Uso de variables de mercado en la predicción de dificultades financieras para las empresas que cotizan en Vietnam

This paper aims to investigate the classification power of market variables as predictors in the financial distress prediction model for listed companies in a frontier market as Vietnam securities market. Data is collected from 70 financially distressed companies that suffer a loss in 3 consecutive years and 156 non-financially distressed companies in Vietnam from 2010 to 2017. Four different models have been constructed using Logit regression and SVM analysis technique to make a prediction in 1 to 3-year ahead. The analysis results show that combining accounting ratios with market variables such as price volatility and P/E can improve the classification ability of the ex-ante model. In addition, contrary to the results of related previous researches in emerging markets, in this study, Logit models outperform SVM models. Therefore, for future research, models that apply other machine learning classifiers such as Decision Tree (DT) or Neural Network (NN) should be investigated.Este artículo tiene como objetivo investigar el poder de clasificación de las variables del mercado como factores predictivos en el modelo de predicción de dificultades financieras para las empresas que cotizan en bolsa en un mercado fronterizo como el mercado de valores de Vietnam. Los datos se recopilan de 70 compañías con dificultades financieras que sufrieron una pérdida en 3 años consecutivos y 156 empresas sin dificultades financieras en Vietnam desde 2010 a 2017. Se han construido cuatro modelos diferentes utilizando regresión Logit y la técnica de análisis de SVM para hacer una predicción en 1 a 3 años por delante. Los resultados del análisis muestran que la combinación de ratios contables con variables de mercado como la volatilidad de los precios y el P / E puede mejorar la capacidad de clasificación del modelo ex ante. Además, a diferencia de los resultados de investigaciones anteriores relacionadas en mercados emergentes, en este estudio, los modelos Logit superan a los modelos SVM. Por lo tanto, para futuras investigaciones, se deben investigar los modelos que aplican otros clasificadores de aprendizaje automático, como el Árbol de decisiones (DT) o la Red neuronal (NN)

Using SiO2 nano-particles for better color uniformity and lumen output in 8500 K conformal and in-cup white LEDs

In the effort of improving the performance of white light LEDs devices (WLEDs), the SiO2 nano-particles were applied and have shown a significant impact on the optical properties. Specifically, the light output of the lighting devices is enhanced when a mixture of SiO2 particles and silicone gel is diffused on the encapsulation layer surface. This enhancement is the result of light scattering from SiO2 that strengthens the emitted blue light at further angles and reduces the color discrepancy. The evidence is that CCT deviation in SiO2-doped structure decline from 1000 K to 420 K in -70° to 70°. In addition, the SiO2 with refractive index in between the phosphor material and outside environment allows light to be emitted outward more effectively. This lighting enhancement of SiO2-doped structure increases the lumen output by 2.25% at 120 mA power source in comparison to structure without SiO2. These experimental outcomes suggest that SiO2 is an effective material to add in WLEDs structure for better lighting efficiency

Feature selection methods and sampling techniques to financial distress prediction for Vietnamese listed companies

The research is taken to integrate the effects of variable selection approaches, as well as sampling techniques, to the performance of a model to predict the financial distress for companies whose stocks are traded on securities exchanges of Vietnam. A firm is financially distressed when its stocks are delisted as requirement from Vietnam Stock Exchange because of making a loss in 3 consecutive years or having accumulated a loss greater than the company’s equity. There are 12 models, constructed differently in feature selection methods, sampling techniques, and classifiers. The feature selection methods are factor analysis and F-score selection, while 3 sets of data samples are chosen by choice-based method with different percentages of financially distressed firms. In terms of classifying technique, logistic regression together with SVM are used in these models. Data are collected from listed firms in Vietnam from 2009 to 2017 for 1, 2 and 3 years before the announcement of their delisting requirement. The experiment’s results highlight the outperformance of the SVM model with F-score selection method in a data sample containing the highest percentage of non-financially distressed firms

Measuring banking efficiency in Vietnam: parametric and non-parametric methods

The article aims to evaluate the business efficiency of commercial banks in Vietnam using both parametric and non-parametric approaches. In this study, the Stochastic Frontier Analysis (SFA), which belongs to a parametric method, and Data Envelopment Analysis (DEA), a non-parametric approach, are applied to a sample of 30 joint stock commercial banks in Vietnam in the period of 2011–2015. Applying Tobit regression model, the impact of bank size, bank age, and the ownership feature on the efficiency of bank service industry in Vietnam is also investigated. The analysis results show that in general, the Vietnamese banking efficiency is improving during the selected period regardless of techniques used. However, there is small level of similarity in efficiency rankings identified from the SFA and DEA models. In terms of efficiency determinants, the results show that all three variables of size, age, and state ownership have a positive impact on bank efficiency

THE PATENTED DRUGS UTILIZATION: A STUDY AT NGUYEN DINH CHIEU HOSPITAL IN BEN TRE PROVINCE FROM 2011 TO 2017

Objective: With their new and efficacious active ingredients, patented drugs have important roles in offering high-quality healthcare. However, huge cost-related barriers in accessing patented drugs along with the availability of low-cost bioequivalent generics have great impact on drugs policy in Vietnam. To understand situation of patented drugs utilization at hospitals for a certain period, this pilot study was conducted at Nguyen-Dinh-Chieu Hospital in Ben-Tre Province. Methods: The cross-sectional descriptive study was conducted on the retrospective data of all patented drugs used at Nguyen-Dinh-Chieu Hospital in Ben-Tre Province from 2011-2017. Characteristics of drugs utilization were described by frequency and percentage of drugs quantities and costs. Criteria for the description were as follows: active ingredient, route of administration, therapeutic class and manufacturing country. Data were extracted from the hospital information system and were processed by R software. Results: From 2011 to 2017, there were 212 patented drugs used which related to 145 active ingredients and 20 therapeutic classes. 88% were single active ingredient drugs and 49% were oral drugs. Antimicrobial and cardiovascular drugs represented the largest number of drugs and the highest cost. 79% of patented drugs were manufactured by companies in Europe and the majority came from France and Germany. Conclusion: This study provided initial information about the utilization of patented drugs during a long period of time at a Vietnamese hospital. The understanding gained will aid medical managers in assessment and adjustment of the drugs list, thus, optimizing the hospital budget and the equity in access to drugs within communities