Skoči na glavni sadržaj

Izvorni znanstveni članak

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

Accounting and governance risk forecasting in the health care industry

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


Puni tekst: engleski pdf 557 Kb

str. 487-501

preuzimanja: 403

citiraj


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

Datum izdavanja:

20.12.2015.

Posjeta: 765 *