Empowering cash managers to achieve cost savings by improving predictive accuracy

Cash management is concerned with optimizing the short-term funding requirements of a company. To this end, different optimization strategies have been proposed to minimize costs using daily cash flow forecasts as the main input to the models. However, the effect of the accuracy of such forecasts on cash management policies has not been studied. In this article, using two real data sets from the textile industry, we show that predictive accuracy is highly correlated with cost savings when using daily forecasts in cash management models. A new method is proposed to help cash managers estimate if efforts in improving predictive accuracy are proportionally rewarded by cost savings. Our results imply the need for an analysis of potential cost savings derived from improving predictive accuracy. From that, the search for better forecasting models is in place to improve cash management.

[1]  A. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[2]  George Athanasopoulos,et al.  Forecasting: principles and practice , 2013 .

[3]  Michael Anthony Bauer,et al.  XLPM: efficient algorithm for the analysis of protein-protein contacts using chemical cross-linking mass spectrometry , 2014, BMC Bioinformatics.

[4]  Rob J Hyndman,et al.  25 years of time series forecasting , 2006 .

[5]  Matthew Scotch,et al.  Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks , 2014, BMC Bioinformatics.

[6]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[7]  V. Srinivasan,et al.  Deterministic cash flow management: State of the art and research directions , 1986 .

[8]  D. Cox,et al.  An Analysis of Transformations , 1964 .

[9]  Merton H. Miller,et al.  A Model of the Demand for Money by Firms , 1966 .

[10]  B. Stone.  Cash Planning and Credit-Line Determination With a Financial Statement Simulator: A Case Report on Short-Term Financial Planning , 1973 .

[11]  Ronald Harley,et al.  A random forest method for real-time price forecasting in New York electricity market , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[12]  Jurgen A. Doornik,et al.  Encompassing and Automatic Model Selection , 2008 .

[13]  E. Fama,et al.  CASH BALANCE AND SIMPLE DYNAMIC PORTFOLIO PROBLEMS WITH PROPORTIONAL COSTS , 1969 .

[14]  Fotios Petropoulos,et al.  forecast: Forecasting functions for time series and linear models , 2018 .

[15]  Adam Zagorecki,et al.  Prediction of Methane Outbreaks in Coal Mines from Multivariate Time Series Using Random Forest , 2015, RSFDGrC.

[16]  Manish Kumar,et al.  Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest , 2006 .

[17]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[18]  Marcelo Seido Nagano,et al.  Evolutionary models in cash management policies with multiple assets , 2014 .

[19]  Bernell K. Stone.  The Payments-Pattern Approach to the Forecasting and Control of Accounts Receivable , 1976 .

[20]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[21]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Fionnuala M. Gormley,et al.  The utility of cash flow forecasts in the management of corporate cash balances , 2007, Eur. J. Oper. Res..

[23]  Patrick Siarry,et al.  A Continuous Genetic Algorithm Designed for the Global Optimization of Multimodal Functions , 2000, J. Heuristics.

[24]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[25]  G. Gregory,et al.  Cash flow models: A review , 1976 .

[26]  Jurgen A. Doornik,et al.  Empirical Model Discovery and Theory Evaluation: Automatic Selection Methods in Econometrics , 2014 .

[27]  Hans G. Daellenbach,et al.  Are Cash Management Optimization Models Worthwhile? , 1974, Journal of Financial and Quantitative Analysis.

[28]  Antonio Criminisi,et al.  Decision Forests for Computer Vision and Medical Image Analysis , 2013, Advances in Computer Vision and Pattern Recognition.

[29]  H. Kantz,et al.  Nonlinear time series analysis , 1997 .

[30]  N. M. Girgis.  Optimal Cash Balance Levels , 1968 .

[31]  Derek W. Bunn,et al.  Forecasting Electricity Prices , 2003 .

[32]  Algirdas Laukaitis,et al.  Functional data analysis for cash flow and transactions intensity continuous-time prediction using Hilbert-valued autoregressive processes , 2008, Eur. J. Oper. Res..

[33]  J. Contreras,et al.  Forecasting electricity prices for a day-ahead pool-based electric energy market , 2005 .

[34]  Steven F. Maier,et al.  A Short-Term Disbursement Forecasting Model , 1981 .

[35]  Michael D. Bradley,et al.  Forecasting with a nonlinear dynamic model of stock returns and industrial production , 2004 .

[36]  B. Stone,et al.  Daily Cash Forecasting: A Simple Method for Implementing the Distribution Approach , 1977 .

[37]  Thomas W. Miller,et al.  Daily Cash Forecasting with Multiplicative Models of Cash Flow Patterns , 1987 .

[38]  B. Stone,et al.  Daily Cash Forecasting and Seasonal Resolution: Alternative Models and Techniques for Using the Distribution Approach , 1985, Journal of Financial and Quantitative Analysis.

[39]  Timo Teräsvirta,et al.  Forecasting economic variables with nonlinear models , 2005 .

[40]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[41]  Marcelo Seido Nagano,et al.  Stochastic Cash Flow Management Models: A Literature Review Since the 1980s , 2015 .

[42]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[43]  Nicholas Sarantis,et al.  Nonlinearities, cyclical behaviour and predictability in stock markets: international evidence , 2001 .

[44]  Timo Teräsvirta,et al.  Chapter 8 Forecasting economic variables with nonlinear models , 2006 .

[45]  F. Black,et al.  The Pricing of Options and Corporate Liabilities , 1973, Journal of Political Economy.

[46]  William J. Baumol,et al.  The Transactions Demand for Cash: An Inventory Theoretic Approach , 1952 .

[47]  Bernell K. Stone,et al.  The Use of Forecasts and Smoothing in Control-Limit Models for Cash Management , 1972 .

[48]  Douglas A. Wolfe,et al.  Nonparametric Statistical Methods , 1973 .

[49]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[50]  H. Akaike.  A new look at the statistical model identification , 1974 .

[51]  Michael P. Clements,et al.  Forecasting economic and financial time-series with non-linear models , 2004 .

[52]  M. Small.  Applied Nonlinear Time Series Analysis: Applications in Physics, Physiology and Finance , 2005 .

[53]  Markku Penttinen,et al.  Myopic and stationary solutions for stochastic cash balance problems , 1991 .

[54]  Hae-Sang Park,et al.  A simple and fast algorithm for K-medoids clustering , 2009, Expert Syst. Appl..