Optimal investment portfolio selection from the largest Ukrainian companies: comparative study of conventional and responsible portfolios

This study examines the relationship between corporate social responsibility (CSR) and earnings management after controlling for endogeneity of CSR. Using a sample of non-financial firms listed on Korean Securities Market between 2002 and 2010, this study finds that ignoring endogeneity biases the estimated relation between CSR and earnings management. Specifically, the results show that the negative and significant relation between CSR commitment and discretionary accruals reported in the previous studies becomes insignificant. However, the negative and significant relation between CSR commitment and real activities manipulation remains significant even when the endogeneity of CSR commitment is taken into account. Therefore, this study provides evidence that proactive CSR engagement significantly affects firm’s practice of real activities manipulation, while it does not affect its practice of discretionary accruals. These results indicate that CSR commitment leads managers to be more responsible in management of operational activities than in accruals management.

Corporate social responsibility (CSR) reporting plays a key role in management control, particularly in light of the increased demand for non-financial reporting after the financial crisis of 2008–2009. This literature review evaluates 47 empirical studies that concentrate on the influence of several board composition variables on the quantity and quality of CSR reporting. The author briefly introduces the research framework that underpins current empirical studies in this field. This is followed by a discussion of the main variables of board composition: (1) committees (audit and CSR committees), (2) board independence, (3) board expertise, (4) CEO duality, (5) board diversity (gender and foreign diversity), (6) board activity, and (7) board size. The author, then, summarizes the key findings, discusses the limitations of the existing research and offers useful recommendations for researchers, firm practice and regulators.

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.