Interfaces with Other DisciplinesAnalytical debiasing of corporate cash flow forecasts
Introduction
Cash flow forecasts play a pivotal role in corporate financial management tasks. For instance, the forecasts are used in liquidity management to ensure solvency and in foreign-exchange risk management to identify and hedge exposures resulting from foreign business activities. Inaccurate forecasts are an unreliable basis for corporation-wide financial plans and can lead to liquidity shortages, uncovered currency risks or increased hedging costs. In large multinational companies, cash flow forecasts are prepared for individual subsidiaries from different countries and distinct business divisions. The cash flows are predicted by local financial managers who report the forecasts to the corporation’s central finance department, where corporation-wide financial plans are derived from all delivered forecasts.
Since the cash flow forecasts are typically generated by human experts with different backgrounds, attitudes and individual forecasting procedures, the forecasts are likely to be biased by behavioral or political dimensions, overall leading to reduced forecast accuracy. Biased judgmental forecasting and decision-making are phenomena observed in many contexts. In numerous studies and laboratory experiments significant biases are found regularly in various forecasts, decreasing the accuracy and negatively influencing business performance (Leitner and Leopold-Wildburger, 2011). For instance, in a study by Lawrence, O’Connor, and Edmundson (2000), errors in sales forecasts of three manufacturing-based companies were attributed mainly to inefficiencies and biases. In another study, Enns (2002) analyzed the influence of biased and uncertain demand forecasts on production scheduling and found that biases significantly influence lateness of the delivery to the customer.
There are several empirical studies providing further strong evidence for the existence of cognitive biases in expert forecasting, such as the well-known anchoring and adjustment. Hogarth and Makridakis (1981) and Lawrence, Goodwin, O’Connor, and Oenkal (2006) provide extensive overviews of judgmental forecasting and its heuristics and biases. Although the effect can be mitigated and reduced with adequate decision support systems (George, Duffy, Ahuja, 2000, Remus, Kottemann, 1995), it is regularly observed that information provided by decision support systems is undervalued and biases can therefore only be partly removed (Bhandari, Hassanein, Deaves, 2008, Lim, O’Connor, 1996). Overall, it is likely that corporate cash flow forecasts in multinational companies are also biased and that these biases influence decision models.
For instance, Gormley and Meade (2007) consider the problem of corporate-wide cash balance management using short-term cash flow forecasts. Based on data from a large international company, the authors show that transactional costs strongly depend on the accuracy of the cash flow forecasts used as input for financial planning.
Unfortunately, while in general there is awareness of the importance of accurate financial forecasts for corporate planning and control (Graham, Harvey, 2001, Kim, Mauer, Sherman, 1998), there is practically no research available that empirically analyzes corporate cash flow forecasts. This is particularly true regarding the identification, interpretation, and finally mitigation of judgmental biases in forecasts. Hence, corporate financial controllers have little guidance on how to assess and improve the quality of their heterogeneous cash flow forecasts. In our context, identification and removal of biases before the forecasts are used in corporation-wide planning activities could lead to increased financial efficacy.
In this paper we conduct an empirical analysis of statistical approaches to debiasing cash flow forecasts using a unique set of corporate cash flow forecasts with different horizons. The data are provided by a large international corporation and generated by experts from 34 legal entities in various countries over a period of six years and for several currencies. We analyze and discuss the impact of statistical correction methods for different estimation techniques and parameters.
To our knowledge, this is the first work to decompose, quantify, and correct cash flow forecast biases in corporate settings. More generally, we are not aware of any empirical work analyzing biases in large sets of heterogeneous enterprise financial forecast data. More important, we are not aware of scientific publications that analyze the parameters learned by correction models, although these might provide valuable insights.
Our research makes several contributions to the literature. First, we find that different types of biases exist in the empirical cash flow forecast data of our sample company. Second, we find that substantial improvements of forecast accuracy can be achieved by debiasing forecasts using statistical techniques. Third, we analyze and compare the parameters learned by the correction models and find significant differences between the respective parameters learned in different business environments. We show that the learned parameters can be related to the characteristics of the business the forecast has been generated in and argue that the parameters provide valuable insights for corporate financial controllers to better understand, quantify, feedback, and systematically mitigate biases over time.
Section snippets
Forecast debiasing methods
Many studies report successful debiasing of expert forecasts with statistical correction techniques. The most common approach is Theil’s method (Theil, 1966), which is based on a decomposition of the mean squared error (MSE). For a time series of length T with actuals A1, …, AT and corresponding forecasts F1, …, FT, the metric is defined as .
With means and standard deviations sF, sA of the forecasts and actuals, and r as the correlation between forecasts and actuals, the
Empirical data and research design
In this section, we characterize our sample company and the available dataset before we describe the design of our empirical analysis. Results are presented subsequently in Section 4.
Experimental results
Aggregated results are presented in Table 4. Each row in the table corresponds to one of the correction techniques and parameterizations (henceforth we will refer to a technique-parameter combination as correction model or simply model). The table shows median ΔAPE achieved with a model over all forecasts and individually for each of the six division/invoice type subsamples ({AP, HP, IM} crossed with {Invoice Received (IR), Invoice Issued (II)}). The rows are ranked descending by median ΔAPE in
Summary and conclusion
In this paper, we empirically analyzed statistical error correction methods for cash flows forecasts generated by individual experts in the context of corporate financial controlling. While there is awareness of the importance of accurate financial forecasts for corporate planning and control, there is practically no research available that empirically analyzes corporate cash flow forecasts. Furthermore, there is no previous work on the selection and parameterization of error correction
References (35)
Explaining the pricevolume relationship: The difference between price changes and changing prices
Organizational Behavior and Human Decision Processes
(1988)- et al.
Error measures for generalizing about forecasting methods: Empirical comparisons
International Journal of Forecasting
(1992) - et al.
Loss function assumptions in rational expectations tests on financial analysts earnings forecasts
Journal of Accounting and Economics
(2004) - et al.
Debiasing investors with decision support systems: An experimental investigation
Decision Support Systems
(2008) MRP performance effects due to forecast bias and demand uncertainty
European Journal of Operational Research
(2002)- et al.
Countering the anchoring and adjustment bias with decision support systems
Decisions Support Systems
(2000) Statistical forecasting—The state of the art
Omega—The International Journal of Management Science
(1974)- et al.
Robust weighted LAD regression
Computational Statistics & Data Analysis
(2006) Statistical correction of judgmental point forecasts and decisions
Omega
(1996)Correct or combine? Mechanically integrating judgmental forecasts with statistical methods
International Journal of Forecasting
(2000)
The utility of cash flow forecasts in the management of corporate cash balances
European Journal of Operational Research
The theory and practice of corporate finance: Evidence from the field
Journal of Financial Economics
Why are judgements less consistent in less predictable task situations?
Organizational Behavior and Human Decision Processes
Scale, randomness and the calibration of judgmental confidence intervals
Organizational Behavior and Human Decision Processes
A field study of sales forecasting accuracy and processes
European Journal of Operational Research
Experiments on forecasting behavior with several sources of information—A review of the literature
European Journal of Operational Research
Judgmental forecasting with interactive forecasting support systems
Decision Support Systems
Cited by (12)
Fast evaluation of crack growth path using time series forecasting
2019, Engineering Fracture MechanicsCitation Excerpt :In this study, we promote a new approach for forecasting the crack propagation based on machine learning algorithms. The main idea is to rely on time series forecasting which is a crucial issue in many disciplines [65,25,28,57,31,26,56,41,59,5,4,8,36], because the future values can be explored from its past ones with the lowest devised error. Forecasting problems based on machine learning have been successfully applied in various areas such as the economy, finance and hydrology.
News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions
2019, European Journal of Operational ResearchCitation Excerpt :Hence, it is likely that these forecasts Numerous studies have analyzed professional forecasts for their predictive performance and ability to identify potential biases (Blanc & Setzer, 2015; Mostard, Teunter, & de Koster, 2011). In the field of macroeconomic forecasting, a recent study (Dovern & Weisser, 2011) finds that the distribution of a forecasts accuracy varies significantly across indicators, forecasters and nations.
How do turbulent sectoral conditions sector influence the value of coal mining enterprises? Perspectives from the Central-Eastern Europe coal mining industry
2018, Resources PolicyCitation Excerpt :In light of this fact and in order to contribute to the general theory of value management, this article was written from the perspective of the coal mining industry. A majority of value measurement methods involve the financial profits of an enterprise (Bąk and Sierpińska-Sawicz, 2016; Kumar, 2016; Blasco and Ribal, 2013; Blanc and Setzer, 2015; Jennergren, 2008). The accounting approach uses data from balance sheets on net assets, but the value of the assets and their changes in time directly depend on the profit amount (Kowalska-Styczeń and Owczarek, 2016; Qu and Zhang, 2015; Kraus and Strömsten, 2012; Brodny, 2012).
The applicability of machine learning algorithms in accounts receivables management
2023, Journal of Applied Accounting ResearchFeeding-Back Error Patterns to Stimulate Self-Reflection versus Automated Debiasing of Judgments
2023, Proceedings of the Annual Hawaii International Conference on System SciencesCash flow prediction using artificial neural network and GA-EDA optimization
2019, Journal of Project Management (Canada)