Social commerce (s-commerce) is a rapidly developing form of e-commerce powered by social media influencers (SMIs). It can create valuable opportunities for retailers. In light of this growing trend, this study explores the influence of consumers’ engagement experiences (social support and presence) on community identification and consumers’ attachment to SMIs, along with their impact on consumers’ stickiness in the s-commerce context. We explore this through social presence and social support theory. The survey data from 411 s-commerce users via an online questionnaire were analyzed empirically with the PLS-SEM approach. The results indicated that presence and social support have significantly positive impacts on consumers’ attachment to SMIs and community identification, respectively. This increases users’ stickiness in s-commerce. This study enriches our understanding of user stickiness in s-commerce and can assist online vendors in developing marketing strategies and cultivating sustained relationships with their users.
This research investigates the role of trading volume and data frequency in volatility forecasting by evaluating the performance of Generalized Autoregressive Conditional Heteroskedasticity Mixed-Data Sampling (GARCH-MIDAS), traditional GARCH, and intraday GARCH models. We take trading volume as the proxy for information flow and examine whether the Sequential Information Arrival Hypothesis (SIAH) is supported in the China stock market. The contributions of this study are as follows. (1) We provide a more consistent comparison to evaluate the forecasting ability of the MIDAS approach. (2) We extend the literature on the forecasting performance of trading volume to the GARCH-MIDAS approach. (3) We present clear evidence to support that forecasting ability strongly relies upon data frequency. The empirical results show that: (1) GARCH-MIDAS is not able to beat the traditional GARCH method when both are estimated by the same predictor sampled at different frequencies; (2) there is a positive relation between trading volume and volatility, but no clear evidence appears that SIAH holds in the China stock market; and(3) high-frequency data are highly recommended for daily realized volatility (RV) forecasting, whereas intraday GARCH could significantly outperform traditional GARCH and GARCH-MIDAS in volatility forecasting.
The existing contradictory findings on the contribution of trading volume to volatility forecasting prompt us to seek new solutions to test the sequential information arrival hypothesis (SIAH). Departing from other empirical analyses that mainly focus on sophisticated testing methods, this research offers new insights into the volume‐volatility nexus by decomposing and reconstructing the trading activity into short‐run components that typically represent irregular information flow and long‐run components that denote extreme information flow in the stock market. We are the first to attempt at incorporating an improved empirical mode decomposition (EMD) method to investigate the volatility forecasting ability of trading volume along with the Heterogeneous Autoregressive (HAR) model. Previous trading volume is used to obtain the decompositions to forecast the future volatility to ensure an ex ante forecast, and both the decomposition and forecasting processes are carried out by the rolling window scheme. Rather than trading volume by itself, the results show that the reconstructed components are also able to significantly improve out‐of‐sample realized volatility (RV) forecasts. This finding is robust both in one‐step ahead and multiple‐step ahead forecasting horizons under different estimation windows. We thus fill the gap in studies by (1) extending the literature on the volume‐volatility linkage to EMD‐HAR analysis and (2) providing a clear view on how trading volume helps improve RV forecasting accuracy.
This research investigates the role of high-frequency data in volatility forecasting of the China stock market by particularly feeding different frequency return series directly into a large number of GARCH versions. The contributions of this research are as follows. 1) We provide clear evidence to support that the superiority of traditional time series models in volatility forecasting remains by taking advantage of high-frequency data. 2) We incorporate different distribution assumptions in GARCH models to capture the stylized facts of highfrequency data. The result shows that: 1) data frequency in GARCH application substantially influence the accuracy of volatility forecasting, as the higher the frequency is of the return series, the better are the forecasts provided; 2) non-normal distributions such as skewed student-t and generalized error distribution are more capable at reproducing the stylized facts of both intraday and daily return series than normal distribution; and 3) GARCH estimated by 5-min returns not only outperforms other GARCH alternatives, but also considerably beats RV-based models such as HAR and ARFIMA at volatility forecasting.
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