Can customer sentiment impact firm value? An integrated text mining approach

Developing measures to capture customer sentiment and securing a positive customer experience is a strategic necessity to improve firm profitability and shareholder value. The paper considers the relationship between customer satisfaction, earnings, and firm value as these drives change in stock prices, customer, and investor sentiment. The present study investigates the impact of customer sentiment polarity on stock prices based on Indian automobile sector databased such as the Indian Nifty Auto SNE (Maruti Suzuki, Tata Motors, and Eicher). A top-down approach is adopted to construct a financial proxy-based sentiment index completed with sentiment extracted from automobile news and customer reviews. The paper uses a text mining approach to holistically measure customer sentiment’s impact on investor sentiment and stock prices. The study was initially performed at the overall individual stock from the Nifty Auto NSE but focused on the top three passenger vehicle manufacturing companies i.e., Maruti Suzuki, Tata Motors, and Eicher. It was found that the sentiment index was augmented with news and customer reviews allows predicting more accurately NIFTY AUTO stock price movements. This implies that customer sentiment is a major driver of investor sentiment which in turn impacts the stock market and the firm value. Thus, the present study is an integrated approach to holistically measure customer sentiment’s impact on investor sentiment and stock prices.WOS:00071138140002

Sentiment Analysis of Twitter Data for Predicting Stock Market Movements

Predicting stock market movements is a well-known problem of interest. Now-a-days social media is perfectly representing the public sentiment and opinion about current events. Especially, twitter has attracted a lot of attention from researchers for studying the public sentiments. Stock market prediction on the basis of public sentiments expressed on twitter has been an intriguing field of research. Previous studies have concluded that the aggregate public mood collected from twitter may well be correlated with Dow Jones Industrial Average Index (DJIA). The thesis of this work is to observe how well the changes in stock prices of a company, the rises and falls, are correlated with the public opinions being expressed in tweets about that company. Understanding author's opinion from a piece of text is the objective of sentiment analysis. The present paper have employed two different textual representations, Word2vec and N-gram, for analyzing the public sentiments in tweets. In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from twitter and analyze the correlation between stock market movements of a company and sentiments in tweets. In an elaborate way, positive news and tweets in social media about a company would definitely encourage people to invest in the stocks of that company and as a result the stock price of that company would increase. At the end of the paper, it is shown that a strong correlation exists between the rise and falls in stock prices with the public sentiments in tweets.Comment: 6 pages 4 figures Conference Pape

Understanding the Relationship between Online Discussions and Bitcoin Return and Volume: Topic Modeling and Sentiment Analysis

This thesis examines Bitcoin related discussions on Bitcointalk.com over the 2013-2022 period. Using Latent Dirichlet Allocation (LDA) topic modeling algorithm, we discover eight distinct topics: Mining, Regulation, Investment/trading, Public perception, Bitcoin’s nature, Wallet, Payment, and Other. Importantly, we find differences in relations between different topics’ sentiment, disagreement (proxy for uncertainty) and hype (proxy for attention) on one hand and Bitcoin return and trading volume on the other hand. Specifically, among all topics, only the sentiment and disagreement of Investment/trading topic have significant contemporaneous relation with Bitcoin return. In addition, sentiment and disagreement of several topics, such as Mining and Wallet, show significant relationships with Bitcoin return only on the tails of the return distribution (bullish and bearish markets). In contrast, sentiment, disagreement, and hype of each topic show significant relation with Bitcoin volume across the entire distribution. In addition, whereas hype has a positive relation with trading volume in a low-volume market, this relation becomes negative in a high-volume market

How Mood Affects The Stock Market: Empirical Evidence From Chinese Microblog

With the advent of the Web2.0 era, social media can achieve the rapid transmission of information and reduce the information asymmetry. In our study, we selected social media of Sina Weibo because of its wide use in China. Through text mining technology, this paper we extracted total 22504 tweets related to real estate industry. We succeeded in classify microblog accounts and two clusters of social media users are selected: individual investors and official media. Based on two dimensions of attention and emotion, this paper discusses the influence of different users on the stock market. Interestingly, the empirical results show that (1) there is an inverse U-shaped curve between attention and stock return for both official media and investor which support the over-attention underperformance hypothesis. (2) We also find that both daily sentiment of official media and investor are positively correlated to stock return. Our study contributes to a better understanding of emotion and stock market, particularly based on Chinese microblog

Mining the Impact of Investor Sentiment on Stock Market from WeChat

In this study, the CSI 300 Index in China mainland and original articles from authoritative stock WeChat public accounts are investigated regarding their relations. First, a sentence-level sentiment classification approach for analyzing investor sentiment polarities in text corpus is proposed by expanding synonyms. Then, the Granger causality test is utilized to examine the impact of sentiment index on the stock price and volume-values. It shows that the influence of overall investor sentiment on volume-values is more rapid than that on stock price and the impact of positive sentiment is found to be more lasting than the negative in both stock price and volume-values. Furthermore, it is worth noting that there is a dual-stage phenomenon in the impact of positive sentiment on volume-values, which indicates that some investors react to positive information immediately while others may choose to wait and follow the trend