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https://doi.org/10.1080/1331677X.2015.1082434

Accounting and governance risk forecasting in the health care industry

Audrius Kabašinskas   ORCID icon orcid.org/0000-0001-6863-5895
Ingrida Vaičiulytė
Asta Vasiliauskaitė

Puni tekst: engleski, pdf (557 KB) str. 487-501 preuzimanja: 143* citiraj
APA 6th Edition
Kabašinskas, A., Vaičiulytė, I. i Vasiliauskaitė, A. (2015). Accounting and governance risk forecasting in the health care industry. Economic research - Ekonomska istraživanja, 28 (1), 487-501. https://doi.org/10.1080/1331677X.2015.1082434
MLA 8th Edition
Kabašinskas, Audrius, et al. "Accounting and governance risk forecasting in the health care industry." Economic research - Ekonomska istraživanja, vol. 28, br. 1, 2015, str. 487-501. https://doi.org/10.1080/1331677X.2015.1082434. Citirano 17.01.2020.
Chicago 17th Edition
Kabašinskas, Audrius, Ingrida Vaičiulytė i Asta Vasiliauskaitė. "Accounting and governance risk forecasting in the health care industry." Economic research - Ekonomska istraživanja 28, br. 1 (2015): 487-501. https://doi.org/10.1080/1331677X.2015.1082434
Harvard
Kabašinskas, A., Vaičiulytė, I., i Vasiliauskaitė, A. (2015). 'Accounting and governance risk forecasting in the health care industry', Economic research - Ekonomska istraživanja, 28(1), str. 487-501. https://doi.org/10.1080/1331677X.2015.1082434
Vancouver
Kabašinskas A, Vaičiulytė I, Vasiliauskaitė A. Accounting and governance risk forecasting in the health care industry. Economic research - Ekonomska istraživanja [Internet]. 2015 [pristupljeno 17.01.2020.];28(1):487-501. https://doi.org/10.1080/1331677X.2015.1082434
IEEE
A. Kabašinskas, I. Vaičiulytė i A. Vasiliauskaitė, "Accounting and governance risk forecasting in the health care industry", Economic research - Ekonomska istraživanja, vol.28, br. 1, str. 487-501, 2015. [Online]. https://doi.org/10.1080/1331677X.2015.1082434

Sažetak
Previous authors have proved the advantage of commercial Accounting and Governance Risk (AGR) evaluation methods over academic methods. However, the information used in commercial methods is not readily available to an investor. Therefore, the most important features used in academic methods and the AGR was
forecast by Random Forests. It found a weak relation between the AGR rating and share price data (Close and Volume), using a skew t-distribution. For visualisation we used the Kohonen map, which identified three clusters. Clusters revealed AGR increasing, decreasing trendsetting and cluster-based companies which appear to have
no clear trend. A self-organised map (SOM) used the AGR history of alpha-stable distribution parameters, which were calculated from the stock data (Close and Volume). Also, the test sample (companies rating data), following from skew t-distribution, has been simulated by maximum likelihood method, and parameters of the skew t-distribution have been estimated.

Ključne riječi
Random Forests; stable distribution; skew t-distribution; prediction; AGR rating; data analysis; mathematical models

Hrčak ID: 171542

URI
https://hrcak.srce.hr/171542

Posjeta: 208 *