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Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet

Author

Listed:
  • Hans Weytjens
  • Enrico Lohmann

    (Mercateo AG)

  • Martin Kleinsteuber

    (Mercateo AG
    Technische Universität München (TUM))

Abstract

Cash flow prediction is important. It can help increase returns and improve the allocation of capital in healthy, mature firms as well as prevent fast-growing firms, or firms in distress, from running out of cash. In this paper, we predict accounts receivable cash flows employing methods applicable to companies with many customers and many transactions such as e-commerce companies, retailers, airlines and public transportation firms with sales in multiple regions and countries. We first discuss “classic” forecasting techniques such as ARIMA and Facebook's™ Prophet before moving on to neural networks with multi-layered perceptrons and, finally, long short-term memory networks, that are particularly useful for time series forecasting but were until now not used for cash flows. Our evaluation demonstrates this range of methods to be of increasing sophistication, flexibility and accuracy. We also introduce a new performance measure, interest opportunity cost, that incorporates interest rates and the cost of capital to optimize the models in a financially meaningful, money-saving, way.

Suggested Citation

  • Hans Weytjens & Enrico Lohmann & Martin Kleinsteuber, 2021. "Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet," Electronic Commerce Research, Springer, vol. 21(2), pages 371-391, June.
    • Handle: RePEc:spr:elcore:v:21:y:2021:i:2:d:10.1007_s10660-019-09362-7
    DOI: 10.1007/s10660-019-09362-7
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    References listed on IDEAS

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    1. Ke Gong & Yi Peng & Yong Wang & Maozeng Xu, 2018. "Time series analysis for C2C conversion rate," Electronic Commerce Research, Springer, vol. 18(4), pages 763-789, December.
    2. Qian Wang & Jijun Yu & Weiwei Deng, 2019. "An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations," Electronic Commerce Research, Springer, vol. 19(1), pages 59-79, March.
    3. Shasha Liu & Bingjia Shao & Yuan Gao & Su Hu & Yi Li & Weigui Zhou, 2018. "Game theoretic approach of a novel decision policy for customers based on big data," Electronic Commerce Research, Springer, vol. 18(2), pages 225-240, June.
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    Cited by:

    1. Zhang, Junting & Liu, Haifei & Bai, Wei & Li, Xiaojing, 2024. "A hybrid approach of wavelet transform, ARIMA and LSTM model for the share price index futures forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 69(PB).

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