Purpose Both investors and the stock markets are believed to behave in a perfectly rational manner, where investors focus on utility maximization and are not subjected to cognitive biases or any information processing errors. However, it has been discovered that the sentiment of the social mood has a significant impact on the stock market. This study aims to analyze how did the protest event of Tesla happened in April 2021 have a significant effect on the company's stock performance as well as its competitors, Nio, under the competitive effect. Design/methodology/approach The research is based on time series data collected from Tesla and Nio by employing 10 days, 15 days and 20 days anticipation and adjustment period for the event study. This study employed a text sentiment analysis to identify the polarity of the sentiment of the protest event using the Microsoft Azure machine learning tool which utilizes MPQA subjective lexicon. Findings The findings provide further evidence to show that a company-specific negative event has deteriorating effects on its stock performance, while having an opposite effect on its competitors. Research limitations/implications The paper argues that negative sentiments through social media word of mouth (SWOM) affect the stock market not just in the short run but potentially in the longer run. Such negative sentiments might create a snowball effect which causes the market to further scrutinize a company's operations and possibly lose confidence in the company. Originality/value This study explores how the Tesla's protest event at Shanghai Auto Show 2021 has a significant impact on Tesla's stock performance and prolonged negative impact although Tesla implemented immediate remedial actions. The remedial actions were not accepted positively and induced a wave of negative news which had a more persistent effect.

Purpose This paper examines whether guests contribute sWOM (social word of mouth) on different SNSs (social networking sites) regarding various personal motivations. SNSs have changed the way guests eat and experience their food and dishes. Marketing managers have effectively targeted SNSs as a marketing tool, yet have little research about drivers of guests' sWOM contribution on SNSs has been done. A model including the significant motives: (1) experiences, (2) opinion leadership, (3) reflection of self and (4) need for unique is tested to investigate their positive effects on contribution behavior of social media guests. Design/methodology/approach The data collected from 411 guests by using the snowball method was used for analysis. The structural equation modeling was applied to examine the relationships among the constructs and test the eight proposed hypotheses. Findings Results reveal that experiences, opinion leadership, reflection of self and need for unique were positively associated with contributing sWOM of restaurants. Furthermore, those who have positive experiences tend to be opinion leadership and reflection of self. And guests who show reflection of self, they are more likely to have opinion leaders and show need for uniqueness. Our study expands the existing frameworks of sWOM contribution by identifying various motivations and labeling sWOM. Findings provide restaurant managers with managerial implications for online marketing strategies on SNSs to attract sWOM contribution among guests. Research limitations/implications It has some limitations while discovering the motivations of positive sWOM contribution. First, we only focused on the motivation of contributing positive sWOM, while negative sWOM received many arguments in changing attitudes toward buying products or services. Second, we collected data in Vietnam only without comparing with different countries. Future research could explore further cross-cultural perspectives to fill the gap. Third, this study explored sWOM contribution in service environment, sWOM contribution from service context may be slightly different from those of product brands. Practical implications These findings highlight the motivations of sWOM contribution that restaurant managers must recognize and make use of it. SNSs have given power to consumers to post everything at anytime and anywhere they like, therefore restaurant managers need to deeply understand why their consumers contribute sWOM. In digital era, customers and guests have become the ultimate tools for promoting product or service brands. The marketing managers should create an online platform in order to facilitate their consumers to discuss their brand frequently (Charu et al., 2018). Restaurants should have policies to push positive eWOM maximally and also reduce advertising costs. Originality/value This is one of the first studies on sWOM contribution of what motivate guests to contribute their sWOM on SNSs. Theoretically, this study offers deep insights into the links between various motivations and sWOM in foodservice context. Managerially, understanding these motivations allow marketing managers create effective policies that motivate guests to contribute positive word of mouth.

Purpose It has been demonstrated in the US market that expected market excess returns can be predicted using the average higher-order moments of all firms. This study aims to empirically test this theory in emerging markets. Design/methodology/approach Two measures of average higher moments have been used (equal-weighted and value-weighted) along with the market moments to predict subsequent aggregate excess returns using the linear as well as the quantile regression model. Findings The authors report that both equal-weighted skewness and kurtosis significantly predict subsequent market returns in two countries, while value-weighted average skewness and kurtosis are significant in predicting returns in four out of nine sample markets. The results for quantile regression show that the relationship between the risk variable and aggregate returns varies along the spectrum of conditional quantiles. Originality/value This is the first study that investigates the impact of third and fourth higher-order average realized moments on the predictability of subsequent aggregate excess returns in the MSCI Asian emerging stock markets. This study is also the first to analyze the sensitivity of future market returns over various quantiles.

Purpose This study explores whether a new machine learning method can more accurately predict the movement of stock prices. Design/methodology/approach This study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model. Findings The hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016. Originality/value This study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.