@article { , title = {An assessment of the financial soundness of the Kazakh banks.}, abstract = {The contribution of the banking industry to the financial crisis of 2007/8 has raised public concerns about the financial soundness of banks around the world, with many countries still suffering the backlogs of this crisis. The continuous emergence of such crises at both national and international levels increases governments', bank regulators' and financial market participants' need for reliable tools to assess the financial soundness of banks. In this context, this study investigates the financial soundness of the Kazakh banking sector, which is ranked by the World Bank as the first in the world in terms of the percentage of non-performing loans to total gross loans in 2012. Using data about all Kazakh banks over the period January 01, 2008 to January 01, 2014, this study identifies a number of accounting indicators that influence the financial soundness of banks using principal component analysis (PCA). It then uses the outcomes of the PCA in a cluster analysis, and groups the Kazakh banks into sound, risky and unsound banks at two points in time: January 01, 2008 and January 01, 2014. This methodology was further tested against a ranking system of banks and was proven to be more reliable in detecting risky banks. Fifteen financial ratios were initially selected as accounting indicators for the assessment of bank financial soundness. Using PCA, twelve indicators were isolated, which explain five principal components of capital adequacy: return on assets, profitability, asset quality, liquidity and leverage. Using the "k-means" method, the results suggest a structure of the Kazakh banking sector on January 01, 2008 that includes two groups of banks: sound and risky banks. On January 01, 2014, this structure of the banking system has changed to include three groups of banks: sound, risky and unsound banks. Thus in 2014 a new group of banks has emerged, i.e. financially unsound banks. The proposed cluster-based methodology has proven to be a reliable tool to detect the financial soundness of Kazakh banks, which makes it suitable for bank monitoring and supervision purposes. This study is the first to employ a cluster-based methodology to assess the financial soundness of a banking sector. This methodology can be used at a micro-level to determine the structure of a banking sector. Also, it can be used to monitor any changes in the structure of a banking sector and provide early warning signals about the financial health of banks.}, doi = {10.1108/AJAR-03-2019-0022}, eissn = {2443-4175}, issn = {2443-4175}, issue = {1}, journal = {Asian journal of accounting research}, note = {INFO COMPLETE (Now published, checked and updated 30/3/2021 LM; Still EarlyCite 27/10/2020 LM -- rec'd from contact ; not yet on publisher website 23.07.2020 GB) PERMISSION GRANTED (Gold OA journal according to publisher website, article to be published under CC BY. Prior to availability of the published version, permission granted for the following: version = AAM ; embargo = 0 months ; licence = BY-NC ; https://www.emeraldgrouppublishing.com/services/authors/author-policies/author-rights ; 23.07.2020 GB) DOCUMENT READY (VOR downloaded 28/9/2020 -- AAM rec'd and used in first instance - to be replaced with VOR when published 23.07.2020 GB) ADDITIONAL INFO: Omaima Hassan, Xin Zhang The e-mail received by the authors makes no mention of Gold OA, referring only to the archiving of the accepted version. However, the journal website clearly states that the journal is fully Gold OA. We should double-check whether the article actually has been published under a CC BY licence before updating the record with the VOR. If the VOR is published under a CC BY licence, then we will also need to update the licence used for the tables and appendices (Output ID 906761).}, pages = {23-37}, publicationstatus = {Published}, publisher = {Emerald}, url = {https://rgu-repository.worktribe.com/output/951060}, volume = {6}, keyword = {Financial soundness, Banks, Banking, Emerging economies, Cluster analysis, Principal component analysis, Kazakhstan}, year = {2021}, author = {Salina, Aigul P. and Zhang, Xin and Hassan, Omaima A.G.} }