Changes in Investors' Market Attention and Near-Term Stock Market Returns

Do sentiment indices impact the premium of prominent pricing factors?

“…The Granger causality test contributes new evidence to the literature that potential shifts in VIX impact the return premium on CMA and RMW and vice versa. Klemola et al (2016) find that GSVI can forecast future returns except for D_Bull. To further test the Klemola et al (2016)'s GSVIs impact on expected returns of prominent pricing factors and addressing our hypothesis II, we provide two-way Granger causality test results in Table 3.…”

“…This study employs shifts in four GSVIs [market crash (D_Crash), bear market (D_Bear), bull market (D_Bull), and market rally (D_Rally)] because they capture the extreme events and the market variations. Klemola, Nikkinen, and Peltomäki (2016) employ GSVIs to examine the relationship between investors' market attention and stock market returns. They find that these measures of market attention are potential gauges for investor sentiment.…”

“…Among others, Randall, Suk, and Tully (2003) have used Net Cash Flow into Mutual Funds, Lashgari (2000) have used the Barron's Confidence Index, Baker and Wurgler (2006) employs the issuance Percentage, Whaley (2000) has employed the VIX-Investor Fear Gauge, and Kumar and Persaud (2002) have employed the Risk Appetite Index (RAI). Klemola et al (2016) have used Google Search Volume Index for Google search terms "market crash, bear market, bull market, and market rally" to predict near-term future stock returns. Habibah et al (2017) have used the pessimistic GSVIs and VIX to predict the stock returns.…”

“…First two terms measure negative sentiments while the other two measure positive sentiments. These search terms were used by Klemola et al (2016) to measure changes in investors' market attention. Because of economic uncertainty, investors are more concerned about the risk and return on their investment, and they try to minimize the risk by selling (buying) stocks if stock prices are expected to go down (up).…”

Examining the Sources of Excess Return Predictability: Stochastic Volatility or Market Inefficiency?

“…In a follow up study, García (2013) …nds that a sentiment measure constructed using counts of positive versus negative words in …nancial columns of the New York Times helps to predict daily DJIA returns. Klemola, Nikkinen and Peltomäki (2016) …nd that weekly changes in the volume of Google searches for the terms "market crash" and "bear market" are signi…cant negative predictors of 1-week ahead percentage changes in the S&P 500 stock index, but they do not control for stochastic volatility. But given that these three studies focus on the predictability of raw stock returns as opposed to excess stock returns, the predictability …ndings are not informative about market ine¢ ciency.…”