Longevity risk management through Machine Learning: state of the art

Profit is the main purpose and the key result of any enterprise operation. Profit management involves two stages, namely, generation and distribution. Profit generation is the main stage in the overall process of profit management and not only the basis for its distribution, but also a source of the production assets renewal and the new self-financing of the further development of the enterprise. Based on the analysis of approaches to the essence and classification of company profits, an advanced complex classification is proposed. In order to improve the profit generation management of a motor transport enterprise and to identify the main factors affecting its profits, an analysis of technical and economic indicators, analysis of profitability indicators and SWOT analysis were carried out. Given the results obtained, a questionnaire was created and a survey was carried out among managers and specialists of the enterprise, which allowed to identify the factors that have the greatest impact on the activity of the motor transport enterprise. The factors are classified into external and internal. Also, a factor analysis of the company’s profit was carried out, which made it possible to determine key indicators that require priority attention in managing the profit generation at a motor transport enterprise. It was concluded that the most important external factors include the Ministry of Infrastructure’s activities aiming at adapting the world tendencies to the Ukrainian automotive industry. The internal factors in controlling the profit generation of the enterprises are in paying greatest attention to the volume of services rendered, their cost and the size of transportation tariffs.

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.

The regression tree approach is an effective and easy to interpret technique where it utilizes a recursive binary partitioning algorithm that divides the sample into partitioning variables with the strongest correlation to the response variable. Earnings per share can be considered as one of the main factors in making the investment decision. This study aims to build a predictive model for earnings per share in the context of the Middle East and North African countries (MENA) . The sample of the study consists of sixty-three banks, which were chosen from eight countries, with a total of six-hundred thirty observations. The simple regression, regression tree, and its pruned regression tree, conditional inference tree, and cubist regression are used to build the predictive model for earnings per share that depends on total assets, total liability, bank book value, stock volatility, age of the bank, and net cash. The results show that the cubist regression is outperforming other approaches where it improves root mean square error for the predictive model by approximately double in comparison with other methods. More interesting results are obtained from the important scores, where it shows that the total assets of the bank, bank book value, and total liability have the biggest impact on the prediction of earnings per share. Also, the cubist regression gives an improvement in R-squared over other methods by at least 30% and 23% using training and testing data, respectively.