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Etisalat is a global business which currently operates in 16 countries across the Middle East, Asia and Africa, and as one of the 25 leading global telecoms companies, the management team has identified Nigeria, the largest black Nation on earth as a strategic opportunity to increase both its brand perception and returns to its shareholders. This piece, written based on few frameworks and models to provide an analytical recommendation to the leadership of Etisalat on how to penetrate the market, and also sustain its presence despite the monopolistic competition. The report provides an insight into a business case establishing the growth of LTE subscriptions in the market and also provides a macroeconomic analysis of the business environment of the Nigerian market. The popular SWOT analysis framework guided the critics of the status and capability of Etisalat as a global business, providing insight to its competitive advantages and its area of development as a brand. One identified opportunity for Etisalat, among many others, is the strong governmental and public relations capability of Etisalat which is considered a strategic opportunity to grow its presence in the region. The report also analyzed the economy of the country from a macro and more objective perspective. Identifying key challenges and strategic case for why Etisalat would succeed in Nigeria despite the obvious challenges. Identifying FDI in the telecoms industry as a growing area, the country has been able to help service businesses attract more infrastructural investment which has also reduced the cost of operation at scale. Finally, the piece provides critical short-term and long-term recommendations with an implementation plan which further identifies areas of immediate opportunities

One of the biggest risks in the economies of countries is the bankruptcy of industries and companies. Predicting the bankruptcy of companies and industries provides the opportunity for the government, investors, and shareholders to take strategic actions to deal withcrises and prevent potential losses. From an economic perspective, financial distress can be interpreted as the company being loss-making, in which case the company has failed to succeed. In fact, in this case, the company's rate of return is less than the cost of capital. Another form of financial distress occurs when the company fails to comply with one or more of the debt covenant terms, such as maintaining the current ratio or the special value ratio to total assets according to the contract. Therefore, there is no general agreement on the definition of financial distress, and the reasons that lead companies towards failure are very different. One possible reason for failure is the wrong direction ofthe company, which can be analyzed through the financial ratios of the companies. Since the 1960s, many studies have only examined financial ratios as indicators of failure. Therefore, the analysis is the basis for improving and developing failureprediction models. In this study, the influential variables in predicting bankruptcy were first selected using backgrounds. The most important factors were identified with the opinions of experts, and the weight of each criterion was obtained using the AHP technique. Then, these criteria were ranked using the TOPSIStechnique to identify the most important factors. After completing these steps, by entering these variables into an artificial neural network of the multilayer perceptron type as input and using the backpropagation algorithm to train the network, the prediction accuracy increased, and accurate and reliable results were obtained

Macroeconomic factors significantly influence a country's economy and financial markets, thereby playing a pivotal role in the development of financial products. The PESTEL framework serves as a comprehensive analytical tool to examine the impact of political, economic, social, technological, environmental, and legal factors in identifying opportunities and threats for financial product development. This study utilizes the PESTEL model to analyze the impact of macroeconomic factors on the evolution of financial products and explores the associated opportunities and challenges. The research investigates macroeconomic factors such as interest rates, inflation, economic growth, political stability, societal changes, technological advancements, growing environmental consciousness, and regulatory frameworks. Data were collected from reliable secondary sources and analyzed using qualitative content analysis. Findings indicate that political stability and government support for financial innovation can foster the development of new products. Conversely, political instability and unexpected regulatory changes pose significant threatsto financial firms. Economic conditions like interestrates and economic growth exert a substantial influence on market demand and access to capital. The results suggest that lower interest rates and economic growth create favorable opportunities for financial product development.Furthermore, evolving consumer needs and preferences, coupled with technological advancements, particularly in fintech, are transforming financial processes and customer experiences, enhancing efficiency, and improving customer satisfaction. Additionally, the increasing focus on environmental issues has driven demand for green and sustainable financial products. This research recommends that financial managers and policymakers analyze and understand these factors tocapitalize on emerging opportunities and mitigate risks by adapting to environmental and technological changes. Future research can delve deeper into the intricate relationships between these factors through a more international perspective

Thispaper first reviewed the relevant literature and several R&D evaluation models in various industries. Then, a suitable model for the evaluation of R&D capabilities was selected through a New Product Development (NPD) approach within the Iranian automotiveindustry in 9 main dimensions and 29 indicators, validated based on the structural equations proposed in another paper. In the first stage, the coefficient matrix of criteria was identified according to the pairwise comparison matrix. Then, the VIKOR values S, R, and Q were calculated. The ranking involved smaller Q values. The criteria were ranked as follows: 1) research and design capabilities, 2) customer and market, 3) technological capabilities,4) financial capabilities resources, 5) organizationalstructure, 6) intellectual capital capabilities,7) NPDprocess, 8) management capabilities and, 9) strategic capabilities. Finally, there were suggestions about the capabilities falling under the lowest ranks

Cluster fluctuations and fractal structures are important features of space-time correlation in complex financial systems. However, the microscopic mechanism of creation and expansion of these two features in financial markets remains challenging. In the current research, by using factor-based model design and considering a new interactive mechanism called multi-level clustering, the formation process of cluster fluctuations was investigated with regard to the fractal structure of financial markets. For this purpose, the daily information of the final price of 150 shares that were accepted in the Tehran Stock Exchange, after the final screening, was entered in 5 sections with 30 shares in each section, in the desired model, and they were aggregated in three stock levels., sector and market were measured. Due to the fact that some investors have a longer investment horizon in the stock market and due to the limitation of the investigated time period, the maximum investment horizon of 1000 days has been determined in the model.In addition, the data studied in the research model are from August 2012 to September 2018. The findings of the research showed that the intensity of the tendency of collec-tive behavior at the sector level is much stronger than at the market level. In addition, based on the findings of the research, it was determined that the distribution of simulation eigenvalues in three levels is significantly similar to the distribution of real data. Also, according to the investor's time horizon, the studied series always has a long-term memory for fluctuations. In addi-tion, it was found that long-term memory is directly related to fractal dimen-sions. The findings of this research, in addition to providing a new insight into the space-time correlations of financial markets, show that multi-level conglomeration is one of the mechanisms for creating the microscopic mi-crostructure of such markets. In other words, multi-level collective behavior is an important factor in the occurrence of cluster and fractal fluctuations in the market, and therefore, it should be considered from this point of view in the interpretation of the concept of risk and the definition of risk manage-ment strategies.