Asymmetric Impact of Investor Sentiment on Brazilian Stock Market Volatility

Abstract

Purpose: The purpose of this study was to analyze the effect of investor sentiment on the volatility of the Brazilian stock market. Specifically, it aimed to identify if the asymmetric behavior of sentiment could be observed in emerging markets, considering companies that have characteristics that are difficult to price. Originality/value: Unlike most studies on investor sentiment, this study focuses on its impact on the stock market volatility, as well as on the characteristics of companies associated with difficult pricing. Design/methodology/approach: The volatility of the IBRX100 index was used to represent the Brazilian stock market, and as a proxy for investor sentiment it was selected Miranda's index (2018), based on market data. Data were estimated using the two-stage least squares (MQ2E) technique to address endogeneity problems. Finally, the volatility of companies with difficult-to-price characteristics was segregated to analyze their sensitivity to sentiment. Findings: The results indicate that sentiment has a negative and sig nificant relationship with the volatility of the Brazilian market, as well as evidences an asymmetrical behavior, being statistically stronger in pessimistic periods. Additional analyzes evidence that the explanatory sentiment capacity is sensitive to companies' characteristics, but only companies with a high book-to-market ratio showed asymmetric behavior, as expected by the literature. The portfolios segmented by size and illiquidity maintained an asymmetric behavior, but it was the volatility of the large companies and the less illiquid ones that were best explained by sentiment, indicating that the Brazilian market has distinctive characteristics in relation to developed markets.

Exogenous shock: hierarchical effect of the industrial entrepreneur confidence in the mist of the COVID-19 pandemic

“…The analysis through the Pearson correlation can provide the first signs of the relationships between the variables which are going to be identified in the estimated models (Ferreira et al ., 2021). Table 4 presents the result of the correlation.…”