The search for performance is the main objective of a company and its measurement represents one of the only possible fields of evaluation, comparison and choice that can reduce both arbitrariness and ultimately inform on the relevance of organisations and their programmes. In the banking sector, it cannot be otherwise. Through our paper, and based on previous research, we will try to clarify the notion of corporate performance by focusing on financial performance and on one of the key indicators that allows us to judge it reliably in banks and to guarantee, consequently, their survival, their sustainability and the creation of value for their shareholders. This indicator is the ROE. Thus, we attempt to explain the factors and parameters that influence ROE in one way or another by means of an econometric analysis. This analysis relates variables that reflect asset management, costs and debt levels to financial profitability. Finally, we propose share price forecasts for the banks studied, taking into consideration the current situation in Morocco and the sector in question due to COVID-19.
Modern portfolio theory is closely linked to the concept of diversification. As a result, the most important decision of investor is to make his allocation asset portfolio more and more efficient. Thus, for a given level of risk, the investor seeks to maximise the expected return and minimise the risk by constructing an optimal portfolio. In this paper, we seek to know how unsupervised learning can be used to define the asset allocation strategy. In this sense, we have carried out a comparative study between a so-called classical portfolio, which is based on the modern portfolio theory (i.e. a portfolio constructed on the basis of numerical optimisation) and a portfolio based on unsupervised learning. The aim of this comparison is to look for the best performing method that can give the best asset allocation. Our findings show that the optimal strategy for an ambitious investor lies to the unsupervised learning algorithms that allow a dynamic analysis of portfolio. However, the optimal strategy for a risk-averse investor is still the numerical optimisation approach.
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