Neural Networks and their application in the fields of corporate finance

This article deals with the usefulness of neuronal networks in the area of corporate finance. Firstly, we highlight the initial applications of neural networks. One can distinguish two main types: layer networks and self organizing maps. As Altman al. (1994) underlined, the use of layer networks has improved the reclassifying rate in models of bankruptcy forecasting. These first applications improved bankruptcy forecasting by showing a relationship between capital structure and corporate performance. The results highlighted in our second part, show the pertinence of the use of the algorithm of Kohonen applied to qualitative variables (KACM). More particularly, in line with Altman (1968, 1984), one can suggest the coexistence of negative and positive effects of financial structure on performance. This result allows us to question scoring models and to conclude as to a non-linear relationship. In a larger framework, the methodology of Kohonen has allowed a better perception of the factors able to explain the leasing financing (Cottrell et al., 1996). The objective is here to explain the factors of the choice between leasing and banking loans. By using different variables, we highlight the characteristics of firms which most often use leasing. The corporate financing policy could be explained by: the cost of the financing, advantages of leasing or by the minimization of agency costs in leasing, we highlight a relationship between resorting to leasing and credit rationing.neural netwoks, SOM, corporate finance

Determinants of the choice leasing vs Bank Loan: evidence from the french sme by Kacm

The question of leasing credit as a substitute or complement of a banking loan has still not been resolved in the financial literature. As a continuation of these arguments, the objective of this article is, on the one hand, to determine the characteristics of firms using leasing credit and on the other hand, to better understand the relationship between leasing and credit rationing. Firstly, our results suggest that SME use leasing all the more the leasing so when they are young, leveraged, less solvent and that they present an small size and an important failure probability. Thus, leasing pushes back the limits of banking debt for firms that have no access to it. Secondly, our results suggest a strong and significant relationship between credit rationing and the use of leasing. In this framework the latter appears to be a last resort financing.Leasing, credit rationing, SME, Self organising maps (SOM)

optimal pruned K-nearest neighbors: op-knn application to financial modeling

The paper proposes a methodology called OP-KNN, which builds a one hidden- layer feedforward neural network, using nearest neighbors neurons with extremely small com- putational time. The main strategy is to select the most relevant variables beforehand, then to build the model using KNN kernels. Multiresponse Sparse Regression (MRSR) is used as the second step in order to rank each kth nearest neighbor and finally as a third step Leave-One- Out estimation is used to select the number of neighbors and to estimate the generalization performances. This new methodology is tested on a toy example and is applied to financial modeling

optimal pruned K-nearest neighbors: op-knn application to financial modeling

The paper proposes a methodology called OP-KNN, which builds a one hidden- layer feedforward neural network, using nearest neighbors neurons with extremely small com- putational time. The main strategy is to select the most relevant variables beforehand, then to build the model using KNN kernels. Multiresponse Sparse Regression (MRSR) is used as the second step in order to rank each kth nearest neighbor and finally as a third step Leave-One- Out estimation is used to select the number of neighbors and to estimate the generalization performances. This new methodology is tested on a toy example and is applied to financial modelin

Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time

This study attempts to show how a Kohonen map can be used to improve the temporal stability of the accuracy of a financial failure model. Most models lose a significant part of their ability to generalize when data used for estimation and prediction purposes are collected over different time periods. As their lifespan is fairly short, it becomes a real problem if a model is still in use when re-estimation appears to be necessary. To overcome this drawback, we introduce a new way of using a Kohonen map as a prediction model. The results of our experiments show that the generalization error achieved with a map remains more stable over time than that achieved with conventional methods used to design failure models (discriminant analysis, logistic regression, Cox’s method, and neural networks). They also show that type-I error, the economically costliest error, is the greatest beneficiary of this gain in stability

Instructional Design Strategies for Teaching the Mental Status Examination and Psychiatric Interview: a Scoping Review

Objective: The psychiatric mental status examination is a fundamental aspect of the psychiatric clinical interview. However, despite its importance, little emphasis has been given to evidence-based instructional design. Therefore, this review summarizes the literature from an instructional design perspective with the aim of uncovering design strategies that have been used for teaching the psychiatric interview and mental status examination to health professionals. Methods: The authors conducted a scoping review. Multiple databases, reference lists, and the gray literature were searched for relevant publications across educational levels and professions. A cognitive task analysis and an instructional design framework was used to summarize and chart the findings. Results: A total of 61 articles from 17 countries in six disciplines and three educational levels were identified for data extraction and analysis. Most studies were from the USA, presented as educational case reports, and carried out in undergraduate education in the field of psychiatry. Few articles described the instructional rationale for their curriculum. None of the studies compared the effectiveness of different instructional design components. Reported learning activities for each task domain (knowledge, skills, and attitudes) and for each step of an instructional design process were charted. Most articles reported the use of introductory seminars or lectures in combination with digital learning material (videos and virtual patients in more recent publications) and role-play exercises. Conclusions: Educators in psychiatry should consider all task domains of the psychiatric interview and mental status examination. Currently, there is a lack of empirical research on expertise acquisition and use of instructional design frameworks in this context

Emittance-preserving acceleration of high-quality positron beams using warm plasma filaments

Preserving the quality of positron beams in plasma-based accelerators, where wakefields are generated in electron filaments, is challenging. These wakefields are characterized by transversely non-linear focusing fields and non-uniform accelerating fields. However, a nonzero plasma temperature linearizes the transverse wakefield within the central region of the electron filament. In this study, we employ 3D particle-in-cell simulations with mesh refinement to demonstrate that beams with emittances on the order of tens of nanometers are contained within the linearized region of the transverse wakefield. This enables emittance preservation to one percent, while positron beams with the same charge and micrometer emittances, which sample the non-linear part of the transverse wakefield, experience a relative emittance growth of ten percent. Additionally, we observe a significant reduction in the growth rate of the slice energy spread for the tens of nanometers emittance beams in comparison to the micrometer emittance beams. The utilization of warm plasmas in conjunction with low-emittance beams opens up new avenues for enhancing the beam quality across various plasma-based positron acceleration approaches.Comment: To be submitted as a proceedings for the 6th European Advanced Accelerator Concepts worksho

An Actor-Oriented and Architecture-Driven Approach for Spatially Explicit Agent-Based Modeling

Nowadays, there is an increasing need to rapidly build more realistic models to solve environmental problems in an interdisciplinary context. In particular, agent-based and spatial modeling have proven to be useful for understanding land use and land cover change processes. Both approaches include simulation platforms often used in several research domains to develop models explaining and analyzing complex phenomena. Domain experts generally use an ad hoc approach for model development, which relies on a code-and-fix life cycle, going from a prototype model through progressive refinement. This adaptive approach does not capture systematically actors’ knowledge and their interactions with the environment. The development and maintenance of resulting models become cumbersome and time-consuming. In this article, we propose an actor and architecture-driven approach that relies on relevant existing methods and satisfies the needs of spatially explicit agent-based modeling and implementation. We have designed an Agent Global Experiment framework incorporating a meta-model built from actor, agent architecture, and spatial concepts to produce an initial model from specifications provided by domain experts and system analysts. An engine is built as a tool to support model transformation. Domain knowledge including spatial specifications is summarized in a class diagram which is later transformed into the agent-based model. Finally, the XML file representing the model produced is used as input in the transformation process leading to code. This approach is illustrated on a hunting and population dynamic model to generate a running code for GAMA, an agent-based and spatially explicit simulation platform