Web 2.0, focusing on user involvement and cooperation, is vital for online marketing. To succeed in such a cutthroat industry, every online business must place a premium on earning and retaining clients' trust and loyalty in the digital realm. The study's objective is to ascertain whether or not electronic consumer loyalty and online trust are enhanced by social media marketing. The results of the poll, which included 596 individuals, showed that social media marketing tools had a significant effect on consumers' faith in and commitment to businesses through digital channels. The results of this study will hopefully add to what has been applied to social media marketing for online businesses.

Omnichannel is not just a marketing, e-commerce, or customer support buzzword. This future customer engagement platform helps businesses communicate with customers through centralized channels on a smart interface. It is difficult to achieve customer loyalty when the risk in online transactions, which creates anxiety, exists in all transaction processes in an omnichannel system. Hence, the purpose of this research was to analyze the influence of anxiety on relationships when clients purchase from an omnichannel platform using the stimulus–organism–response (SOR) paradigm. To fulfill study aims, qualitative and quantitative research approaches were used. In-depth interviews and focus group discussions were used to acquire qualitative data, while survey responses from 485 participants were used to collect quantitative data. This study’s results revealed relationships between consumer psychology factors such as perceived mental benefits, hedonic value, and anxiety. Moreover, customer anxiety in omnichannel can be measured as a novel and exact concept in marketing science and have a moderating role in the effect of perceived mental benefits on electronic loyalty and perceived mental benefits on hedonic value in omnichannel systems. As a result, enterprises were also offered various managerial implications to develop their omnichannel system.

<p><span lang="EN-US">Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of powerful algorithms features in machine learning and artificial intelligence has opened a promising new direction. This study compares the predictive ability of three forecasting models, including <a name="_Hlk106797328"></a>support vector machine (SVM), artificial neural networks (ANN), and logistic regression. The data used is those of the stocks in the VN30 basket with a holding period of one day. With the rolling window method, this study got a highly predictive SVM with an average accuracy of 92.48%.</span></p>

This study aimed to forecast the exchange rate between the Vietnamese dong and the US dollar for the following month in the context of the COVID-19 pandemic. It used the Support Vector Regression (SVR) algorithm under the Uncovered Interest Rate Parity (UIRP) theoretical framework; the results are compared with the Ordinary Least Square (OLS) regression model and the Random Walk (RW) model under the rolling window method. The data included the VND/USD exchange rate, the bank interest rate for the 1-month term, and the 1-month T-bill from January 01, 2020, to September 11, 2021. The research discovered a linear link between the two nations' exchange rates and interest rate differentials. Interest rate differentials are input variables to forecast interest rate differentials. Furthermore, the connection between the exchange rate and interest rate differentials during this era does not support the UIRP hypothesis; hence, the error for OLS predictions remains large. The study provided a model to forecast future exchange rates by combining the UIRP theoretical framework and the SVR algorithm. The UIRP theoretical framework can anticipate exchange rate differentials using the input variable and the interest rates between two nations. Meanwhile, the SVR algorithm is a robust machine learning technique that enhances prediction accuracy. Doi: 10.28991/ESJ-2022-06-03-014 Full Text: PDF

Risk management and stock investment decision-making is an essential topic for investors and fund managers, especially in the context of the COVID-19 pandemic. The problem becomes easier if the market is efficient, where stock prices fully reflect potential risk. Nevertheless, if the market is not efficient, investors may have an opportunity to find an effective investment method. Vietnam is one of the emerging markets; the efficiency is still weak. Thus, there will be an opportunity for astute investors. This study aims to test the weak-form efficient market and provide a modern approach to investors’ decision-making. To achieve that aim, this study uses historical data of stocks in the VN-Index and VN30 portfolio to buy and sell within a one-day period under the rolling window approach to test the Ho Chi Minh City Stock Exchange (HoSE) through a runs test and to perform stock trading using the support vector machine (SVM) and logistic regression. The buying/selling of stocks is guided by the forecasted outcomes (increase/decrease) of logistic regression and SVM. This study adjusted the return rate in proportion to the risks and compared it with index investments of VN-Index and VN30 to evaluate investment efficiency. The test results dismissed the weak-form efficient-market hypothesis, which opens up many opportunities for short-term traders. This study’s primary contribution is to provide a stock trading strategy for short-term investors to maximize trading profits. Because logistic regression and SVM have proven effective trading methods, investors can use them to achieve abnormal returns.