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Forecasting and Planning of Financing of Economically Disadvantaged Enterprises Based on the ARIMA Model
Corresponding Author(s) : Nuriddin Javliev
American Journal of Economics and Business Management,
Vol. 7 No. 12 (2024): December
Abstract
This article analyzes the causes of the economic crisis arising in the conditions of economic instability, slow payment circulation, risk of non-payment, current inflation and insufficient qualifications of managers. In this direction, researches carried out by economists are studied and their opinions are presented. In fact, as a result of special attention paid to entrepreneurship by the Uzbek government in recent years, the entrepreneurship sector has become a leading branch of the economy. Scientific research dedicated to the development of a financial mechanism on the subject is also important for innovative development and increasing social welfare. Proposals and recommendations aimed at supporting and developing various sectors of the economy were developed through these processes.
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- Aidas, Malakauskas., Aušrinė, Lakštutienė. (2021). Financial distress prediction for small and medium enterprises using machine learning techniques. The Engineering Economics, 32(1):4-14. doi: 10.5755/J01.EE.32.1.27382.
- Anzhela, Y., Petrova., Margarita, Deyneka. (2022). Arima-models: modeling and forecasting prices of stocks. Internauka, doi: 10.25313/2520-2294-2022-2-7921
- Bokai, Zhang. (2019). Research on Fixed Assets Investment Forecast Based on ARIMA Model. doi: 10.1109/ICEMME49371.2019.00083
- Bruce, Chapman., Ric, Simes. (2005). Profit Related Loans for Economically Disadvantaged Regions. Research Papers in Economics,
- Dong, Mei, Li., Kaiyao, Xu., Yun, Daisy, Li., Yu, Jiang., Ming, Tang., Yangdan, Lu., Chun, Cheng., Chunxiao, Wang., Guanbing, Mo. (2022). Financial Distress Prediction for Digital Economy Firms: Based on PCA-Logistic. Journal of Risk Analysis and Crisis Response, 12(1) doi: 10.54560/jracr.v12i1.319
- Dr., Jyoti, Nair., Dr., JK, Sachdeva. (2022). Predictive Modelling for Financial Distress amongst Manufacturing Companies in India. Journal of Global Economy, 18(4):261-276. doi: 10.1956/jge.v18i4.665
- Erfina, Dukalang., Irfan, Zamzam., Zulkifli, Abu. (2024). Analysis of Financial Distress Predictions Using Altman, Zavgren, Fulmer, Ohlson, Taffler, and Ca-Score Models as Early Warning Systems in Manufacturing Companies. Nominal Barometer Riset Akuntansi dan Manajemen Indonesia, 13(1):81-97. doi: 10.21831/nominal.v13i1.65081
- Feng, Shen., Yongyong, Liu., Run, Wang., Wei, Zhou. (2020). A dynamic financial distress forecast model with multiple forecast results under unbalanced data environment. Knowledge Based Systems, 192:105365-. doi: 10.1016/J.KNOSYS.2019.105365
- Frank, Ranganai, Matenda., Mabutho, Sibanda., Eriyoti, Chikodza., Victor, Gumbo. (2020). Corporate default risk modeling under distressed economic and financial conditions in a developing economy. Journal of Credit Risk, doi: 10.21314/JCR.2020.267
- I., Litvin., M, M, Fesenko., Olena, Hurman., Halina, Nahorniak., Оksana, М., Kuzmenko. (2022). Forecast-planning system of financial support for the development of industrial enterprises. Revista Amazonia Investiga, 11(53):132-145. doi: 10.34069/ai/2022.53.05.13.
- Khyrina, Airin, Fariza, Abu, Samah., Nurul, Azifah, Mohd, Khalid., Jamaluddin, Jasmis., Noor, Afni, Deraman., Lala, Septem, Riza., Zainab, Othman. (2024). Autoregressive Integrated Moving Average (ARIMA) Algorithm Adaptation for Business Financial Forecasting. Journal of Advanced Research in Applied Sciences and Engineering Technology, 38(1):37-47. doi: 10.37934/araset.38.1.3747
- Khyrina, Airin, Fariza, Abu, Samah., Nurul, Azifah, Mohd, Khalid., Jamaluddin, Jasmis., Noor, Afni, Deraman., Lala, Septem, Riza., Zainab, Othman. (2024). Autoregressive Integrated Moving Average (ARIMA) Algorithm Adaptation for Business Financial Forecasting. Journal of Advanced Research in Applied Sciences and Engineering Technology, 38(1):37-47. doi: 10.37934/araset.38.1.3747
- R., Chaves., André, Luis, Debiaso, Rossi., Luis, Echecopar, García. (2023). Financial Distress Prediction in an Imbalanced Data Stream Environment. Lecture Notes in Computer Science, 168-179. doi: 10.1007/978-3-031-40725-3_15
- Ran, H. (2023). MABAC method for multiple attribute group decision making under single-valued neutrosophic sets and applications to performance evaluation of sustainable microfinance groups lending. Plos one, 18(1), e0280239.
- Yi, Chen., Jifeng, Guo., Junqin, Huang., Bin, Lin. (2022). A novel method for financial distress prediction based on sparse neural networks with International Journal of Machine Learning and Cybernetics, 13(7):2089-2103. doi: 10.1007/s13042-022-01566-y
- Zhangong, Huang., Huwei, Li. (2024). ARIMA-SVR-based risk aggregation modeling in the financial behavior. Kybernetes, doi: 10.1108/k-01-2024-0249
- Zongguo, Ma., Xu, Wang., Yan, Hong, Hao. (2023). Development and application of a hybrid forecasting framework based on improved extreme learning machine for enterprise financing risk. Expert systems with applications, 215:119373-119373. doi: 10.1016/j.eswa.2022.119373.
References
Aidas, Malakauskas., Aušrinė, Lakštutienė. (2021). Financial distress prediction for small and medium enterprises using machine learning techniques. The Engineering Economics, 32(1):4-14. doi: 10.5755/J01.EE.32.1.27382.
Anzhela, Y., Petrova., Margarita, Deyneka. (2022). Arima-models: modeling and forecasting prices of stocks. Internauka, doi: 10.25313/2520-2294-2022-2-7921
Bokai, Zhang. (2019). Research on Fixed Assets Investment Forecast Based on ARIMA Model. doi: 10.1109/ICEMME49371.2019.00083
Bruce, Chapman., Ric, Simes. (2005). Profit Related Loans for Economically Disadvantaged Regions. Research Papers in Economics,
Dong, Mei, Li., Kaiyao, Xu., Yun, Daisy, Li., Yu, Jiang., Ming, Tang., Yangdan, Lu., Chun, Cheng., Chunxiao, Wang., Guanbing, Mo. (2022). Financial Distress Prediction for Digital Economy Firms: Based on PCA-Logistic. Journal of Risk Analysis and Crisis Response, 12(1) doi: 10.54560/jracr.v12i1.319
Dr., Jyoti, Nair., Dr., JK, Sachdeva. (2022). Predictive Modelling for Financial Distress amongst Manufacturing Companies in India. Journal of Global Economy, 18(4):261-276. doi: 10.1956/jge.v18i4.665
Erfina, Dukalang., Irfan, Zamzam., Zulkifli, Abu. (2024). Analysis of Financial Distress Predictions Using Altman, Zavgren, Fulmer, Ohlson, Taffler, and Ca-Score Models as Early Warning Systems in Manufacturing Companies. Nominal Barometer Riset Akuntansi dan Manajemen Indonesia, 13(1):81-97. doi: 10.21831/nominal.v13i1.65081
Feng, Shen., Yongyong, Liu., Run, Wang., Wei, Zhou. (2020). A dynamic financial distress forecast model with multiple forecast results under unbalanced data environment. Knowledge Based Systems, 192:105365-. doi: 10.1016/J.KNOSYS.2019.105365
Frank, Ranganai, Matenda., Mabutho, Sibanda., Eriyoti, Chikodza., Victor, Gumbo. (2020). Corporate default risk modeling under distressed economic and financial conditions in a developing economy. Journal of Credit Risk, doi: 10.21314/JCR.2020.267
I., Litvin., M, M, Fesenko., Olena, Hurman., Halina, Nahorniak., Оksana, М., Kuzmenko. (2022). Forecast-planning system of financial support for the development of industrial enterprises. Revista Amazonia Investiga, 11(53):132-145. doi: 10.34069/ai/2022.53.05.13.
Khyrina, Airin, Fariza, Abu, Samah., Nurul, Azifah, Mohd, Khalid., Jamaluddin, Jasmis., Noor, Afni, Deraman., Lala, Septem, Riza., Zainab, Othman. (2024). Autoregressive Integrated Moving Average (ARIMA) Algorithm Adaptation for Business Financial Forecasting. Journal of Advanced Research in Applied Sciences and Engineering Technology, 38(1):37-47. doi: 10.37934/araset.38.1.3747
Khyrina, Airin, Fariza, Abu, Samah., Nurul, Azifah, Mohd, Khalid., Jamaluddin, Jasmis., Noor, Afni, Deraman., Lala, Septem, Riza., Zainab, Othman. (2024). Autoregressive Integrated Moving Average (ARIMA) Algorithm Adaptation for Business Financial Forecasting. Journal of Advanced Research in Applied Sciences and Engineering Technology, 38(1):37-47. doi: 10.37934/araset.38.1.3747
R., Chaves., André, Luis, Debiaso, Rossi., Luis, Echecopar, García. (2023). Financial Distress Prediction in an Imbalanced Data Stream Environment. Lecture Notes in Computer Science, 168-179. doi: 10.1007/978-3-031-40725-3_15
Ran, H. (2023). MABAC method for multiple attribute group decision making under single-valued neutrosophic sets and applications to performance evaluation of sustainable microfinance groups lending. Plos one, 18(1), e0280239.
Yi, Chen., Jifeng, Guo., Junqin, Huang., Bin, Lin. (2022). A novel method for financial distress prediction based on sparse neural networks with International Journal of Machine Learning and Cybernetics, 13(7):2089-2103. doi: 10.1007/s13042-022-01566-y
Zhangong, Huang., Huwei, Li. (2024). ARIMA-SVR-based risk aggregation modeling in the financial behavior. Kybernetes, doi: 10.1108/k-01-2024-0249
Zongguo, Ma., Xu, Wang., Yan, Hong, Hao. (2023). Development and application of a hybrid forecasting framework based on improved extreme learning machine for enterprise financing risk. Expert systems with applications, 215:119373-119373. doi: 10.1016/j.eswa.2022.119373.