Neural network methods for forecasting the reliability of Ukrainian banks

The rise of robo-advisor wealth management services, which constitute a key element of fintech revolution, unveils the question whether they can dominate human-based advice, namely how to address the client’s behavioral biases in an automated way. One approach to it would be the application of machine learning tools during client profiling. However, trained neural network is often considered as a black box, which may raise concerns from the customers and regulators in terms of model validity, transparency, and related risks. In order to address these issues and shed more light on how neurons work, especially to figure out how they perform computation at intermediate layers, this paper visualizes and estimates the neurons’ sensitivity to different input parameters. Before it, the comprehensive review of the most popular optimization algorithms is presented and based on them respective data set is generated to train convolutional neural network. It was found that selected hidden units to some extent are not only specializing in the reaction to such features as, for example, risk, return or risk-aversion level but also they are learning more complex concepts like Sharpe ratio. These findings should help to understand robo-advisor mechanics deeper, which finally will provide more room to improve and significantly innovate the automated wealth management process and make it more transparent.

This study applied Altman Z-score model to assess the bankruptcy risk of a set of multidisciplinary enterprises of various types, mainly small and medium enterprises, with data taken from official financial reports of 180 enterprises in Soc Trang province. The binary logistic regression was employed to assess the impact of non-financial and financial factors on the bankruptcy risk of enterprises. The research findings showed that both the non-financial factors such as business area, types and size of the business, the educational level of managers and executors and other characteristics, and the financial factors (indicators) such as earnings before tax, net profit/equity ratio, earnings before interest and tax/total assets ratio, equity/total debt ratio, affect the bankruptcy risk of enterprises. Predicting the bankruptcy risk and measuring its determinants play an important role not only as an effective managing tool of the business, but also as evidence for policymakers to support the sustainable development of business.

The study aims to investigate the impact of determinants of financial distress on financial sustainability of Ethiopian commercial banks. The balanced panel data of 12 commercial banks of Ethiopia have been taken for the study from 2011 to 2017. The research deploys Ordinary Least Square (OLS) Regression Model. The indicators of financial distress are bank’s specific internals and macro-economic factors. The proxies of financial sustainability are Return on Assets, Return on Equity, Financial Stability Index and Bank Soundness. The findings reveal that the Absolute Liquidity Risk and Net Income Growth are found to be positive and significant and Solvency Risk negative and significant in relation to Return on Assets. Asset Quality is found to be positive and significant and Solvency Risk negative and significant with respect to Return on Equity. The Asset Quality and Net Income Risk are positive and significant and Solvency Risk is negative and significant with relation to the Financial Stability Index. Absolute Liquidity Risk and Liquidity Risk are positive and significant and Credit Risk negative and significant with Bank Soundness. Free Cash Flow and Net Income Growth are essential for enhancing Return on Assets and Bank Soundness, and managing equity within the prudential norms could bring forth short-term financial sustainability of commercial banks. By lowering provisioning of loan loss, Growth in Net Interest Income and managing Solvency Risk could ensure financial stability to the banks, which in turn leads to financial sustainability. The study reveals that financial sustainability of banks is insulated from the exposures of systematic risks originating from macroeconomic factors.