Loan Thi Vu

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 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.

Type of the article: Research Article

Abstract
The increasing behavioral volatility and informational complexity of emerging stock markets such as Vietnam create a critical need for more advanced analytical approaches to identify reliable momentum signals. This study aims to develop and validate an integrated momentum-based trading strategy specifically designed for the Vietnamese stock market. Using price and trading volume data for all stocks listed on the VNINDEX from January 2015 to February 2025, the methodology combines permutation-based entropy measures to capture short-term structural patterns with a formation–holding period framework to analyze medium- and long-term dynamics through Continuing Overreaction. The empirical results reveal a pronounced structural divergence in momentum behavior across investment horizons. Short-term momentum is persistent and strongly associated with low-complexity price and volume patterns, indicating coordinated behavioral trading and temporary predictability. In contrast, medium- and long-term Continuing Overreaction effects exhibit consistently negative values across various formation and holding horizons, suggesting that excess trading intensity leads to systematic mean reversion rather than sustained momentum. Backtesting over the period from January 2023 to February 2025 demonstrates that the proposed integrated strategy substantially outperforms a passive VNINDEX buy-and-hold benchmark, achieving a Sharpe ratio of 3.96 compared to 0.64 for the market. The superior performance remains robust across alternative portfolio construction settings and reflects improved downside risk control rather than increased return volatility. These findings indicate that integrating entropy-based complexity measures with volume-driven behavioral indicators provides a more effective framework for enhancing risk-adjusted returns in emerging stock markets.