Portfolio optimization under mean-CVaR simulation with copulas on the Vietnamese stock exchange

The rise of robo-advisor wealth management services, which constitute a key element of fintech revolution, unveils the question whether they can dominate human-based advice, namely how to address the client’s behavioral biases in an automated way. One approach to it would be the application of machine learning tools during client profiling. However, trained neural network is often considered as a black box, which may raise concerns from the customers and regulators in terms of model validity, transparency, and related risks. In order to address these issues and shed more light on how neurons work, especially to figure out how they perform computation at intermediate layers, this paper visualizes and estimates the neurons’ sensitivity to different input parameters. Before it, the comprehensive review of the most popular optimization algorithms is presented and based on them respective data set is generated to train convolutional neural network. It was found that selected hidden units to some extent are not only specializing in the reaction to such features as, for example, risk, return or risk-aversion level but also they are learning more complex concepts like Sharpe ratio. These findings should help to understand robo-advisor mechanics deeper, which finally will provide more room to improve and significantly innovate the automated wealth management process and make it more transparent.

In the digital age and technological advancements, short video platforms have become essential tools for online sales and marketing. In addition, shopping through short video marketing has gained significant attention, especially among Generation Z, as it brings unique and novel shopping experiences. The primary goal of this study is to explore the factors of short video marketing that influence the purchase intentions of Generation Z consumers in Vietnam. To conduct this study, a quantitative approach was employed, utilizing a 5-point Likert scale questionnaire administered online through a non-probability sampling method. The sample comprised 350 respondents aged between 16 and 26 from Vietnam, representing Generation Z, who made purchases through short video marketing. The relationships among various variables were analyzed using Structural Equation Modeling (SEM). The study’s results demonstrated a positive, significant, and direct relationship between all factors of short video marketing, including interesting content, perceived usefulness, scenario-based experience, user interaction, perceived enjoyment, and involvement of celebrities and consumer brand attitude. Among these factors, perceived usefulness is the most influential factor on customer brand attitude. In addition, the study revealed that consumer brand attitude, acting as a mediating variable, had a positive and significant impact on consumers’ purchase intentions. Based on the findings, the study suggested strategies for businesses to enhance the quality and content on short video platforms, thereby improving the effectiveness of their marketing strategies.

Acknowledgment
The authors express a sincere gratitude to all the participants who generously took part in this research study.

Until recently, the statistical approach was the main technique in solving the prediction problem. In the framework of static models, the tasks of forecasting, the identification of hidden periodicity in data, analysis of dependencies, risk assessment in decision making, and others are solved. The general disadvantage of statistical models is the complexity of choosing the type of the model and selecting its parameters. Computing intelligence methods, among which artificial neural networks should be considered at first, can serve as alternative to statistical methods. The ability of the neural network to comprehensively process information follows from their ability to generalize and isolate hidden dependencies between input and output data. Significant advantage of neural networks is that they are capable of learning and generalizing the accumulated knowledge. The article proposes a method of neural networks training in solving the problem of prediction of the time series. Most of the predictive tasks of the time series are characterized by high levels of nonlinearity and non-stationary, noisiness, irregular trends, jumps, abnormal emissions. In these conditions, rigid statistical assumptions about the properties of the time series often limit the possibilities of classical forecasting methods. The alternative methods to statistical methods can be the methods of computational intelligence, which include artificial neural networks. The simulation results confirmed that the proposed method of training the neural network can significantly improve the prediction accuracy of the time series.