through linear-quadratic multiobjective compromise programming models. We illustrate our approach through a reformulation of the suboptimal state-of-the-art Gormley-Meade¿s model to achieve optimality. Furthermore, we introduce a much simpler formulation that we call the boundless model that also provides optimal solutions without using bounds. Results from a sensitivity analysis using real data sets from 54 different companies show that our boundless model is highly robust to cash flow prediction errors.Generalitat de Catalunya [2014 SGR 118]; Ministerio de Economia y Competitividad [Collectiveware TIN2015-66863-C2-1-R].Salas-Molina, F.; Rodriguez-Aguilar, JA.; Pla Santamaría, D. (2018). Boundless multiobjective models for cash management. Engineering Economist (Online). 63(4):363-381. https://doi.org/10.1080/0013791X.2018.1456596S363381634Artzner, P., Delbaen, F., Eber, J.-M., & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3), 203-228. doi:10.1111/1467-9965.00068Baccarin, S. (2009). Optimal impulse control for a multidimensional cash management system with generalized cost functions. European Journal of Operational Research, 196(1), 198-206. doi:10.1016/j.ejor.2008.02.040Ballestero, E., & Romero, C. (1998). Multiple Criteria Decision Making and its Applications to Economic Problems. doi:10.1007/978-1-4757-2827-9Bar-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. doi:10.1016/s0165-1889(03)00064-2Baumol, W. J. (1952). The Transactions Demand for Cash: An Inventory Theoretic Approach. The Quarterly Journal of Economics, 66(4), 545. doi:10.2307/1882104Bemporad, A., & Morari, M. (1999). Control of systems integrating logic, dynamics, and constraints. Automatica, 35(3), 407-427. doi:10.1016/s0005-1098(98)00178-2Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust Optimization. doi:10.1515/9781400831050Branke, J., Deb, K., Miettinen, K., & Słowiński, R. (Eds.). (2008). Multiobjective Optimization. Lecture Notes in Computer Science. doi:10.1007/978-3-540-88908-3Chelouah, R., & Siarry, P. (2000). Journal of Heuristics, 6(2), 191-213. doi:10.1023/a:1009626110229Chen, 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. doi:10.1017/s0269964809000242Constantinides, 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. doi:10.1287/opre.26.4.620Moraes, M. B. da C., & Nagano, M. S. (2014). Evolutionary models in cash management policies with multiple assets. Economic Modelling, 39, 1-7. doi:10.1016/j.econmod.2014.02.010Da Costa Moraes, M. B., Nagano, M. S., & Sobreiro, V. A. (2015). Stochastic Cash Flow Management Models: A Literature Review Since the 1980s. Decision Engineering, 11-28. doi:10.1007/978-3-319-11949-6_2De Avila Pacheco, J. V., & Morabito, R. (2011). Application of network flow models for the cash management of an agribusiness company. Computers & Industrial Engineering, 61(3), 848-857. doi:10.1016/j.cie.2011.05.018Girgis, N. M. (1968). Optimal Cash Balance Levels. Management Science, 15(3), 130-140. doi:10.1287/mnsc.15.3.130Golden, B., Liberatore, M., & Lieberman, C. (1979). Models and solution techniques for cash flow management. Computers & Operations Research, 6(1), 13-20. doi:10.1016/0305-0548(79)90010-8Gormley, 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. doi:10.1016/j.ejor.2006.07.041Gregory, G. (1976). Cash flow models: A review. Omega, 4(6), 643-656. doi:10.1016/0305-0483(76)90092-xGurobi Optimization, Inc (2017) Gurobi optimizer reference manual, Houston.Keown, A. J., & Martin, J. D. (1977). A Chance Constrained Goal Programming Model for Working Capital Management. The Engineering Economist, 22(3), 153-174. doi:10.1080/00137917708965174Miller, M. H., & Orr, D. (1966). A Model of the Demand for Money by Firms. The Quarterly Journal of Economics, 80(3), 413. doi:10.2307/1880728Neave, E. H. (1970). The Stochastic Cash Balance Problem with Fixed Costs for Increases and Decreases. Management Science, 16(7), 472-490. doi:10.1287/mnsc.16.7.472PARK, C. S., & HERATH, H. S. B. (2000). EXPLOITING UNCERTAINTY—INVESTMENT OPPORTUNITIES AS REAL OPTIONS: A NEW WAY OF THINKING IN ENGINEERING ECONOMICS. The Engineering Economist, 45(1), 1-36. doi:10.1080/00137910008967534Penttinen, M. J. (1991). Myopic and stationary solutions for stochastic cash balance problems. European Journal of Operational Research, 52(2), 155-166. doi:10.1016/0377-2217(91)90077-9Rockafellar, R. T., & Uryasev, S. (2002). Conditional value-at-risk for general loss distributions. Journal of Banking & Finance, 26(7), 1443-1471. doi:10.1016/s0378-4266(02)00271-6Salas-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. doi:10.1016/j.ijforecast.2016.11.002Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2016). A multi-objective approach to the cash management problem. Annals of Operations Research, 267(1-2), 515-529. doi:10.1007/s10479-016-2359-1Srinivasan, V., & Kim, Y. H. (1986). Deterministic cash flow management: State of the art and research directions. Omega, 14(2), 145-166. doi:10.1016/0305-0483(86)90017-4Stone, B. K. (1972). The Use of Forecasts and Smoothing in Control-Limit Models for Cash Management. Financial Management, 1(1), 72. doi:10.2307/3664955Stone, B. K., & Miller, T. W. (1987). Daily Cash Forecasting with Multiplicative Models of Cash Flow Patterns. Financial Management, 16(4), 45. doi:10.2307/3666108Xu, X., & Birge, J. R. (2008). Operational Decisions, Capital Structure, and Managerial Compensation: A News Vendor Perspective. The Engineering Economist, 53(3), 173-196. doi:10.1080/00137910802262887Yu, P.-L. (1985). Multiple-Criteria Decision Making. doi:10.1007/978-1-4684-8395-

    DATA-DRIVEN DECISION-MAKING AND ITS APPLICATION TO THE CORPORATE CASH MANAGEMENT PROBLEM

    Full text link
    Esta tesis investiga el problema de gestión de tesorería desde un punto de vista multidimensional. La gestión de tesorería trata de equilibrar la cantidad que se mantiene en efectivo y la que se dedica a inversiones a corto plazo. Normalmente, los tesoreros toman decisiones basándose en el nivel óptimo de tesorería por motivos operativos y de precaución. En esta tesis exploramos las oportunidades para mejorar la toma decisiones derivadas de modelar la incertidumbre presente en los flujos de caja con la ayuda de procedimientos basados en datos en un entorno multiobjetivo. Por un lado, los tesoreros pueden conseguir ahorros a través de la previsión de tesorería. Para ello, realizamos un estudio empírico con el objetivo de aprovechar las más recientes técnicas de aprendizaje automático como paso clave para conectar el análisis de los datos disponibles con los procesos de optimización en la gestión de tesorería. Por otro lado, los tesoreros pueden estar interesados no solo en el coste sino también en al riesgo asociado a sus decisiones. Por esta razón, tratamos el problema de gestión de tesorería desde una perspectiva multiobjetivo, considerando tanto el coste como el riesgo. Además, debido a la cambiante situación financiera actual, exploramos la selección de modelos de gestión de tesorería en función de diferentes condiciones operativas y de su robustez. También demostramos la utilidad de las previsiones a través de un nuevo modelo de gestión de tesorería que mejora el estado del arte al garantizar soluciones óptimas. Como la mayoría de las empresas trabaja con sistemas de tesorería con múltiples cuentas bancarias, desarrollamos un marco para la formulación y solución del problema de gestión de tesorería con múltiples cuentas bancarias. Finalmente, en un intento de acercar teoría y práctica, también ofrecemos una librería de software en Python para usuarios interesados en la construcción de sistemas de ayuda a la toma de decisiones en gestión de tesorería.This thesis investigates the cash management problem from a multidimensional perspective. Cash management focuses on finding the balance between cash holdings and short-term investments. Typically, cash managers make decisions based usually on a firm's optimal cash balance for operational and precautionary purposes. We here explore the opportunities for improved decision-making derived from modeling cash flow uncertainty with the help of data-driven procedures within a multiobjective context. On the one hand, cash managers may achieve cost savings by forecasting future cash flows. To this end, we perform an empirical analysis of daily cash flow time-series to take advantage of modern machine learning techniques as a key step to connect data analysis and optimization methods in cash management. On the other hand, cash managers may be interested not only in the cost but also in the risk associated to decision-making. Thus, we address the cash management problem from a multiobjective perspective focusing on both cost and risk. In addition, under the current situation of time-varying financial circumstances, the selection of cash management models according to operating conditions and its robustness are worth considering questions. We also show the utility of forecasts through a new cash management model which outperforms the state-of-the-art by guaranteeing optimal solutions. Since most firms usually deal with cash management systems with multiple accounts, we develop a framework to formulate and solve the multiple bank accounts cash management problem. Finally, in an attempt to fill the gap between theory and practice, we also provide a software library in Python for practitioners interested in building decision support systems for cash management.Esta tesi investiga el problema de gestió de tresoreria des d'un punt de vista multidimensional. La gestió de tresoreria tracta d'equilibrar la quantitat que es manté en efectiu i la que es dedica a inversions a curt termini. Normalment, el tresorers prenen decisions basant-se en el nivell òptim de tresoreria per motius operatius i de precaució. En aquesta tesi explorem les oportunitats per millorar la presa de decisions derivades de modelitzar la incertesa present en els fluxos de caixa amb l'ajuda de procediments basats en dades. Per un costat, els tresorers poden aconseguir estalvis de costos mitjançant la previsió de tresoreria. Per tal d'aconseguir-ho, realitzem d'un estudi empíric amb l'objectiu d'aprofitar les més recents tècniques d'aprenentatge automàtic per connectar l'anàlisi de les dades disponbiles amb els procesos d'optimització en la gestió de tresoreria. Per altra banda, els tresorers poden estar interessats no sols en el cost sinó també en el risc associat a les seues decisions. Per tant, tractem el problema de gestió de tresoreria des d'un punt de vista multiobjectiu, fixant-se tant en el cost com en el risc. A més a més, degut a la canviant situació financera actual, explorem la selecció de models de gestió de tresoreria en funció de diferents condicions operatives i de la seua robustesa. També demostrem la utilitat de les previsions mitjançant un nou model de tresoreria que millora l'estat de l'art al garantir solucions òptimes. Com que la majoria d'empreses treballa amb sistemes de tresoreria amb múltiples comptes bancaris, desenvolupem un marc per a la formulació i solució del problema de gestió de tresoreria amb múltiples comptes bancaris. Finalment, en un intent d'apropar teoria i pràctica, també oferim un llibreria en Python per a usuaris interessats en la construcció de sistemes d'ajuda a la presa de decisions en la gestió de tresoreria.Salas Molina, F. (2017). DATA-DRIVEN DECISION-MAKING AND ITS APPLICATION TO THE CORPORATE CASH MANAGEMENT PROBLEM [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/95408TESI

    A multidimensional review of the cash management problem

    Full text link
    In this paper, we summarize and analyze the relevant research on the cash management problem appearing in the literature. First, we identify the main dimensions of the cash management problem. Next, we review the most relevant contributions in this field and present a multidimensional analysis of these contributions, according to the dimensions of the problem. From this analysis, several open research questions are highlighted

    Formulating the imputed cost of equity capital for priced services at Federal Reserve banks

    Get PDF
    This paper was presented at the conference "Economic Statistics: New Needs for the Twenty-First Century," cosponsored by the Federal Reserve Bank of New York, the Conference on Research in Income and Wealth, and the National Association for Business Economics, July 11, 2002. According to the 1980 Monetary Control Act, the Federal Reserve Banks must establish fees for their priced services to recover all operating costs as well as the imputed costs of capital and taxes that would be incurred by a profit-making firm. Since 2002, the Federal Reserve has made fundamental changes to the calculations used to set the imputed costs. This article describes and analyzes the current approach, which is based on a simple average of three methods as applied to a peer group of bank holding companies. The methods estimate the cost of equity capital from three perspectives_the historical average of comparable accounting earnings, the discounted value of expected future cash flows, and the equilibrium price of investment risk as per the capital asset pricing model. The authors show that the current approach also provides stable and sensible estimates of the cost of equity capital over the past twenty years.Federal Reserve banks - Service charges ; Bank holding companies ; Bank capital ; Depository Institutions Deregulation and Monetary Control Act of 1980

    Interest Rates and Their Prospect in the Recovery

    Get PDF
    macroeconomics, interest rates

    The use of intellectual capital information by sell-side analysts in company valuation

    Get PDF
    This paper investigates the role of intellectual capital information (ICI) in sell-side analysts’ fundamental analysis and valuation of companies. Using in-depth semi-structured interviews, it penetrates the black box of analysts’ valuation decision-making by identifying and conceptualising the mechanisms and rationales by which ICI is integrated within their valuation decision processes. We find that capital market participants are not ambivalent to ICI, and ICI is used: (1) to form analysts’ perceptions of the overall quality, strengths and future prospects of companies; (2) in deriving valuation model inputs; (3) in setting price targets and making investment recommendations; and (4) as an important and integral element in analyst–client communications. We show that: there is a ‘pecking order’ of mechanisms for incorporating ICI in valuations, based on quantifiability; IC valuation is grounded in valuation theory; there are designated entry points in the valuation process for ICI; and a number of factors affect analysts’ ICI use in valuation. We also identify a need to redefine ‘value-relevant’ ICI to include non-price-sensitive information; acknowledge the boundedness and contextuality of analysts’ rationality and motives of their ICI use; and the important role of analyst–client meetings for ICI communication
    corecore