This paper aims to assess the maturity level of competitive intelligence (CI) in Moroccan companies, so as to improve theirs practices, and to justify their investment in competitive intelligence. To do so, we have identified the maturity model based on a comprehensive review of recent literature. The objectives of this paper are threefold: (1) to determine the major purposes of a CI maturity model (CIMM), (2) to identify the types of CI dimensions and levels of maturity, (3) to evaluate Moroccan companies in terms of CI practice. Our approach is to develop a conceptual framework of the CI maturity model that articulates (1) dimensions of CI, and (2) maturity levels of CI. We note that little attention has been given in previous research to how CI is actually conducted in Moroccan companies. For this purpose, an empirical study was conducted. The results discuss various perspectives and insights from a competitive intelligence maturity model point of view in the Moroccan context. The results show that the majority of the Moroccan companies are in an early stage of the CI levels, where the CI practice is only to employ environment scanning and the competition in the business environment is not intense. We also note the absence of CI structure at this level. Most of these Moroccan companies are not able to cope with changes in the business environment. The CI systems and processes are released on an irregular basis. This study is the first to investigate the Competitive Intelligence Maturity Model (CIMM) in the Moroccan context. The findings of this research show that there are six CI dimensions (CI culture of an organization; CI deliverables; CI sourcing; CI cycle; CI investment in terms of resources; CI users and CI application) that should be taken into account in CI implementation with regard to the CI level (early, mid, world class).
Nowadays, modeling and forecasting the volatility of stock markets have become central to the practice of risk management; they have become one of the major topics in financial econometrics and they are principally and continuously used in the pricing of financial assets and the Value at Risk, as well as the pricing of options and derivatives. The aim of this article is to compare the GARCH (Generalised Auto Regressive Conditional Heteroskedasticity) family models —GARCH (1.1), GJR-GARCH, PGARCH, EGARCH, and IGARCH— with the EWMA (Exponentially Weighed Moving Average) model in the hope of finding the best model to forecast the volatility of the Moroccan stock-market index MADEX. We use daily returns covering the period between 01/04/1993 and 30/08/2016. We find that the asymmetric model IGARCH following a normal error distribution yields the best forecasting performance results and therefore, surpasses the EWMA model. Our results could have application in the risk management in Morocco, as well as leading to a better understanding of the Moroccan stock-exchange volatility dynamics, especially with the lack of previous similar studies.
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