Baidu index and predictability of Chinese stock returns
© The Author(s). 2017
Received: 5 September 2016
Accepted: 13 March 2017
Published: 24 March 2017
A number of studies have investigated the predictability of Chinese stock returns with economic variables. Given the newly emerged dataset from the Internet, this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns. The empirical results show that 1) the Search Frequency of Baidu Index (SFBI) can predict next day’s price changes; 2) the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks; 3) the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs. These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.
The predictability of stock returns has long been the focus in financial economics with both cross-sectional and time series analysis. Voluminous variables have been put forward to predict the stock returns, including book-to-market ratio (Pontiff and Schall, 1998), inflation rate (Nelson, 1976), dividend-price ratio (Fama and French, 1988) and term structure of the interest rates (Campbell, 1987), etc. As for the predictability of Chinese stock market, Lee and Rui (2000) show that US stock return helps predict returns of Shanghai A and B stocks. Chen et al. (2010) document that only 5 out of 18 firm-specific variables can predict returns in Chinese stock market and attribute this weak predictability to the low informativeness and less heterogeneously distributed of stock returns. Goh et al. (2013) find that US economic variables have statistically significant predictive power for Chinese stock returns after China entering into the WTO. Specifically, the joint variables of China and US economic variables have superior predictive power. Jordan et al. (2014) find that the returns of economically-linked economies can predict the aggregate Chinese stock market. Chen et al. (2016) show that the U.S. economic variables can strongly forecast the monthly volatility of Chinese stock returns.
Recently, scholars begin to employ the information extracted from Internet as the new source of information for financial studies, including the online stock message boards (Tumarkin and Whitelaw, 2001; Antweiler and Frank, 2004), Twitter (Bollen et al., 2011; Zhang et al., 2016a), Google Trends (Da et al., 2011; Da et al., 2015), Baidu Index (Zhang et al., 2013), Baidu News (Zhang et al., 2014; Shen et al., 2016), online stock commentary column (Zhang et al., 2016c) and Sina Weibo (Jin et al., 2016). In particular, Da et al. (2011) show that the search frequency from Google Trends can predict stock returns in the next weeks. Joseph et al. (2011) consider the online ticker search as the proxy for investor sentiment and find that the sentiment can predict abnormal stock return and trading volume at weekly horizon. Dimpfl and Jank (2016) find that the internet search queries can predict next day’s volatility.
This paper connects with the above-mentioned two streams of literature and contributes to the existing literature in two aspects. On the one hand, we give the first empirical study of the predictability of Baidu Index, the newly emerged internet information, on Chinese stock returns, while other studies mainly focus on the market variables (Goh et al., 2013; Chen et al., 2010; Chen et al., 2016). Although Zhang et al. (2013) advocate the search frequency of stock name in Baidu Index as the proxy for investor attention, they only focus on the explanatory power of this proxy on stock return and do not investigate its predictability. On the other hand, we complement the existing studies on Chinese stock returns (Goh et al., 2013; Jordan et al., 2014) in the sense that our predictive variable is at daily horizon. The causes of the changes in daily returns, volatility of trading volume is significantly different from the monthly movement of the market performance (Admati and Pfleiderer, 1988; Amihud and Mendelson, 1987). Therefore, we can gain more insights into the pricing mechanism and our study has potential important implications for pricing mechanisms, asset allocation, and risk management.
The remainder of this paper is organized as follows. "Data description" section describes the Baidu Index and capital data. "Empirical analysis" section performs the empirical analysis, including the lead-lag relationships, the cross-sectional analysis and the trading strategy. "Conclusions" section sets forth the conclusions.
The sample focuses on both the Shanghai Stock Exchange and the Shenzhen Stock Exchange with 30 stocks in each board respectively, i.e., the ChiNext, the SME Board and the Main Board, covering the calendar days from March 1st 2011 to March 30th 2012.1 Totally, there are 90 stocks with 267 trading days. In particular, we obtain the daily trading volume, daily stock return rate after dividend reinvestment and the market return from the China Stock Market and Accounting Research (CSMAR) database. The 1-min prices are retrieved from the RESSET Database to calculate the intraday volatility. In a pooled analysis, the daily trading volume ranges from 33700 to 1.5*108, the firm capitalization ranges from 6.5*105 to 5.8*109, the PE ratio ranges from −667 to 3119 and the turnover ranges from 0.0398 to 58.94. Due to the diversified distribution in different boards as well as the distinct characteristic of the stocks, our sample can be viewed as a parsimonious representation of Chinese stock market.
where AV SFBI is the average value in the sample period and SD SFBI is the standard deviation of the SFBI time series.
where Ret I,t is the daily stock return rate after dividend reinvestment and Ret M,t is the corresponding market index return. In particular, we choose the return of Chinese Stock Index 300 (CSI 300) as the market return for the reason that the CSI 300 is the first index launched by both the Shanghai and Shenzhen Stock Exchanges and thus it represents the whole market.
where AVTV is the daily trading volume on each trading day and AV TV is the average trading volume in the whole sample period.
where Pt is the 1-min stock price and AVP is the average stock price. N denotes the number of observation and N = 240.
Results and discussion
This section provides empirical analysis on the lead-lag relationships between SFBI and stock returns, the market reactions around the MSD and LSD events as well as the performance of the trading strategy.
Summary of the regression results This table reports the regression results of model (6). As the analysis is based on the stock by stock regression, we employ the Bonferroni correction method to correct the p-values for the multiple comparisons problem. The significance cut-off is set to α/n (α = 0.05 and n = 90). Since not all the variables are standardized, the reported coefficients are not the basis points but depend on the scale of the variables
SSFBI t − 1
SSFBI t − 2
SSFBI t − 3
SSFBI t − 4
SSFBI t − 5
AbRet t − 1
AbRet t − 2
AbRet t − 3
AbRet t − 4
AbRet t − 5
MSD and LSD
This paper employs the Baidu Index as a predictive variable and investigates its predictability for Chinese stock returns. The empirical findings show that Baidu Index can predict the price changes on the next trading day. After constructing the MSD and LSD, we mainly find that the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks. Besides, we also construct a trading strategy by shorting on the most SFBI and longing on the least SFBI. The trading strategy outperforms the corresponding market index returns. However, we must caution scholars in adopting our trading strategy in real investment. Because the transaction costs associated with the rebalancing are not considered.
This work is supported by the National Natural Science Foundation of China (71320107003 and 71532009).
DS and YZ contributed to study design, data collection and provided the first draft of the manuscript. DS, WZ, YZ and XX participated in the empirical analysis, interpretation of the results, final draft and proof reading of this manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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