Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2015.1082434
Accounting and governance risk forecasting in the health care industry
Audrius Kabašinskas
orcid.org/0000-0001-6863-5895
Ingrida Vaičiulytė
Asta Vasiliauskaitė
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
Datum izdavanja:
20.12.2015.
Posjeta: 1.061 *