Forecasting Abnormal Stock Returns and Trading Volume Using Investor Sentiment: Evidence from Online Search ?

By Kissan Joseph, M. Babajide Wintoki, Zelin Zhang. 2011

368 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research, Marketing

View Paper: https://www.semanticscholar.org/paper/2182ef2c599e2474a1bde08560e0aea18ec5322f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

1. Guangda Ouyang
2. Takumi Wakao
3. Fenghua Wen
4. Fumiko Takeda
5. Longhao Xu
6. Gang Kou

In Management Science and Operations Research

1. Guangda Ouyang
2. Takumi Wakao
3. Fenghua Wen
4. Fumiko Takeda
5. Longhao Xu
6. Gang Kou

In Marketing

1. Takumi Wakao
2. Fumiko Takeda

Citations in the corpus

• Kernel Archetypal Analysis for Clustering Web Search Frequency Time Series (2014, background): “Temporal changes in search volumes were found to correlate with changing behaviors of investors [18], [19] and to allow for predicting abnormal stock returns [20], [21].”
• Google Search Intensity and Its Relationship with Returns and Trading Volume of Japanese Stocks (2014, positive): “Following Joseph et al. (2011), we regard ߙ as the abnormal return of stocks.”
• Retail investor attention and stock price crash risk: Evidence from China (2019, methodology): “Recently, many studies, (e.g., Joseph et al. (2011), Yu and Zhang (2012), and Zhang et al. (2014)) have selected the Baidu search volume to measure RIA to investigate financial market phenomenon.”