Differential Emotions and the Stock Market - The Case of Company-Specific Trading

Practitioners and researchers alike increasingly use social media messages as an additional source of information to analyse stock price movements. In this regard, previous preliminary findings demonstrate the incremental value of considering the multi-dimensional structure of human emotions in sentiment analysis instead of the predominant assessment of the binary positive-negative valence of emotions. Therefore, based on emotion theory and an established sentiment lexicon, we develop and apply an open source dictionary for the analysis of seven different emotions (affection, happiness, satisfaction, fear, anger, depression, and contempt).To investigate the connection between the differential emotions and stock movements we analyse approximately 5.5 million Twitter messages on 33 S&P 100 companies and their respective NYSE stock prices from Yahoo!Finance over a period of three months. Subsequently, we conduct a lagged fixed-effects panel regression on the daily closing value differences. The results generally support the assumption of the necessity of considering a more differentiated sentiment. Moreover, comparing positive and negative valence, we find that only the average negative emotionality strength has a significant connection with company-specific stock price movements. The emotion specific analysis reveals that an increase in depression and happiness strength isassociated with a significant decrease in company-specific stock prices

IS IT WORTH IT? DISMANTLING THE PROCESS OF SOCIAL MEDIA RELATED SALES PERFORMANCE

Social media platforms present unique possibilities for companies to interact with their customers and take up a key role in building relationships. A substantial body of research has demonstrated the impact of social media regarding, for example, brand awareness and corporate reputation. However, little is known concerning the financial Return on Investment from social media engagement and specific strategies to leverage it. To this end, the study draws on relationship marketing theory to develop and operationalise a research model, which understands objective firm performance in terms of sales as a result of relationship antecedents (i.e., corporate investment and dyadic similarity) mediated through the customer-perceived relationship strength. To test the assumed research model, we collect and analyse a dataset of over 1.5 million Twitter messages revolving around ten car manufacturers and measure the impact on new car registration volumes. The results of this study suggest that companies can increase their sales volume through greater relationship investment (i.e., by providing interest group-specific information) and by adopting a social media strategy that promotes the users’ relationship satisfaction (i.e., raises the share of voice within user messages)

Differentiated Sentiment Analysis of Corporate Social Media Accounts

Social media managers as well as analysts use social media messages as an additional channel to manage and measure opinions towards brands. Currently, however, sentiment tools have been predominantly focused on the binary positive-negative valence of emotions and thereby neglected the multi-dimensional structure of human emotions. Moreover, while practitioners try to address the issue of undifferentiated customer requests towards a brand by operating more interest group-specific accounts, research still lacks understanding regarding the impact of different account types. We approach this research gap by developing a classification of social media accounts and, subsequently, deploy a sentiment analysis that differentiates between seven emotions within 532,363 thousand tweets towards 641 accounts from 33 S&P 100 companies. Our results confirm the assumed necessity of considering different account types when studying corporate social media presence and assessing differentiated emotions in social media analytics

Analyzing the relationship between differentiated online sentiment and company-specific stock prices

PRACTITIONERS AND RESEARCHERS ALIKE INCREASINGLY USE SOCIAL MEDIA MESSAGES AS AN ADDITIONAL SOURCE OF INFORMATION WHEN DEALING WITH STOCKS. BASED ON EMOTION THEORY AND AN ESTABLISHED SENTIMENT LEXICON, WE DEVELOP AND APPLY AN OPEN SOURCE DICTIONARY FOR THE ANALYSIS OF SEVEN DIFFERENT EMOTIONS IN 5.5 MILLION TWITTER MESSAGES ON 33 S&P 100 COMPANIES. WE FIND VARYING EXPLANATORY POWER OF DIFFERENT EMOTIONS (ESP. HAPPINESS, AND DEPRESSION) FOR COMPANY-SPECIFIC STOCK PRICE MOVEMENTS OVER A PERIOD OF THREE MONTHS

Increasing sales performance through social media activities

SOCIAL MEDIA PLATFORMS PRESENT UNIQUE POSSIBILITIES FOR COMPANIES TO INTERACT WITH THEIR CUSTOMERS AND TAKE UP A KEY ROLE IN BUILDING RELATIONSHIPS. HOWEVER, LITTLE IS KNOWN CONCERNING THE FINANCIAL RETURN ON INVESTMENT FROM SOCIAL MEDIA ENGAGEMENT AND SPECIFIC STRATEGIES TO LEVERAGE IT. THE ANALYSIS OF OVER 1.5 MILLION TWEETS REVOLVING AROUND TEN CAR MANUFACTURERS SUGGESTS THAT COMPANIES CAN INCREASE THEIR SALES VOLUME THROUGH GREATER RELATIONSHIP INVESTMENT AND BY ADOPTING A SOCIAL MEDIA STRATEGY THAT PROMOTES THE USERS’ RELATIONSHIP SATISFACTION