Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/306232 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Intelligent Systems in Accounting, Finance and Management [ISSN:] 1099-1174 [Volume:] 31 [Issue:] 3 [Article No.:] e1565 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2024
Verlag: 
Wiley, Hoboken, NJ
Zusammenfassung: 
Liquidity planning and forecasting are essential activities in corporate financial planning team. Traditionally, empirical models and techniques based on in‐house expertise have been used to navigate the numerous challenges of this forecasting activity. These challenges become more complex when the forecasting activities are extended to subsidiaries of a large firm. This paper presents a structured approach that utilizes 240 covariates to predict net liquidity, customer receipts, and payments to suppliers to improve the accuracy and efficiency of liquidity forecasting in subsidiaries and at the corporate level. The approach is empirically validated on a large corporation headquartered in Germany, with average annual revenue from 6 to 7 billion Euro spanning 80 countries. The proposed approach demonstrated superior performance over existing methods in six out of nine forecasts using the data from 2014 to 2018. These findings suggest that a firm's classical approach to liquidity forecasting can be effectively challenged and outperformed by the algorithmic approach.
Schlagwörter: 
corporate finance planning
forecast
liquidity planning
machine learning algorithms
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