Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 2407086, 11 pages
https://doi.org/10.1155/2017/2407086

Research Article

Does Online Investor Sentiment Affect the Asset Price Movement? Evidence from the Chinese Stock Market

Chi Xie1,2 and Yuanxia Wang¹

¹College of Business Administration, Hunan University, Changsha 410082, China
²Center of Finance and Investment Management, Hunan University, Changsha 410082, China

Correspondence should be addressed to Chi Xie; xiechi@hnu.edu.cn

Received 22 April 2016; Accepted 19 December 2016; Published 15 January 2017

Academic Editor: Shaoyi He

Copyright © 2017 Chi Xie and Yuanxia Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

With the quick development of the Internet, online platforms that provide financial news and opinions have attracted more and more attention from investors. The question whether investor sentiment expressed on the Internet platforms has an impact on asset return has not been fully addressed. To this end, this paper uses the Baidu Searching Index as the agent variable to detect the effect of online investor sentiment on the asset price movement in the Chinese stock market. The empirical study shows that although there is a cointegration relationship between online investor sentiment and asset return, the sentiment has a poor ability to predict the price, return, and volatility of asset price. Meanwhile, the structural break points of online investor sentiment do not lead to changes in the asset price movement. Based on the empirical mode decomposition of online investor sentiment, we find that high frequency components of online investor sentiment can be used to predict the asset price movement. Thus, the obtained results could be useful for risk supervision and asset portfolio management.

1. Introduction

The Internet has become an important platform to acquire and exchange information, and the impact of the Internet has penetrated every field of modern society in recent years. According to the 2014 Annual Report released by the China Internet Network Information Center, the number of cybercitizens has reached up to 0.649 billion, and the average time for each cybercitizen spending on the Internet is as long as 26.1 hours per week. A report published by iResearch also shows that there are more than 35.96 million active investors on easymoney.com, China Economic Net, and the other top 10 online platforms. Online forums, instant messenger, and financial news websites become a substitute for the traditional media to acquire financial information and a widely used channel to express their views on financial markets. In the Internet era, investors receive information more quickly than before, and bad news usually spread in a short time; thus a systematic risk event is more likely to occur. Under this background, it is important to study the impact of online investor sentiment on the asset price movement in the stock market.

The empirical evidence on the relationship between investor sentiment and asset price has been documented by previous studies. For example, De Long et al. [1] find that stock price can diverge significantly from fundamental values because of noise traders’ beliefs, and investor sentiment can explain underpricing of closed-end mutual funds and some other financial anomalies. Barber and Odean [2] propose the price pressure assumption and argue that attention to a stock will lead to an increase in stock price; they also verify this assumption by using abnormal return, abnormal trading volume, and media exposure. Kling and Gao [3] and Stambaugh et al. [4] also note that financial anomalies are partly caused by investor sentiment. Baker et al. [5] point out that relative sentiment is correlated with the relative price of dual-listed companies. Lamia [6] calculates the price premium of investor sentiment by adopting the VAR method. Uygur and Taş [7] provide evidence that the change in investor sentiment has more influence on the industry, banking, and food and beverages sectors. Ni et al. [8] argue that investor sentiment can greatly lead to mispricing in the Chinese stock market. Luo et al. [9] also note that investor sentiment is an important factor in financial risk contagion under the condition of quick information flows. Kumari and Mahakud [10] use VAR GARCH models to investigate the significant effect of investor sentiment on the stock market volatility. More recently, Wu et al. [11] reexamine the risk premiums in the Fama-French model and discuss the role of investor sentiment, and market premiums fall as investors in the stock market show extreme optimism or extreme pessimism. Zaremba [12] reports that the variation of market sentiment plays an important role in returns on cross-country value strategies. Miwa [13] finds that market-wide investor sentiment induces stock mispricing. In general, most of the existing literature provides evidence that investor sentiment has significant impact on stock price.

With the quick development of the information technology, modern financial market relates to the Internet more closely. With the easier access to the Internet, investors receive more and more information from the Internet, and their sentiment and opinions can also be expressed on online platforms and spread widely in a short time. Some scholars pay attention to this phenomenon and investigate the impact of the Internet on financial markets. Campbell and Cecez-Kecmanovic [14] predict that online forums are the sources of abnormal trading behavior in the stock market. Takeda and Wakao [15] examine the relationship between search intensity and stock trading behavior by using the Google Search Index and 189 Japanese stocks from 2008 to 2011 and find that higher search intensity is associated with more active trading activity. Leung and Ton [16] investigate the impact of Internet stock message boards on cross-sectional returns of small-capitalization stocks and conclude that a high message board activity quickly reflects itself in the price of the above stocks in the Australian Securities Exchange. Hamid and Heiden [17] prove that the models for predicting the stock market volatility can be improved by using search engine data.

Previous works have verified the impact of investor sentiment on asset price, but until now, the impact of online investor sentiment on the asset price movement has not been fully explored. The question whether online investor sentiment leads to changes in the asset price movement has not been empirically examined. Aiming to fill the gap in the current literature, we apply the autoregressive distributed lag model and the empirical mode decomposition (EMD) to study the impact of online investor sentiment on the asset price movement based on a structural break point investigation.

The remainder of this paper is organized as follows. Section 2 describes our data collection and conducts a descriptive analysis of online investor sentiment and the asset price movement. Section 3 applies cointegration, Granger causality test, autoregressive distributed lag model, and empirical mode decomposition to examine the relationship between online investor sentiment and the asset price movement. Section 4 is the conclusion.

2. Data Collection and Variable Analysis

To investigate the impact of investor sentiment on the asset price movement, we first define the variables of online investor sentiment and the asset price movement and then analyze the basic characteristic of the asset price movement.

There are two methods of evaluating online investor sentiment. The first is the direct way which detects the number and content of posts on the Internet forums, such as text mining methods. The second method uses the search intensity index provided by Google, Baidu, and other search engines. The evaluation result obtained by the first method contains more investor sentiment information, but at the same time, more noise information could be embedded in the evaluation results; moreover, the accuracy of the evaluation results is easily affected by manual data collecting. Due to the strong searching function of Google and Baidu search engine, the second method is more objective than the first method. Thus, we adopt the Search Index of the Shanghai Stock Index, released by the website Baidu (https://index.baidu.com/), which is a widely used search engine in China, to measure investor sentiment on online platforms. This variable is denoted by SENINDEX. The sample period starts from January 14, 2011, and ends on June 5, 2015. The frequency of sentiment data is weekly. In general, when investors are optimistic about the market, the SENINDEX index is relatively high, while the low investor sentiment is related to the low level of SENINDEX index. Additionally, we use the variation (VSEM) and conditional volatility (GARCHVSEN) to describe investor sentiment for the overall evaluation. The GARCH model of online investor sentiment is constructed as follows:

where is the conditional volatility. By comparing different distributions, we find that the GARCH model with a generalized error distribution (GED) gets a higher goodness-of-fit compared to the normal and distributions. Thus we use the GARCH-GED model to fit the asset price and sentiment series data.

We use the Shanghai Stock Exchange Index to represent the asset price movement of the Chinese stock market. The sample period is consistent with the online investor sentiment. The original data of the stock index are obtained from WIND database. Similar to investor sentiment data, we also use the primary (SHINDEX), variation (RSH), and conditional volatility (GARCHRSH) to describe the asset price movement. The GARCH model of asset price is as follows:

By applying (1)–(2), the evaluation results of different variables are acquired in Figures 1–3. Observing Figures 1–3, a similar volatility trace can be found for all three groups’ time series data, indicating significant correlation between investor sentiment and the asset price movement. Meanwhile, when asset price decreases, investor sentiment becomes stable (Figure 1). But in the period from June 2014 to June 2015, as asset price dramatically increased, investor sentiment rose simultaneously. Thus, the correlation structure may change in different market conditions. From Figures 2 and 3, we find that the volatilities of investor sentiment are higher than those of asset price, suggesting that investors may overreact to the market volatility. Overall, these figures show that investor sentiment in online platforms has a link with the asset price movement. In order to detect the relationship more specifically, we use time series analysis and other econometric methods to conduct further research.

Figure 1: The variation of the SHINDEX and SENINDEX series.

Figure 2: The variation of the RSH and VSEN series.

Figure 3: The variation of the GARCHRSH and GARCHVSEN series.

3. Empirical Study and Result Analysis

3.1. Cointegration and Granger Causality Test

Table 1 shows the unit root test of residuals for SHINDEX-SENINDEX, RSH-VSEN, and GARCHRSH-GARCHVSEN, all the residuals follow a stationary process, and the cointegration relationship between investor sentiment and the asset price movement is significant.

Table 1: ADF test results.

ADF
Critical value
value

1%
5%
10%

SHINDEX-SENINDEX
−6.8038
−3.4626
−2.8756
−2.5743
0.0000

RSH-VSEN
−5.7672
−3.4612
−2.8749
−2.5740
0.0000

GARCHRSH-GARCHVSEN
−7.5798
−3.4609
−2.8749
−2.5740
0.0000

Note: 1%, 5%, and 10% are significance levels.

Then, we use the Granger causality test to further test whether there is a causality relationship between each pair of variables and present the results in Table 2. We can see that investor sentiment (SENINDEX) has a significant impact on the asset price index (SHINDEX), and SENINDEX is the Granger cause of the variation of the asset price movement. But analyzing the matchups of RSH-VSEN and GARCHRSH-GARCHVSEN, we find that the volatility of investor sentiment is not the Granger cause of the asset volatility. These findings are not consistent with the result of cointegration test. Considering the dramatic change in the time series of different matchups shown in Figure 1, we assume that the contradictory results may be caused by the structural change in the time series data. Thus we conduct a structural change point test in Section 3.2. Besides, different time frequencies of the samples can also affect the empirical results. Owing to the fact that the high frequency data are not publicly provided by the website Baidu, we examine the relationship between investor sentiment and the asset price movement under different time scales by using the empirical mode decomposition method to obtain the investor sentiment data with different time frequencies in Section 3.3.

Table 2: Granger causality test results.

Null hypothesis
F-stat.
Prob.

SHINDEX-SENINDEX

10.3966
0.0000

6.7410
0.0014

RSH-VSEN

1.52490
0.2201

8.48564
0.0003

GARCHRSH-GARCHVSEN

0.59415
0.5530

1.94952
0.1449

Note: there are 213 observations for each sample. “” denotes that is not the Granger cause of .

3.2. The Impact Examination by Using the Structural Break Point Test

We employ multiple structural change models provided by Bai and Perron [18, 19] to detect the structural break points and further examine whether the structural break dates of investor sentiment lead to changes in asset price. The advantage of this method is that it can simultaneously identify the multiple structural break points. Under the framework of this test method, the data process is as follows:

where is the dependent variable, representing the original data of investor sentiment and asset price, is a lagged , and are the coefficients, and is the residuals. The number of the structural break points is , and the locations of the break points are (. Equation (3) can be expressed as

For each possible segment (, we can get the coefficient and residual estimation by using the method of least square:

By comparing the residual square of different segments, we can get the optimal segment with minimal residual square. The corresponding break dates are

Applying the method of Bai and Perron [18, 19], we test the break points of investor sentiment and the asset price series and present the test results in Table 3. Break dates of each series reported in Table 3 are associated with important events in financial markets.

Table 3: Test results of structural break points.

Variables
Break test
F-stat.
Scaled F-stat.
Critical value
Break date

SHINDEX
0 versus
207.71
207.71
8.58
10/10/2014

1 versus
96.26
96.26
10.13
09/02/2011

2 versus
14.64
14.641
11.14
06/29/2012

3 versus 4
1.87
1.87
11.83
/

SENINDEX
0 versus
254.29
254.29
8.58
10/09/2014

1 versus 2
0.19
0.19
10.13
/

RSH
0 versus
12.45
24.91
11.47
07/29/2014

1 versus 2
0.66
1.31
12.95
/

VSEN
0 versus 1
3.25
3.25
8.58
/

GARCHRSH
0 versus
226.32
226.32
8.58
10/10/2014

1 versus
12.580
12.58
10.13
08/26/2011

2 versus
19.17
19.17
11.14
02/14/2014

3 versus 4
2.23
2.23
11.83
/

GARCHSEN
0 versus 1
6.58
6.58
8.58
/

Note: denotes the significance level of 5%.

On October 26, 2011, Pilot Measures for Supervision and Administration of Refinancing Business was released by the China Securities Regulatory Commission (CSRC). Two days later, China Securities Finance Co., Ltd. (hereinafter “CSF”), a financial institution specialized in securities, was established with the approval of the State Council and CRSC. This event promoted the margin transactions in China’s stock market. As the reform was widely discussed by scholars, practitioners, and supervisors, the break dates of the stock market were prior to the reform event and, for the index of SINDEX and GARCHRSH, were on August 26, 2011, and September 2, 2011.

On June 8, 2012, the People’s Bank of China (PBC) cut the benchmark interest rate for deposits and loans, and after the interest rate adjustment, the SHINDEX series had a break point on June 29, 2012. Two years later, PBC lowered the base rate again on November 22, 2014, and around these event windows, there were existing structural points for both SHINDEX and RSH. On January 14, 2014, Chinese stock market reopened the market to initial public offerings after a freeze of one and a half years. The financing function of the stock market was recovered and around this event, conditional volatility of the stock market had a break point.

Later, on July 29, 2014, China’s stock market had a new round of sharp increases, and the bull market lasted until June 2015. The structural break point occurred around the date of July 29, 2014. On November 17, 2014, the Shanghai-Hong Kong Stock Connect was implemented in China’s stock market. This is a remarkable event that increased the openness of the Chinese stock market. Thus, the SHINDEX series structurally changed near this time window.

As shown in above results, we find that there is only one structural break point in the data of investor sentiment, and investor sentiment does not lead to the changes of the stock price movement. We consider that online investor sentiment is probably driven by the asset price movement. Investors maybe overreact to the changes of the market price movement, while the market price movement is less influenced by investor sentiment through judging the structural break points.

3.3. The Impact Examination by Using the Autoregressive Distributed Lag Model

According to the structural break points, we divide the original sample into subsamples (see Tables 4–6). During each subsample period, we construct an autoregressive distributed model to examine the lead-lag relationship between investor sentiment and the asset price movement. The basic model is defined as

Table 4: Autoregressive distributed lag model with the dependent variable of SHINDEX.

Variable
Coefficient
Std.
-statistic
value
Adj.

Panel A: subsample 1 (01/14/2011–09/02/2011)

c
241.9021
222.0302
1.0895
0.2846
0.854

0.9595
0.0729
13.1648
0.0000

−0.0069
0.0032
−2.1524
0.0395

Panel B: subsample 2 (09/09/2011–06/29/2012)

c
583.7682
272.4279
2.1428
0.0394
0.5208

0.7448
0.1236
6.0246
0.0000

0.0001
0.0033
0.0418
0.9669

0.0004
0.0031
0.1444
0.8860

Panel C: subsample 3 (07/06/2012–10/10/2014)

0.9124
0.0364
25.0576
0.0000
0.7645

0.0081
0.0038
2.1256
0.0425

Panel D: subsample 4 (10/17/2014–06/05/2015)

0.9537
0.0893
10.6845
0.0000
0.9672

Table 5: Autoregressive distributed lag model with the dependent variable of RSH.

Variable
Coefficient
Std.
-statistic
value
Adj.

Panel A: subsample 5 (01/14/2011–07/29/2014)

−0.0015
0.0018
−0.8235
0.4114
0.0150

−0.0419
0.0777
−0.5391
0.5906

−0.0103
0.0078
−1.3096
0.1921

−0.0100
0.0087
−1.1530
0.2505

−0.0019
0.0078
−0.2444
0.8072

Panel B: subsample 6 (08/05/2014–06/05/2015)

0.0169
0.0069
2.4546
0.0194
0.0084

0.0425
0.1818
0.2335
0.8168

0.0002
0.0195
0.0116
0.9908

0.0287
0.0197
1.4538
0.1552

0.0235
0.0189
1.2450
0.2217

Table 6: Autoregressive distributed lag model with the dependent variable of GARCHRSH.

Variable
Coefficient
Std.
-statistic
value
Adj.

Panel A: subsample 7 (01/14/2011–08/26/2011)

0.0002
0.0001
2.0082
0.0551
0.7548

0.7065
0.1392
5.0771
0.0000

0.0000
0.0002
0.1553
0.8778

−0.0002
0.0002
−0.9777
0.3372

0.0000
0.0002
0.1396
0.8900

Panel B: subsample 8 (09/02/2011–02/14/2014)

0.0001
0.0000
3.0601
0.0028
0.7072

0.8431
0.0514
16.3937
0.0000

0.0000
0.0001
−0.2758
0.7832

0.0000
0.0001
−0.2424
0.8089

0.0000
0.0001
0.0818
0.9350

Panel C: subsample 9 (02/21/2014–10/10/2014)

0.0001
0.0001
1.9930
0.0573
0.6295

0.7024
0.1333
5.2686
0.0000

0.0003
0.0005
0.5438
0.5914

0.0001
0.0003
0.1911
0.8500

0.0000
0.0002
0.1142
0.9100

Panel D: subsample 10 (10/17/2014–06/05/2014)

0.0002
0.0001
1.9263
0.0660
0.7878

0.8325
0.0890
9.3494
0.0000

0.0002
0.0003
0.6155
0.5440

−0.0005
0.0003
−1.5050
0.1454

0.0004
0.0003
1.3353
0.1943

In (7), the definition of each variable is interpreted in Section 3. By using the subsample data, we estimate the autoregressive distributed models. The results are reported in Tables 4–6.

According to the estimation results of autoregressive distributed lag models, the impact of online investor sentiment on the asset price movement is insignificant except for the subsamples 1 and 3. These results suggest that investor sentiment has more significant impact on the market during the calm period and the beginning period of the bear market, and the relationship between investor sentiment and market return will be weaken during the high-sentiment period. The results could be explained from the psychological and economic views. Firstly, the investors’ neural structure can be divided by different neural quantum when they react to the external stimulus. The unit of a neural quantum is likely to be formed when the stimulus is strong enough (see Stevens et al. [20]). It is easier to form a neural quantum in calm period than in overoptimistic period. Secondly, from the perspective of the market operation, the cycle of the stock market is usually inconsistent with that of the investor sentiment. When the stock market falls from the peak point, the investor sentiment is still in an optimistic condition. Both the cycles of the stock market and investor sentiment are likely to coincide in the calm period. Thus, this phenomenon may weaken the relationship between online sentiment and market return in the overoptimistic period and strengthen their relationship in the calm period. By using the weekly data, we also provide evidence that online investor sentiment is not the lead indicator for asset price during most of the sample period. Tang [21] finds that the influence of an unexpected disturbance lasts only about 30 hours on the Internet. Therefore, the short term effects of online investor sentiment could account for the above empirical results.

3.4. The Impact Examination by Using EMD and the Autoregressive Distributed Lag Model

Because the Baidu Search Index is constructed on a weekly basis, while online investor sentiment could change during a week, thus, it is difficult to capture the short term characteristics based on the original investor sentiment data. To detect the impact of online investor sentiment on the stock market from a shorter term view, we use the empirical mode decomposition (EMD) to obtain the low, medium, and high frequency components of online investor sentiment. EMD is a data-driven analysis method for both nonlinear and nonstationary data. Because it is intuitive, direct, posterior, and adaptive, EMD is widely used in different fields [22]. Some scholars apply the EMD method to get the stationary IMF (Intrinsic Mode Function) and analyze financial markets and find the different characteristics of financial markets by distinguishing the long, medium, and short ranges of the markets [22–25]. To obtain the reasonable instantaneous frequency, we follow two basic requirements for the IMF: (i) the number of extreme value or zero crossings should be equal to or differ at most by one; and (ii) at any point, on the envelope line defined by the local maxima and local minimal, the mean value equals zero. The second condition is necessary, so that the instantaneous frequency will not have the unwanted fluctuation. In our study, the IMF of investor sentiment could be nonstationary, and it can be both amplitude and frequency modulated. To realize EMD for investor sentiment, we design the procedure as follows.

(1) Find the local maximum value and minimum value of original time series data and link them by using the spine function; the envelope line ( and can be constructed.

(2) Calculating the mean value of the envelope line, we can obtain the new time series . Here, which is a time series that has eliminated the low frequency component is the envelope line formed by the local maximum and minimum values and . Theoretically, is the intrinsic mode function. But generally does not satisfy the above two conditions, and we should repeat the above several times to get the intrinsic mode function.

(3) Calculate the standard deviation (SD):

where SD is a critical value to determine whether the sift process should be stopped. A typical value for SD can be set between 0.2 and 0.3. If SD is less than critical value, the sifting process can be stopped, and is the first IMF () which is the highest frequency component of investor sentiment.

(4) Separate from the rest of data by

here, the residual of original investor sentiment still contains the sentiment information of lower frequency (longer period components). We repeat the same sifting process until the residual becomes so small that it is less than a predetermined value, or becomes a monotonic function,

The original investor sentiment can be expressed as

Finally, we acquire to represent different online investor sentiment. The asset price time series data are decomposed by following the same procedures, and the results are displayed in Figure 4.

(a) EMD of SHINDEX

(b) EMD of SENINDEX

(c) EMD of RSH

(d) EMD of VSEM

(e) EMD of GARCHRSH

(f) EMD of GARCHVSEM

Figure 4: EMD results of the original data. The -axis of the th subfigure (the last subfigure) in each panel stands for (the residuals).

represents the highest frequency of each time series, which can reflect online investor sentiment in the short term. By observing for different time series data, we find that online investor sentiment and the Shanghai Stock Index both change quickly in a very short time. For example, on June 29, 2015, the Shanghai Stock Index dropped by −3.34%, while, on the next trading day, the index dramatically rose by 5.53% and then investor sentiment also inversed on this day. By taking of each original variable as the short term component, we investigate the impact of online investor sentiment on the asset price movement. The autoregressive distributed lag model with a high frequency component is estimated and the results are displayed in Table 7.

Table 7: Estimation results of the autoregressive distributed lag model with high frequency components.

Independent variable
Coefficient
Std. error
-statistic
value
Adj.

Panel A: is the dependent variable

−0.2577
0.0678
−3.8003
0.0002
0.0771

−0.0006
0.0003
−1.8411
0.0670

Panel B: is the dependent variable

−0.4562
0.0618
−7.3789
0.0000
0.2239

−0.0121
0.0063
−1.9263
0.0554

Panel C: is the dependent variable

−0.0001
0.0000
−2.4204
0.0164
0.0429

0.0001
0.0000
2.1079
0.0362

From Table 7, we find that the lagged high frequency component of online investor sentiment is closely correlated with the stock price index, return, and the volatility of the stock price index. These results are partly inconsistent with the results in Section 3.2, and the dissimilar results could be affected by the different frequencies of the time series data. The increase in online investor sentiment leads to a decrease in asset price and return and an increase in the volatility; thus the rise of online investor sentiment often suggests a high possibility of market bubbles, so that a short term risk may burst. In Figure 5, when the price index increased to the highest level, the trading volume in the Shanghai stock exchanges, which can also represent investor sentiment, reached the historical highest point. The price index may fall after investor sentiment reached the highest level.

Figure 5: The Shanghai Stock Index and the daily trading volume. Note: in this figure, the full line is the Shanghai Stock Index, and the dotted line is the daily trading volume in the Shanghai stock market. The unit of trading volume is 100 billion Chinese Yuan. The left -axis denotes the point of the Shanghai Stock Index, and the right -axis denotes the daily trading volume of the Shanghai stock market.

Meanwhile, investors can be influenced by online news and opinions. During the period from April 2015 to June 2015, some news and articles released on online platforms strengthened the optimistic sentiment, and some investors believed that the 4000 points of the Shanghai stock market was the start of the bull market and there was no bubble in the stock market. Because investors are more likely to be influenced by the Internet, supervisors should pay more attention to online investor sentiment.

4. Conclusion

In the era of the Internet, investor sentiment can be expressed on online platforms. In this paper, we systematically analyze the impact of online investor sentiment on the asset price movement by using the Baidu Search Index and the Shanghai Stock Exchange Index from January 1, 2011, to June 1, 2015, as the sample. The empirical results show that there is a cointegration relationship between the weekly investor sentiment and the asset price movement, but the autoregressive distributed lag model with the independent variable of sentiment has poor ability to predict the index, return, and volatility of asset price, and the structural break points do not lead to changes in the asset price movement. By using the empirical mode decomposition, we find that the high frequency component of online investor sentiment can be used to predict the asset price movement. Additionally, higher online investor sentiment is associated with a high possibility of market risk. Our empirical results also have policy implications. Because online investor sentiment has significant impact on asset price movement, the supervisors should pay more attention to the Internet news, online forum, instant messages, and other online platforms. For maintaining the stability of financial markets, supervisors could enact the laws or regulations to punish the rumor contagion which may lead to investor panic or overoptimism. Meanwhile, the supervisors can collect web information based on the big data technology and construct the online investor sentiment index, especially the high frequency sentiment index to alert the risk of the stock market.

Competing Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant nos. 71373072 and 71501066, the Specialized Research Fund for the Doctoral Program of Higher Education under Grant no. 20130161110031, and the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant no. 71521061.

References

J. B. De Long, A. Shleifer, L. H. Summers, and R. J. Waldmann, “Noise trader risk in financial markets,” Journal of Political Economy, vol. 98, no. 4, pp. 703–738, 1990.

B. M. Barber and T. Odean, “All that glitters: the effect of attention and news on the buying behavior of individual and institutional investors,” Review of Financial Studies, vol. 21, no. 2, pp. 785–818, 2008.

G. Kling and L. Gao, “Chinese institutional investors' sentiment,” Journal of International Financial Markets, Institutions and Money, vol. 18, no. 4, pp. 374–387, 2008.

R. F. Stambaugh, J. Yu, and Y. Yuan, “The short of it: investor sentiment and anomalies,” Journal of Financial Economics, vol. 104, no. 2, pp. 288–302, 2012.

M. Baker, J. Wurgler, and Y. Yuan, “Global, local, and contagious investor sentiment,” Journal of Financial Economics, vol. 104, no. 2, pp. 272–287, 2012.

S. Lamia, “The impact of investor sentiment on the Tunisian stock market,” Journal of Business Studies Quarterly, vol. 5, no. 2, pp. 90–111, 2013.

U. Uygur and O. Taş, “The impacts of investor sentiment on different economic sectors: evidence from Istanbul stock exchange,” Borsa Istanbul Review, vol. 14, no. 4, pp. 236–241, 2014.

Z.-X. Ni, D.-Z. Wang, and W.-J. Xue, “Investor sentiment and its nonlinear effect on stock returns-new evidence from the Chinese stock market based on panel quantile regression model,” Economic Modelling, vol. 50, no. 4, pp. 266–274, 2015.

C.-Q. Luo, C. Xie, C. Yu, and Y. Xu, “Measuring financial market risk contagion using dynamic MRS-Copula models: the case of Chinese and other international stock markets,” Economic Modelling, vol. 51, no. 12, pp. 657–671, 2015.

J. Kumari and J. Mahakud, “Does investor sentiment predict the asset volatility? Evidence from emerging stock market India,” Journal of Behavioral and Experimental Finance, vol. 8, pp. 25–39, 2015.

P.-C. Wu, S.-Y. Liu, and C.-Y. Chen, “Re-examining risk premiums in the Fama-French model: the role of investor sentiment,” The North American Journal of Economics and Finance, vol. 36, no. 4, pp. 154–171, 2016.

A. Zaremba, “Investor sentiment, limits on arbitrage, and the performance of cross-country stock market anomalies,” Journal of Behavioral and Experimental Finance, vol. 9, no. 3, pp. 136–163, 2016.

K. Miwa, “Investor sentiment, stock mispricing, and long-term growth expectations,” Research in International Business and Finance, vol. 36, pp. 414–423, 2016.

J. Campbell and D. Cecez-Kecmanovic, “Communicative practices in an online financial forum during abnormal stock market behavior,” Information & Management, vol. 48, no. 1, pp. 37–52, 2011.

F. Takeda and T. Wakao, “Google search intensity and its relationship with returns and trading volume of Japanese stocks,” Pacific-Basin Finance Journal, vol. 27, no. 1, pp. 1–18, 2014.

H. Leung and T. Ton, “The impact of internet stock message boards on cross-sectional returns of small-capitalization stocks,” Journal of Banking & Finance, vol. 55, pp. 37–55, 2015.

A. Hamid and M. Heiden, “Forecasting volatility with empirical similarity and Google Trends,” Journal of Economic Behavior and Organization, vol. 117, pp. 62–81, 2015.

J. Bai and P. Perron, “Estimating and testing linear models with multiple structural changes,” Econometrica, vol. 66, no. 1, pp. 47–78, 1998.

J. Bai and P. Perron, “Critical values for multiple structural change tests,” The Econometrics Journal, vol. 6, no. 1, pp. 72–78, 2003.

S. S. Stevens, C. T. Morgan, and J. Volkmann, “Theory of the neural quantum in the discrimination of loudness and Pitch,” The American Journal of Psychology, vol. 54, no. 3, pp. 315–335, 1941.

C. Tang, “An empirical study on the evolution of web influence of unexpected mass disturbances,” Journal of Intelligence, vol. 31, no. 4, pp. 58–63, 2012 (Chinese).

M.-J. Xu, P.-J. Shang, and A.-J. Lin, “Cross-correlation analysis of stock markets using EMD and EEMD,” Physica A: Statistical Mechanics and Its Applications, vol. 442, pp. 82–90, 2016.

N.-E. Huang, M.-L. Wu, W. Qu, S.-R. Long, and S.-S. Shen, “Applications of Hilbert-Huang transform to non-stationary financial time series analysis,” Applied Stochastic Models in Business and Industry, vol. 19, no. 3, pp. 245–268, 2003.

X.-Y. Qian, G.-F. Gu, and W.-X. Zhou, “Modified detrended fluctuation analysis based on empirical mode decomposition for the characterization of anti-persistent processes,” Physica A: Statistical Mechanics and Its Applications, vol. 390, no. 23-24, pp. 4388–4395, 2011.

A. Lin, P. Shang, G. Feng, and B. Zhong, “Application of empirical mode decomposition combined with K-nearest neighbors approach in financial time series forecasting,” Fluctuation and Noise Letters, vol. 11, no. 2, Article ID 1250018, 2012.