The Nonlinear Relationship between Investor Sentiment, Stock Return, and Volatility

Gang He,Haifeng Gu,Shuzhen Zhu

doi:10.1155/2020/5454625

Abstract

Based on the DSSW model, we analyze the nonlinear impact mechanism of investor sentiment on stock return and volatility by adjusting its hypothesis in Chinese stock market. We examine the relationship between investor sentiment, stock return, and volatility by applying OLS regression and quantile regression. Our empirical results show that the effects of investor sentiment on stock market return are asymmetric. There is “Freedman effect” in Chinese stock market, but only optimistic sentiment has a significant nonlinear impact on stock market returns when the stock market is a balanced market or a bear market. Meanwhile, “create the space effect” does exist in Chinese stock market too. It only exists when the market is in equilibrium, and only pessimistic sentiment has the nonlinear effect on stock market volatility.

Highlights

A growing body of research focuses on the relationship between investor sentiment, stock return, and volatility
Stambaugh et al [8] studied the stock return characteristics of these arbitrage strategies under different sentiment states by building different portfolio strategies. e results show that high sentiment leads to an overvalued stock far more than the undervalued stock price caused by low sentiment
In recent years, some papers have begun to study the nonlinear effects of investor sentiment on stock market return, they tend to use the ordinary least squares regression analysis method to study the relationship between the two when the market returns are in the mean level

Summary

The Model

De Long et al [11] proposed the famous DSSW model which pointed out the influence of irrational behavior of noise traders on stock prices. ey laid the basic framework of noise trading theory and questioned the rationality of traditional financial theory. E second item in formula (9) indicates that when the overall sentiment of the investor is not 0, the price of stock will deviate from its fundamental value. E third item in formula (9) describes the volatility of risky asset prices caused by changes of investor sentiment in the market. Compared with the classic DSSW model, first of all, the adjusted DSSW model represents the investor’s cognitive bias ρ∗ as a function of sentiment It holds that when investor sentiment is generally optimistic, f(St) > 0, g(St) < 1, the corresponding average of expected price deviation ρ∗ is positive, but the expected price bias fluctuation σ. We express the cognitive bias of investors as a function related to sentiment, which is more helpful to deduce the influence of investor’s sentiment change on the stock market price in theory. Frequent volatility of investor sentiment will increase the volatility of stock market

Empirical Design

OLS Nonlinear Regression

Quantile Regression Analysis

Findings

Conclusions