APPLYING CHAID TO IDENTIFY THE ACCOUNTING-FINANCIAL CHARACTERISTICS OF THE MOST PROFITABLE REAL ESTATE COMPANIES IN SPAIN

The aim of this study is the determination, from an empirical perspective, of the accounting and financial features which could condition financial profitability of real estate companies, to identify the performances that guarantee its permanency in the current marketplace, characterized by the world economic crisis, specially in Spain, whose housing sector represents an important contributor to the economic growth. Although at a theoretical level the DuPont Model establishes the relationships between a group of accounting ratios and financial profitability. This paper uses a sample of 5,484 Spanish real estate companies to quantify these relationships and to extract the most relevant ones and to obtain the patterns of the most profitable companies. We use ROE to measure profitability and we analyze various independent variables about solvency, liquidity, activity, turnover, financial equilibrium and investment structure. The main contribution is of methodological nature, as we have applied statistics tools that do not require initial hypotheses on the distribution of the variables, by using a data mining technique of classification and regression tree based on rule induction algorithms known as CHAID. The study provides quantitatively success profiles by means of a set of rules describing the patterns of the most profitable companies.CHAID; financial profitability; classification trees; accounting ratios; Spain.

Fonts alternatives de proteïna en aqüicultura

Es recullen tot un seguit de primeres matèries, com a font proteica alternativa, per a la fabricació de pinsos per a aqüicultura, amb l'objectiu de substituir totalment o parcialment les farines de peix. Les fonts proteiques alternatives poden provenir de proteïna animal o vegetal, d'organismes unicel•lulars, i de nitrogen no proteic. Dos motius són els que ens porten a intentar aquesta substitució: d'una banda, un abaratiment del cost dels pinsos, i, de l'altra, limitar la dependència que tenim respecte al forniment de les farines de peix.Alternatives protein sources for the intensive rearing of fish, are listed. This alternative sources come from animal proteins, plant proteins, single cell proteins and non-protein nitrogen. Two reasons are for the research of alternative protein sources for the fishmeal replacement, first the less cost of the feed and second restrict the reliance on fishmeal supply.Se citan una serie de fuentes proteicas alternativas, para la fabricación de piensos para la acuicultura, con el objetivo de sustituir total o parcialmente las harinas de pescado. Las fuentes proteicas alternativas pueden ser de proteina animal o vegetal, de organismos unicelulares, o de nitrógeno no proteico. Dos motivos son los que justifican este intento de sustitución: por un lado, el menor coste de los piensos, y por el otro, limitar la dependencia que tenemos del suministro de las harinas de pescado

La valoración de activos mediante opciones de intercambio: aplicación a las finanzas empresariales

Este trabajo tiene por objeto analizar la ecuación diferencial de Black and Scholes y evaluar su aplicación en la valoración de opciones de intercambio de activos reales. Los modelos de valoración de opciones financieras, que han sido objeto de un gran desarrollo formal en los últimos años, tienen un campo de aplicación de gran interés en la valoración de activos, tanto reales (fusiones y adquisiciones de empresas, opciones de abandono y crecimiento de las inversiones, etc.) como financieros (deuda empresarial, derechos de suscripción, obligaciones convertibles, etc.) que adquieren una nueva perspectiva desde esta teoría de derechos contingentes, que complementa y, en ocasiones, sustituye con ventaja a las valoraciones tradicionales basadas en los descuentos de flujos de caja ajustados al riesgo

A credit scoring model for institutions of microfinance under the Basil II normative

El crecimiento de los microcréditos a nivel mundial, junto con la normativa internacional sobre requerimientos de capital (Basilea II), están impulsando a las instituciones de microfinanzas (IMFs) a una mayor competencia con las entidades bancarias por este segmento de negocio. La banca tradicionalmente ha contado con adecuados modelos de credit scoring para analizar el riesgo de incumplimiento, pero esto no ha sido así en las IMFs supervisadas. El objetivo de esta investigación es diseñar un modelo de credit scoring para una institución sometida a supervisión y especializada en microcréditos, como es la Entidad de Desarrollo de la Pequeña y Micro Empresa (Edpyme) del sistema financiero del Perú. El resultado de la investigación muestra la metodología y fases necesarias para diseñar el modelo, así como el proceso de valoración y validación para que pueda ser aplicado en el área de negocio, especialmente para establecer la política de tasas de interés con clientes. Por último, también se muestra cómo puede utilizarse el modelo para desarrollar una gestión del riesgo de crédito en el marco de los métodos IRB de Basilea II.------------------------------------------------------------The growth of microcredit worldwide along with international rules on capital requirements (Basel II) are increasing the competition between microfinance institutions (MFIs) and banks for this business segment. The bank system traditionally has relied on adequate credit scoring models to analyze the risk of payment failures, but this has not been the case in supervised MFIs. The objective of this research is to design a credit scoring model for any institution subjected to supervision and specialized in microcredit as the Development Agency for Small and Micro Enterprise (Entidad de Desarrollo de la Pequeña y Micro Empresa - Edpyme) of the financial system in Peru. The results of this research includes a methodology and the steps needed to design the model, and the assessment and validation process that can be applied in the business area, in particular, to establish an interest rate policy with customers. Eventually, the paper also explains how the model can be used to develop credit risk management under the Basel II IRB approaches.Publicad

A CREDIT SCORING MODEL FOR INSTITUTIONS OF MICROFINANCE UNDER THE BASEL II NORMATIVE

The growth of microcredit worldwide along with international rules on capital requirements (Basel II) are increasing the competition between microfinance institutions (MFIs) and banks for this business segment. The bank system traditionally has relied on adequate credit scoring models to analyze the risk of payment failures, but this has not been the case in supervised MFIs. The objective of this research is to design a credit scoring model for any institution subjected to supervision and specialized in microcredit as the Development Agency for Small and Micro Enterprise (Entidad de Desarrollo de la Pequeña y Micro Empresa - Edpyme) of the financial system in Peru. The results of this research includes a methodology and the steps needed to design the model, and the assessment and validation process that can be applied in the business area, in particular, to establish an interest rate policy with customers. Eventually, the paper also explains how the model can be used to develop credit risk management under the Basel II IRB approaches.Microcredit; institutions of microfinance; Basel II; credit scoring; Logit; IRB

Analyzing political and systemic determinants of financial risk in local governments

Studies have shown that political variables can infl uence the volume of government debt and have recommended investigating the joint effects of diverse factors on the risk of local government default. Considering the relation between economic management and political constraints, this paper examines the joint infl uence of political and systemic factors on the risk of loan default by Spanish local governments. To do so, we analyze 148 city councils for the period 2006-2011, using a logit model with panel data and an artifi cial neural network. The empirical results indicate that the fi nancial risk of local governments is affected both by political factors specifi c to each case and, simultaneously, by systemic variables for the country. Specifi cally, political variables such as the mayor not having economics-related university studies, the under-representation of female councilors in the municipal corporation, municipal government by a party with a progressive ideology, and ideological alignment between the municipal and the regional government are all associated with greater fi nancial risk. Moreover, rising national unemployment, an increased sovereign risk premium, the impact of the electoral cycle, and that of declining economic growth are all factors that may increase the risk of default. The fi ndings presented are of great potential interest for governments, managers, national and international fi scal authorities, fi nancial regulators, and citizens at large, because an understanding of the signifi cance of these variables can help authorities make appropriate decisions to prevent and/or overcome problems related to municipal insolvency

Propuesta de un de mejoramiento de la calidad de atención al cliente en el servicio de telepago de BAC – Credomatic.

El estudio presenta un plan de mejoramiento dela calidad de la atención al cliente en el servicio de telepago de BAC – Credomatic, tiene como objetivo principal examinar la calidad de servicio percibida por los tarjeta habiente (Clientes)

Multiple cycles of dose-intensive chemotherapy with repeated stem cell support as induction treatment in metastatic breast cancer: a feasibility study

The purpose of this trial was to study feasibility and tolerance of a dose-intensive multicyclic alternating induction chemotherapy with repeated stem cell support in a series of 43 metastatic breast cancer patients. Anthracycline-naive patients (n = 21) received cyclophosphamide 2.5 g/m2 plus doxorubicin 80 mg/m2 alternating every 14 days with paclitaxel 200-350 mg/m2 plus cisplatin 120 mg/m2. Patients who had previously received anthracyclines (n = 22) received cisplatin 120 mg/m2 plus etoposide 600 mg/m2 alternating with paclitaxel 200-350 mg/m2 plus ifosfamide 8 g/m2. Peripheral blood stem cells were infused after every course except the first, with a median CD34+ dose of 2.1 ´ 106/kg per cycle. Positive selection of CD34+ cells was performed in good mobilizers. The median number of cycles administered was six (4-8), and the time interval between them was 17 days. Median summation dose intensities (SDI) actually administered for the CA-TP and PE-TI protocol were 4.95 and 4.69, respectively (87% of scheduled SDI). There were 15 complete (35%) and 21 partial responses (49%), for an overall response rate of 84% (95% CI, 73%-95%). Infection or neutropenic fever occurred in 50% of the cycles. There was one treatment-related death. After a median follow-up of 26 months, the median event-free-survival was 12 months (95% CI: 10-14) and overall survival was 31 months. These high dose-intensity induction treatments seem to be feasible with sequential stem cell support

A comparison of Covid-19 early detection between convolutional neural networks and radiologists

[EN] Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.Project Chest screening for patients with COVID 19 (COV2000750 Special COVID19 resolution) funded by Instituto de Salud Carlos III. Project DIRAC (INNVA1/2020/42) funded by the Agencia Valenciana de la Innovacion, Generalitat Valenciana.Albiol Colomer, A.; Albiol, F.; Paredes Palacios, R.; Plasencia-Martínez, JM.; Blanco Barrio, A.; García Santos, JM.; Tortajada, S.... (2022). A comparison of Covid-19 early detection between convolutional neural networks and radiologists. Insights into Imaging. 13(1):1-12. https://doi.org/10.1186/s13244-022-01250-311213

A comparison of Covid-19 early detection between convolutional neural networks and radiologists

Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.Project Chest screening for patients with COVID 19 (COV2000750 Special COVID19 resolution) funded by Instituto de Salud Carlos III. Project DIRAC (INNVA1/2020/42) funded by the Agencia Valenciana de la Innovación, Generalitat Valenciana.Peer reviewe