Analysis of the investor sentiment and its influence on financial markets behavior through social networks

Resumo

Research in finance is constantly growing and evolving. The first financial theories such as interest theory or game theory are characterized by an important mathematical component. However, in recent decades, theories from other scientific fields have been incorporated, such as psychology. This evolution has had the result of the creation of two schools, the school of traditional finance, whose base are the theories of a more mathematical nature; and the school of behavioral finance, based on psychological theories about human behavior. Behavioral finance defend that investors do not always behave in a rational way, which completely contradicts the postulates of traditional finance. This is where the main difference between these two schools of thought lies and where the criticisms between both lie. To defend their position, behavioral finance uses models and psychological theories about human behavior in order to explain the anomalies that arise in the market and that traditional finance cannot explain. Within the great variety of theories and psychological concepts that are applied in behavioral finance, sentiment stands out. The sentiment refers to the perception that an individual has at a certain moment. This perception influences decision making, and therefore influences investors, and this influence will be reflected in financial markets. Therefore, sentiment is a relevant factor in explaining investor and financial markets behavior. There are different ways of measuring sentiment ranging from simple surveys to elaborate indexes from different financial variables. In recent years a new way of measuring it has emerged thanks to advances in language recognition software, which allows to extract the sentiment from text. In this way it is possible to extract sentiment from news headlines, blogs, or social network posts. Therefore, the analysis of investor sentiment has become a prolific field of research and is currently in constant growth. That is why, this work focuses mainly on the analysis of the relationship between the investment sentiment from social networks and financial markets. Therefore, the objectives of this paper can be summarized in proving the existence of a relationship between the investor sentiment from social networks and financial markets, proving that this relationship is bidirectional, and proving the relevance of the characteristics of investors (experience, type of analysis that makes (technical, fundamental ...), etc.) in said relationship. For this, different databases (monthly and daily) have been used, in which the common variable is the sentiment extracted from messages posted on StockTwits, a social network specialized in financial markets. In addition, financial variables have been used, such as the S & P 500 index returns or the VIX index returns, and variables from social networks, such as the number of messages posted (monthly or daily depending on the database used), investment experience of users of the social network or the investment period of users of the social network (if they usually invest in the short or long term). To carry out the analysis, qualitative methodologies such as fuzzy set qualitative comparative analysis (fsQCA) and quantitative methodologies such as logit and probit models, Granger causality analysis, ARIMA models and GARCH models were used. The results obtained satisfactorily confirmed all the proposed hypotheses, demonstrating the importance of social networks in financial markets. Therefore, this research has many implications for investors and also for companies, since it has been proven that social networks influence the movements and risk of financial markets, and also that the profile of the investor in social networks is relevant in this relationship. Therefore, considering that there is a bidirectional relationship between the investment sentiment of social networks and financial markets, both investors and companies should pay attention to social networks in order to improve their investment strategies.