Liquidity Risk and Investors’ Mood: Linking the Financial Market Liquidity to Sentiment Analysis through Twitter in the S&P500 Index

Abstract

Microblogging services can enrich the information investors use to make financial decisions on the stock markets. As liquidity has immediate consequences for a trader’s movements, this risk is an attractive area of interest for both academics and those who participate in the financial markets. This paper focuses on market liquidity and studies the impact on liquidity and trading costs of the popular Twitter microblogging service. Sentiment analysis extracted from Twitter and different popular liquidity measures were gathered to analyze the relationship between liquidity and investors’ opinions. The results, based on the analysis of the S&P 500 Index, found that the investors’ mood had little influence on the spread of the index.

Leveraged Trading, Irrational Sentiment and Sustainability in the Stock Market: Evidence from China

“…Second, there is little literature related to the relationship between leverage ratio and the sentiment feedback coefficient, and we find the leverage ratio and sentiment feedback coefficients have an "inverted U-shaped" relationship. Third, although there is a huge literature looking at investor sentiment, including measures of investor sentiment [26][27][28][29], investor sentiment and financial market anomalies [2], investor sentiment and stock returns [30][31][32][33][34], investor sentiment and stock market risk [35,36], and investor sentiment and corporate finance [37], however, the studies failed to give a deep looking in the nature of investor sentiment. We screen the multidimensionality contained in the leverage ratio to accurately capture the relationship between the leverage ratio and investor sentiment, and we find that the leverage ratio after purification has the typical characteristics of irrational sentiment.…”

“…There is a huge amount of literature looking at investor sentiment, including measures of investor sentiment [26][27][28][29], investor sentiment and financial market anomalies [2], investor sentiment and stock returns [30][31][32][33][34], investor sentiment and stock market risk [35,36], and investor sentiment and corporate finance [37]. Chinese scholars have also conducted a lot of research on investor sentiment in the Chinese stock market [38,39].…”

Sentiment classification of skewed shoppers' reviews using machine learning techniques, examining the textual features

“…After conducting n‐grams, the bag of words was used to break words into individual word counts variables. The machine learning algorithms have been used in all areas of day‐to‐day life, from anomaly detection, 21 and crash prediction, 22 to sentiment analysis of the investors 23 . The below sections discuss the machine learning techniques have been used for training and evaluation of model accuracy.…”

Investor Attention from Internet Search Volume and Underreaction to Earnings Announcements in Korea

“…In addition, this may be a sustainable development in that investors, especially individual investors who are likely to be inferior in capturing qualified information in comparison with institutional traders, can use public information through the internet for investment in very competitive stock markets. By extension, Guijarro et al [26] analyzes the impact of investors' mood captured from Twitter on market liquidity and on the trading costs.…”