Can google search volume index predict the returns and trading volumes of stocks in a retail investor dominant market

Abstract

This research examines whether Google search volume index (GSVI), a proxy of investor attention, can predict the excess returns and abnormal trading volumes of TPEx 50 index constituents. It also explores the motive underlying GSVI based on positive or negative shocks to stock prices. The empirical data include 48 companies from TPEx 50 index constituents and cover a period from 1 September 2016 to 31 August 2019. The empirical results present that (1) lagged GSVI negatively affects current excess returns, perhaps due to the characteristics of TPEx, in which there are a higher proportion of retail investors, smaller listed companies, and a higher information asymmetry problem. (2) Lagged GSVI can positively affect abnormal current trading volumes. (3) If GSVI is driven by positive shocks, then it can predict excess returns and abnormal trading volumes positively.

Keywords:

• Google search volume index
• TPEx 50 index
• Excess returns
• Abnormal trading volumes
• Investor attention

Subjects:

• G00
• G12
• G40

Public interest statement

The Google Trends tool provides google search volume index (GSVI) for one or more keywords. Existing studies suggest that retail investors’ attention can be gauged by GSVI and that GSVI can forecast stock returns and trading volumes. Our paper aims to examine the effects of GSVI on stock returns and trading volumes in a market dominated by retail investors, i.e. the Taipei Exchange (TPEx). In contrast to most literature, we find that GSVI negatively affects future returns (1-12 weeks later). That could be because when retail investors with lagging information pay attention to stocks, stock returns are likely to reverse. With respect to trading volume, when investors receive favorable news and generate searches, they can make buying decisions according to the search results, resulting in abnormal growth in share trading volume. In contrast, investors may not be used to shorting in response to unfavorable news, therefore no apparent growth occurs in stock trading volume.

Acknowledgements

Financial supports from the Ministry of Science and Technology [grant number 108-2410-H-224 −010; 109-2410-H-224 −015] are gratefully acknowledged.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. The Google Trends tool was launched in 2006 to provide GSVI, which contains statistical data for keywords. Users can observe the search popularity trend of a keyword in a country in any period. The data are gathered from every user who uses the Google search engine, and any use of the engine leaves a trace in Google Trends.

2. Ben-Rephael et al. (2017) use Bloomberg news search as the proxy variable of institutional investor attention, explore the changes in stock returns when institutional investors identified a certain company’s stock, and compare it with GSVI.

3. In Taiwan, the upper limit of commission fee is 0.1425% for buying or selling a share. However, most brokerage firms give a discount of 40 percent in order to shoot for a higher market share. Even some brokerage firms can provide a discount of 70 percent to grab more customers.

4. We suggest that future studies can examine the profitability of this strategy by using more rigorous methodologies.

Additional information

Funding

This work was supported by the Ministry of Science and Technology, Taiwan [109-2410-H-224 −015]; Ministry of Science and Technology, Taiwan [108-2410-H-224 −010].

Notes on contributors

Huei-Hwa Lai

Huei-Hwa Lai is an assistant professor at the Department of Business Administration, Chaoyang University of Technology, Taiwan. She obtained her PhD in Finance from National Yunlin University of Science and Technology, Taiwan. Her research interests cover corporate finance, corporate government, and stock market.

Tzu-Pu Chang is an associate professor at the Department of Finance, National Yunlin University of Science and Technology, Taiwan. He obtained his PhD in Management from National Chiao-Tung University, Taiwan. His research interests include investment and data analysis. Hence, he recently studies machine learning in the finance field. The other two authors, Cheng-Han Hu and Po-Ching Chou, are respectively master and PhD students supervised by Tzu-Pu Chang. They assist this research in data collection and data processing.