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The purpose of this paper is to examine the impact of changes in the exchange rate on long-term investment decisions of Indian manufacturing firms at the sector level.

The study is undertaken on a sample of 1,222 firms from six key manufacturing sectors of Indian economy during the period 2000-2016. The non-linear relationship between real exchange rate and long-term investment is studied using the two-step generalized model of moments estimator.

The study finds a concave (i.e. inverted U-shaped) relationship between the long-term investment and real exchange rate, particularly in case of chemical, construction, machinery and textile sector, in particular, and Indian manufacturing industry as a whole. It implies that investments in these sectors increase with depreciation of real exchange rate up to a point of inflection and subsequent to which it starts decreasing if exchange rate continues to depreciate further. But consumer goods and metal product sectors ensure a convex pattern, which demonstrates that investment is decreasing at the initial stage of depreciation of the exchange rate. The study moves one-step forward in validating this nexus between investment and exchange rate with respect to the price-cost margin and the extent of financial flexibility of firms. It is found that high price cost margin and financial flexibility moderates the adverse impact of exchange rate depreciation and immunizes the long-term investments in the scenario of a weak domestic currency and induce long-term investments.

The study measures the impact of exchange rate changes, but the impact of exchange rate volatility on investment has not been studied, which is absolutely different with different implications.

The study provides a clear guideline to firm managers for using the exchange rate movements in a favorable manner. The findings can be used to ensure sustainable long-term investments with respect to the core competence of firms in terms of price cost margin and financial flexibility at sector level of Indian manufacturing firms.

The study analyzes the non-linear relationship between exchange rate changes and long-term investment behavior of manufacturing firms from six key sectors of India. Further, the study moves one step forward to analyze this nexus under different scenarios of financial flexibility and price cost margin using dynamic panel models.

The purpose of this paper is to provide empirical evidence about the relationship between working capital financing (WCF) and firm profitability in six key manufacturing sectors of Indian Economy. It also aims to capture the change in the financing of working capital requirement over different scenarios of price-cost margin and financial flexibility.

The study is undertaken on a sample of 1,211 firms from 6 key manufacturing sectors of Indian economy from 2000 to 2016. The non-linear relationship between WCF and profitability is studied using two-step generalized model of moments (GMM) estimator.

The study finds a convex relationship between WCF and profitability among firms in chemical, construction, and consumer goods sectors. Firms in these sectors can finance larger portion of their working capital requirements through short-term debt without negatively impacting profitability. However, a concave pattern of relationship for firms in machinery, metal, and textile industries implies increasing debt financing of working capital requirement would increase profitability for the firms who have financed lower portion of their working capital by short-term bank borrowing. But when a higher proportion of working capital requirements are already financed by short-term debt, a further increase in debt financing may impact profitability negatively. Moreover, the study finds that firms with high financial flexibility and high price-cost margin (except textile) can increase profitability by financing larger portion of working capital requirement through short-term debts and the continuation with risky WCF could increase profitability.

The study contributes to the literature on working capital in a number of ways. First, no previous study has been undertaken to explore the non-linear relationship between WCF and corporate profitability over a large sample of firms from six key manufacturing sectors of Indian economy. Second, the study uses a quadratic function to explore the non-linear relationship between WCF and profitability. Third, the study explores the relationship between WCF and profitability with respect to the price-cost margin and financial flexibility of firms under different manufacturing sectors of Indian economy. Finally, the study uses advanced two-step GMM, the panel data techniques to handle unobservable heterogeneity and issues of endogeneity within the data sample.

The paper aims to investigate the dynamic relationship between working capital management and firm profitability for a sample of firms from eight European Union (EU) countries for the period 2006–2015.

The panel regression model is used in the study. Firm profitability is measured using the return on assets (ROA) ratio, whilst cash conversation cycle, financial leverage, size, tangibility and cash flow ratio are used as independent variables. The novelty of this study is the use of cash flow ratio to develop the analysis firms by dividing them as healthy and nonhealthy.

The paper reveals that working capital management affects firm profitability, and a positive relationship exists between them. The paper shows differences of working capital management and firm profitability across countries. The striking result of this study is that an inverted U-shape relationship exists between working capital management and firm profitability. Whereas the findings suggest that firms should be as close as possible to the optimal length of cash cycle to increase profitability, and managers should give a priority to working capital optimization.

The authors consider results of this study relevant to both researchers and business policymakers in the field of working capital management policies.

The rigid spherical particle models proposed in the literature for modeling fracture in rock have some difficulties in reproducing both the observed macroscopic hard rock triaxial failure enveloped and compressive to tensile strength ratio. The purpose of this paper is to obtain a better agreement with the experimental behavior by presenting a 3D generalized rigid particle contact model based on a multiple contact point formulation, which allows moment transmission and includes in a straightforward manner the effect of friction at the contact level.

The explicit formulation of a generalized contact model is initially presented, then the proposed model is validated against known triaxial and Brazilian tests of Lac du Bonnet granite rock. The influence of moment transmission at the contact level, the number of contacts per particle and the contact friction coefficient are assessed.

The proposed contact model model, GCM‐3D, gives an excellent agreement with the Lac du Bonet granite rock, strength envelope and compressive to tensile strength ratio. It is shown that it is important to have a contact model that: defines inter‐particle interactions using a Delaunay edge criteria; includes in its formulation a contact friction coefficient; and incorporates moment transmission at the contact level.

The explicit formulation of a new generalized 3D contact model, GCM‐3D, is proposed. The most important features of the model, moment transmission through multiple point contacts, contact friction term contribution for the shear strength and contact activation criteria that lead to a best agreement with hard rock experimental values are introduced and discussed in an integrated manner for the first time. An important contribution for rock fracture modeling, the formulation here presented can be readily incorporated into commercial and open source software rigid particle models.

Fibrous porous media have a wide variety of applications in insulation, filtration, acoustics, sensing, and actuation. To design such materials, computational modeling methods are needed to engineer the properties systematically. There is a lack of efficient approaches to build and modify those complex structures in computers. The paper aims to discuss these issues.

In this paper, the authors generalize a previously developed periodic surface (PS) model so that the detailed shapes of fibers in porous media can be modeled. Because of its periodic and implicit nature, the generalized PS model is able to efficiently construct the three-dimensional representative volume element (RVE) of randomly distributed fibers. A physics-based empirical force field method is also developed to model the fiber bending and deformation.

Integrated with computational fluid dynamics (CFD) analysis tools, the proposed approach enables simulation-based design of fibrous porous media.

In the future, the authors will investigate robust approaches to export meshes of PS models directly to CFD simulation tools and develop geometric modeling methods for composite materials that include both fibers and resin.

The proposed geometric modeling method with implicit surfaces to represent fibers is unique in its capability of modeling bent and deformed fibers in a RVE and supporting design parameter-based modification for global configuration change for the purpose of macroscopic transport property analysis.

To help investors find an investment policy with strong competitiveness, the purpose of this paper is to construct a multi-period investment decision model with practicality and superior performance.

The paper uses a suitable multi-period risk measure to construct a multi-period portfolio selection model, where target returns at intermediate periods and market frictions are taken into account simultaneously. An efficient scenario tree generation approach is proposed in order to transform the complex multi-period portfolio selection problem into a tractable one.

Numerical results show the new scenario tree generation algorithms are stable and can further reduce the tree size. With the scenario tree generated by the new scenario tree generation approach, the optimal investment strategy obtained under the multi-period investment decision model has more superior performance and robustness than the corresponding optimal investment strategy obtained under the single period investment model or the multi-period investment model only paying attention to the terminal cash flow.

The new risk measure and multi-period investment decision models can stimulate readers to find even better models and to efficiently solve realistic multi-period portfolio selection problems.

The empirical results show the superior performance and robustness of optimal investment strategy obtained with the new models. What's more important, the empirical analyses tell readers how different market frictions affect the performance of optimal portfolios, which can guide them to efficiently solve real multi-period investment decision problems in practice.

The paper first derives the concrete structure of the time consistent generalized convex multi-period risk measure, then constructs a multi-period portfolio selection model based on the new multi-period risk measure, and proposes a new extremum scenario tree generation algorithm. The authors construct a realistic multi-period investment decision model. Furthermore, using the proposed scenario tree generation algorithm, the authors transform the established stochastic investment decision model into a deterministic optimization problem, which can provide optimal investment decisions with robustness and superior performance.

The purpose of this paper is to present a nonlinear model along with stability analysis of a flexible supersonic flight vehicle system.

The mathematical state space nonlinear model of the system is derived using Lagrangian approach such that the applied force, moment, and generalized force are all assumed to be nonlinear functions of the system states. The condition under which the system would be unstable is derived and when the system is stable, the region of attraction of the system equilibrium state is determined using the Lyapunov theory and sum of squares optimization method. The method is applied to a slender flexible body vehicle, which is referenced by the other researchers in the literature.

It is demonstrated that neglecting the nonlinearity in external force, moment and generalized force, as it was assumed by other researchers, can cause significant variations in stability conditions. Moreover, when the system is stable, it is shown analytically here that a reduction in dynamic pressure can make a larger region of attraction, and thus instability will occur in a larger angle of attack, greater angular velocity and elastic displacement.

In order to carefully study the behavior of aeroelastic flight vehicle, a nonlinear model and analysis is definitely necessary. Moreover, for the design of the airframe and/or control purposes, it is essential to investigate region of attraction of equilibrium state of the stable flight vehicle.

Current stability analysis methods for nonlinear elastic flight vehicles are unable to determine the state space region where the system is stable. Nonlinear modeling affects the determination of the stability region and instability condition. This paper presents a new approach to stability analysis of the nonlinear flexible flight vehicle. By determining the region of attraction when the system is stable, it is demonstrated analytically, in this research, that decreasing the dynamic pressure can produce larger region of attraction.

The purpose of this paper is to present eight local elasto‐plastic beam element formulations incorporated into the corotational framework for two‐noded three‐dimensional beams. These formulations capture the warping torsional effects of open cross‐sections and are suitable for the analysis of the nonlinear buckling and post‐buckling of thin‐walled frames with generic cross‐sections. The paper highlights the similarities and discrepancies between the different local element formulations. The primary goal of this study is to compare all the local element formulations in terms of accuracy, efficiency and CPU‐running time.

The definition of the corotational framework for a two‐noded three‐dimensional beam element is presented, based upon the works of Battini .The definitions of the local element kinematics and displacements shape functions are developed based on both Timoshenko and Bernoulli assumptions, and considering low‐order as well as higher‐order terms in the second‐order approximation of the Green‐Lagrange strains. Element forces interpolations and generalized stress resultant vectors are then presented for both mixed‐based Timoshenko and Bernoulli formulations. Subsequently, the local internal force vector and tangent stiffness matrix are derived using the principle of virtual work for displacement‐based elements and the two‐field Hellinger‐Reissner assumed stress variational principle for mixed‐based formulations, respectively. A full comparison and assessment of the different local element models are performed by means of several numerical examples.

In this study, it is shown that the higher order elements are more accurate than the low‐order ones, and that the use of the higher order mixed‐based Bernoulli element seems to require the least number of FEs to accurately model the structural behavior, and therefore allows some reduction of the CPU time compared to the other converged solutions; where a larger number of elements are needed to efficiently discretize the structure.

The paper reports computation times for each model in order to assess their relative efficiency. The effect of the numbers of Gauss points along the element length and within the cross‐section are also investigated.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works on robust inference and finite sample theory were mostly motivated by his thesis advisor, Professor Anirudh Lal Nagar. They eventually led to his most original rethinking of many statistics and econometrics models that developed into the monograph Finite Sample Econometrics published in 2004. His desire to relax distributional and functional-form assumptions lead him in the direction of nonparametric estimation and he summarized his views in his most influential textbook Nonparametric Econometrics (with Adrian Pagan) published in 1999 that has influenced a whole generation of econometricians. His innovative contributions in the areas of seemingly unrelated regressions, parametric, semiparametric and nonparametric panel data models, and spatial models have also inspired a larger literature on nonparametric and semiparametric estimation and inference and spurred on research in robust estimation and inference in these and related areas.