Abstract
Daily cash flow forecasting is important for maintaining company financial liquidity, improves resource allocation, and aids financial managers in decision making. On the one hand, it helps to avoid excessive amounts of cash outstanding on company’s account, and on the other hand, it helps to avoid liquidity issues. This area isn’t popular for research though - literature on the topic is limited and has a major shortcoming - in most cases publicly unavailable datasets are used. It can be attributed to generally smaller availability of financial data than in other fields, but there are two issues arising from such situation - not reproducible results of existing work and the area being less approachable by new potential researchers. The goal of this paper is two-fold. Firstly, it is reviewing existing literature, methods, and datasets used, together with details provided on those datasets. Secondly, it is exploring publicly available datasets to be used in further research, containing either cash flows directly or data from which cash flows can be derived.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Account Receivables Dataset on Kaggle (IBM Late Payment Histories). https://www.kaggle.com/hhenry/finance-factoring-ibm-late-payment-histories. Accessed 10 Mar 2022
Dataset Search from Google. https://datasetsearch.research.google.com/. Accessed 10 Mar 2022
data.world datasets. https://data.world/datasets/open-data. Accessed 10 Mar 2022
Kaggle datasets. https://www.kaggle.com/datasets. Accessed 10 Mar 2022
M5 Forecasting competition dataset. https://www.kaggle.com/c/m5-forecasting-accuracy/data. Accessed 10 Mar 2022
Nasdaq Data Link. https://data.nasdaq.com/. Accessed 10 Mar 2022
Online retail II dataset on UCI Machine Learning Repository. https://archive-beta.ics.uci.edu/ml/datasets/online+retail+ii. Accessed 10 Mar 2022
Predict Future Sales (final project dataset for Coursera course). https://www.kaggle.com/c/competitive-data-science-predict-future-sales/data. Accessed 10 Mar 2022
Registry of Open Data on AWS. https://registry.opendata.aws/. Accessed 10 Mar 2022
UCI Machine Learning Repository. https://archive.ics.uci.edu/. Accessed 10 Mar 2022
Unlabelled dataset on GitHub. https://github.com/SkywalkerHub/Payment-Date-Prediction/blob/main/Dataset.csv. Accessed 10 Mar 2022
Chen, S., et al.: Cash flow forecasting model for electricity sale based on deep recurrent neural network. In: 2019 IEEE International Conference on Power Data Science (ICPDS), pp. 67–70. IEEE (2019)
Cheng, M.Y., Hoang, N.D., Wu, Y.W.: Cash flow prediction for construction project using a novel adaptive time-dependent least squares support vector machine inference model. J. Civ. Eng. Manage. 21(6), 679–688 (2015)
Cheng, M.Y., Roy, A.F.: Evolutionary fuzzy decision model for cash flow prediction using time-dependent support vector machines. Int. J. Project Manage. 29(1), 56–65 (2011)
Kayacan, E., Ulutas, B., Kaynak, O.: Grey system theory-based models in time series prediction. Expert Syst. Appl. 37(2), 1784–1789 (2010)
Manuca, R., Savit, R.: Stationarity and nonstationarity in time series analysis. Physica D 99(2–3), 134–161 (1996)
Martínez, F., Frías, M.P., Pérez-Godoy, M.D., Rivera, A.J.: Dealing with seasonality by narrowing the training set in time series forecasting with knn. Expert Syst. Appl. 103, 38–48 (2018)
Nguyen, L., Novák, V.: Forecasting seasonal time series based on fuzzy techniques. Fuzzy Sets Syst. 361, 114–129 (2019)
Taylor, S.J., Letham, B.: Forecasting at scale. Am. Stat. 72(1), 37–45 (2018)
Wang, J.S., Ning, C.X., Cui, W.H.: Time series prediction of bank cash flow based on grey neural network algorithm. In: 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF), pp. 272–277. IEEE (2015)
Weytjens, H., Lohmann, E., Kleinsteuber, M.: Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet. Electron. Commer. Res. 21(2), 371–391 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Małkus, B., Nalepa, G.J. (2023). Forecasting Daily Cash Flows in a Company - Shortcoming in the Research Field and Solution Exploration. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13810. Springer, Cham. https://doi.org/10.1007/978-3-031-25599-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-25599-1_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-25598-4
Online ISBN: 978-3-031-25599-1
eBook Packages: Computer ScienceComputer Science (R0)