Does financing behavior of Tunisian firms follow the predictions of the market timing theory of capital structure?

In this paper, we show how capital structure decisions made by non-financial firms listed in the Tunis Stock Exchange are affected by the predictions of the so-called market timing theory. Using a set of some relevant variables which reflect the market-timing signals, the firm fundamentals, and the performance of local stock market, we mainly find that leverage ratio of Tunisian firms is short-term driven by their current market valuations. In the long run, the market timing effects are not present at all. Rather, Tunisian firms seem to behave according to the tradeoff theory of capital structure by attempting to adjust their leverage levels towards a target ratio.Market timing theory

The Impact of Inflation on Bank Financial Performance: Case of Tunisia

Inflation is when the price of the most goods and services continue rising upward. This situation may cause the standard of living cost falls because, we have to spend a lot of money to get the same amount of goods and services that we bought previous time. On the other hand bank financial performance is important and is measured by several indictors . In this study we use a methodology of panel data in the sample of 11 banks in Tunisia over the period of ( 2000—2018). We found that inflation has positive impact on ROA  ( return on assets) and negative impact on ROE ( return on equity ) . Keywords: Inflation, bank financial performance, panel data, ROA, ROE DOI: 10.7176/EJBM/13-18-11 Publication date:September 30th 202

Behavioral Finance and Financial Contagion: The Evidence of DCC-MGARCH Model From 63 Equity Markets

The paper aims to test the existence of financial contagion between foreign stock markets of several emerging and developed countries during the U.S subprime crisis. It empirically attests for contagion through a DCC MGARCH (1.1) and an adjusted correlation test over 63 emerging and developing stock markets during the period from 02/01/2003 to 31/12/2013. As a result of the model of DCC-MGARCH analysis, we find the evidence of contagion during U.S subprime crisis for most of the developed and emerging countries.  Another finding is the emerging markets seem to be the most influenced by the contagion effects during U.S. subprime crisis. Since financial contagion is important for monetary policy, risk measurement, asset pricing and portfolio allocation, the findings of paper may be the interest of policy makers, investors, and portfolio managers. Keywords: Dynamic conditional correlation, Financial crisis, Financial contagion, Interdependence JEL Classifications: E44, G0

Entrepreneurship and Sustainability: The Need for Innovative and Institutional Solutions

The role of innovation and institutional quality for achieving sustainability are important issues tackled by current sustainable development debates, particularly in developing countries. Using a modified environmental Kuznets curve model, the present study improves our understanding of the critical roles of innovation, institutional quality, and entrepreneurship in structural change toward a sustainable future for Africa. Our empirical results show that formal and informal entrepreneurship are conducive to reduced environmental quality and sustainability in 17 African countries however informal entrepreneurship contributes more than formal entrepreneurship to this environmental degradation. The relationship between entrepreneurship and sustainable development turns strongly positive in the presence of high levels of innovation and institutional quality. This study contributes to this emerging research strand by clarifying the conditions that allow African countries to move toward more sustainable economies. Our results highlight the important roles played by innovation and institutions for achieving sustainability in Africa

Steady vs. Dynamic Contributions of Different Doped Conducting Polymers in the Principal Components of an Electronic Nose's Response

Multivariate data analysis and machine-learning classification become popular tools to extract features without physical models for complex environments recognition. For electronic noses, time sampling over multiple sensors must be a fair compromise between a period sufficiently long to output a meaningful information pattern, and sufficiently short to minimize training time for practical applications. Particularly when reactivity's kinetics differ from thermodynamics' in sensitive materials, finding the best compromise to get the most from data is not obvious. Here, we investigate on the influence of data acquisition to improve or alter data clustering for molecular recognition on a conducting polymer electronic nose. We found out that waiting for the sensors to reach their steady state is not required for classification, and that reducing data acquisition down to the first dynamical information suffice to recognize molecular gases by principal component analysis with the same materials. Particularly for online inference, this study shows that a good sensing array is no array of good sensors, and that new figure-of-merits shall be defined for sensing hardware aiming machine-learning pattern-recognition rather than metrology

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Neural Circuitry Based on Single Electron Transistors and Single Electron Memories

In this paper, we propose and explain a neural circuitry based on single electron transistors ‘SET’ which can be used in classification and recognition. We implement, after that, a Winner-Take-All ‘WTA’ neural network with lateral inhibition architecture. The original idea of this work is reflected, first, in the proposed new single electron memory ‘SEM’ design by hybridising two promising Single Electron Memory ‘SEM’ and the MTJ/Ring memory and second, in modeling and simulation results of neural memory based on SET. We prove the charge storage in quantum dot in two types of memories