全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

企业应收账款回款时间预测与应用
Prediction and Application of Enterprise Accounts Receivable Repayment Time

DOI: 10.12677/aam.2024.135197, PP. 2096-2104

Keywords: 应收账款,回款时间预测,蒙特卡洛模拟
Accounts Receivable
, Repayment Time Prediction, Monte Carlo Simulation

Full-Text   Cite this paper   Add to My Lib

Abstract:

选取某企业2020~2023年的应收账款真实数据为样本,针对不同类型的客户,分别建立基于客户个体习惯的预测模型、回归预测模型以及蒙特卡洛模拟预测模型,以预测其回款时间。预测结果表明,总体预测效果较好,更有部分月份预测误差低于1%。这些预测模型有助于企业更好地管理现金流、降低风险、进行财务规划,还为企业财务规划、战略决策和市场定位提供有力支持。因此,将本研究预测模型应用于企业财务管理具有重要的实践意义。
The real data of accounts receivable of an enterprise from 2020 to 2023 are selected as samples. For different types of customers, the prediction model, regression prediction model and Monte Carlo simulation prediction model based on customers’ individual habits are established respectively to predict their repayment time. The prediction results show that the overall prediction effect is better, and the prediction error of some months is less than 1 %. These prediction models help enterprises to better manage cash flow, reduce risks, carry out financial planning, and provide strong support for corporate financial planning, strategic decision-making and market positioning. Therefore, it is of great practical significance to apply the prediction model of this study to enterprise financial management.

References

[1]  占济舟, 张福利, 赵佳宝. 供应链应收账款融资和商业信用联合决策研究[J]. 系统工程学报, 2014, 29(3): 384-393 432.
[2]  陈祥锋. 供应链金融服务创新论[M]. 上海: 复旦大学出版社, 2008.
[3]  王群, 杨公遂. 经济政策不确定性、会计稳健性与商业信用供给——基于应收账款的分析[J]. 财会通讯, 2023(3): 27-30.
[4]  孙玉国. 如何建立应收账款动态预算体系[J]. 四川会计, 2001(8): 31-32.
[5]  Richard, I., Smith, J.K. and Ng, C.K. (2010) Evidence on the Determinants of Credit Terms Used in Interfirm Trade. Journal of Finance, 54, 1109-1129.
https://doi.org/10.1111/0022-1082.00138
[6]  Abuhommous, A.A. and Mashoka, T. (2018) A Dynamic Approach to Accounts Receivable: The Case of Jordanian Firms. Eurasian Business Review, 8, 171-191.
https://doi.org/10.1007/s40821-017-0074-8
[7]  Stephen, F.J. (1981) A Transactions Theory of Trade Credit Use. Quarterly Journal of Economics, 96, 243-270.
https://doi.org/10.2307/1882390
[8]  Jegers, D.M. (1996) Special Issue: European Corporate Finance || Trade Credit, Product Quality, and Intragroup Trade: Some European Evidence. Financial Management, 25, 33-43.
[9]  Petersen, M.A. and Rajan, R.G. (1997) Trade Credit: Theories and Evidence. Review of Financial Studies, 10, 661-691.
https://doi.org/10.1093/rfs/10.3.661
[10]  Niskanen, J. and Niskanen, M. (2006) The Determinants of Corporate Trade Credit Policies in a Bank-Dominated Financial Environment: The Case of Finnish Small Firms. European Financial Management, 12, 81-102.
https://doi.org/10.1111/j.1354-7798.2006.00311.x
[11]  Appel, A.P., Malfatti, G.L., Cunha, R.L.D.F., et al. (2020) Predicting Account Receivables with Machine Learning. 2021 International Conference on Information Technology, 23-27 August 2020, San Diego, 151-161.
[12]  马俊伟. Lévy分布下期权蒙特卡洛模拟定价模型[D]: [博士学位论文]. 成都: 西南财经大学, 2014.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133