Version 1
: Received: 23 December 2023 / Approved: 25 December 2023 / Online: 25 December 2023 (10:25:37 CET)
How to cite:
Billios, D.; Seretidou, D.; Stavropoulos, A. Decoding Financial Futures: The Power of Numerical Indicators in Predicting Bankruptcy. Preprints2023, 2023121864. https://doi.org/10.20944/preprints202312.1864.v1
Billios, D.; Seretidou, D.; Stavropoulos, A. Decoding Financial Futures: The Power of Numerical Indicators in Predicting Bankruptcy. Preprints 2023, 2023121864. https://doi.org/10.20944/preprints202312.1864.v1
Billios, D.; Seretidou, D.; Stavropoulos, A. Decoding Financial Futures: The Power of Numerical Indicators in Predicting Bankruptcy. Preprints2023, 2023121864. https://doi.org/10.20944/preprints202312.1864.v1
APA Style
Billios, D., Seretidou, D., & Stavropoulos, A. (2023). Decoding Financial Futures: The Power of Numerical Indicators in Predicting Bankruptcy. Preprints. https://doi.org/10.20944/preprints202312.1864.v1
Chicago/Turabian Style
Billios, D., Dimitra Seretidou and Antonios Stavropoulos. 2023 "Decoding Financial Futures: The Power of Numerical Indicators in Predicting Bankruptcy" Preprints. https://doi.org/10.20944/preprints202312.1864.v1
Abstract
The review aims to examine how well numerical indicators of business bankruptcy can predict outcomes. The paper examines ten critical studies that concentrate on statistical models for bankruptcy predictions utilizing PRISMA criteria. The findings highlight the usefulness of numerical indicators in indicating financial hardship, particularly cash flow ratios. In order to connect theoretical knowledge with real-world corporate strategy applications, the study ends by reaffirming the importance of these indicators in strategic decision-making.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
