Vol 15, No 4 (2021)

The study examines the influence of corporate governance attributes on the corporate risk disclosure in the emerging countries. Board size, non-executive directors, independent directors, board diversity and CEO-duality are the important board of director’s composition that is considered as corporate governance variables for this study. The study focuses on South Africa and Nigeria as these countries are among major players in the African emerging market. The sample comprises 42 financial and non-financial firms listed in Nigerian Stock Exchange and Johannesburg Stock Exchange. The data was drawn from 192 annual reports for the year 2014–2018. The analytical tools employed are manual content analysis and regression. The empirical results show that operational risk disclosure outweighs environmental and strategic risk disclosure. Meanwhile, past information, non-monetary and good news are considered less relevant, however dominate
future, monetary and bad news which are more valuable to diverse stakeholders. Moreover, in considering the important factors that impact on the risk confession, that board size, independent director and diversity have greater influence in driving the risk disclosure upward. Nevertheless, non-executive director and CEO-Duality are statistically insignificant in determining the movement of risk information to divulge. The persistence of contemporary corporate risk practice jampacked with irrelevant information might promote greater agency cost. The implication for the current practice might increase investors’ uncertainty which in turn would raise the company cost of capital. This issue could be addressed by regulating risk disclosure in emerging countries instead of allowing corporate managers to report risk related information at their discretion. Corporate manager are also encourage to appreciate all the potential risk disclosure drivers in the African emerging countries.

The main purpose of this article is to identify the best neural network model algorithm and relevant set of variables for predicting financial distress/bankruptcy in innovative companies. While previous articles in this area considered neural network analysis for large companies from primary sectors of the economy, we take the novel approach of examining the less-explored area of innovative companies. First, we complete a comprehensive review of the relevant literature in order to define the best configuration of factors which can influence bankruptcy, network architecture and learning methodology. We apply our chosen method to a sample of companies from around the world, from industries which are considered innovative, and identify the dependence of bankruptcy probability on a set of factors which are reflected in the financial data of a company. Our evaluation is based on the financial data of 300 companies – 50 of them are bankrupts, and 250 are ‘healthy’. Our results represent the set of relevant factors for bankruptcy prediction and the appropriate neural network. We have applied a total of 19 factors characterising efficiency, liquidity, profitability, sustainability, and level of innovation. Our proposed analysis is appropriate for all sizes of companies. We provided two models in order to cater for the most confidence in terms of obtained results. The total predictive ability of the model developed in our research is almost 98%, which is extremely efficient, and corresponds to the results of the most modern methods. Both approaches demonstrated almost the same level of influence of factor groups on final bankruptcy probability.

Project financing is one of the priority tools for stimulating the country's economic growth around the world, which allows the implementation of large-scale and capital-intensive projects, providing favorable credit conditions with insufficient creditworthiness of the project beneficiaries [1].

As a rule, project financing instruments are long-term (10-30 years, depending on the type of transaction), so this asset class is interesting for the implementation of the task of building long-term models for assessing credit risk associated with the introduction in 2018 of the new international financial reporting standard IFRS 9 "Financial Instruments".

The new standard requires financial institutions to calculate their expected credit loss (ECL) at the time of granting loans and other banking products exposed to credit risk [2], taking into account different time horizons, which significantly changes the traditional approaches to assessing credit risk by commercial banks [3], [4].

As part of this work, a model was built to assess the long-term probability of default for the portfolio of assets of a Russian commercial bank belonging to the project finance segment in accordance with the requirements of the International Financial Reporting standard IFRS 9 "Financial Instruments". At present, the topic of this work is extremely relevant and may be of interest both for commercial banks that are faced with the problem of improving credit risk assessment models

There are innovation creation process, innovations’ classifications, and measures considered in the paper. Recently focus on the literature moving from innovations relationship with financials to the role of people. This review considers board of directors group characteristics and CEO individual characteristics (the part of which impacts only firms from innovative industries) significant for innovation creation. The paper predicts investment in innovation, innovation outcome, and optimal for shareholders’ wealth board of directors’ type in dependence on CEO individual characteristics.