How mood affects the stock market: Empirical evidence from microblogs

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How mood affects the stock market: Empirical evidence from microblogs ⁎

Yuan Suna,b, Xuan Liua, , Guangyue Chenc, Yunhong Haoa, Zuopeng (Justin) Zhangd a School of Business Administration, Zhejiang Gongshang University, No.18, Xuezheng St., Zhejiang Gongshang University, Xiasha University Town, Hangzhou, Zhejiang, People’s Republic of China b Zheshang Research Institute of Zhejiang Gongshang University, 149 JiaoGong road, Hangzhou, Zhejiang, People’s Republic of China c School of Management, Fudan University, No. 220 Handan Road, Yangpu District, Shanghai, People’s Republic of China d Coggin College of Business, University of North Florida, Jacksonville, FL, 32224, USA

A R T I C LE I N FO

A B S T R A C T

Keywords: Text mining Microblog Sentiment analysis Stock return

Examining 22,504 tweets extracted from Sina Weibo, a microblog site, we identify two clusters of microblog users and study how they influence the stock market. Our research contributes the following significant findings to the existing literature. First, we discover that there exists an inverse U-shaped curve between stock return and the attention of both news media and investors. Second, we verify that news media attention plays a positive moderating effect in the relationship between investor attention and the stock return. Finally, we find that social interaction could positively moderate the effect of news media’s and investor’s sentiments on stock return.

1. Introduction A capital market is efficient when stock prices fully reflect all available information circulated in the market. In financial markets characterized by imperfect information, social media can be used as a major instrument to disseminate information to the public. Currently, many companies are taking advantage of social media to improve information transparency and build up their brand image [1]. As one of the major forms of social media in China, microblogs have been increasingly used by people to express and exchange their opinions and feelings. Microblog data can be a useful and timely source for users and organizations to extract valuable information about sentiment and opinions on various topics [2]. For instance, a microblog article entitled “Wanda Real Estate Debt Was Miserably Battered Today” posted at 10:10 a.m. on June 22, 2017, was quickly spread on the Internet. At 10:18 a.m., the microblog account “Mr. Visiting Shan” maliciously added the words “political risk” to the title of the article. At 10:30 a.m., Wanda’s stock price began to plummet. This example poses some interesting questions. For instance, will the information disseminated on social media always affect the emotional and psychological responses of social media users, thereby affecting their financial decisions? To answer such question, sentiment analysis can be an effective approach to investigating microblog feeds to derive insights and provide new perspectives. A similar approach has been applied in a recent study on the Chinese financial market [3].

Recent years have witnessed an increasing number of studies on social media that employ methodologies such as opinion mining to extract and process unstructured data from social media [4–7]. Scholars have begun to explore the relationship between social media and the stock market [8], focusing on two main issues: effects of attention and sharing of moods. Studies concentrating on the effects of attention assume that people usually have limited information-processing capacity and, consequently, make decisions under time pressure [9]. According to the attention theory, the speed of information diffusion is associated with the level of attention. Rapid information diffusion allows people to receive relevant information immediately and take instantaneous trading actions [10]. Within this framework, recent studies have shifted their focuses on media attention [11,12] and investor attention [13,10]. With regard to media attention, the news media plays a vital role in the process of determining which issues will receive low or high levels of public attention. Investor attention could also affect stock market return [14–16]. Social media is used by people not only to share their opinions but also to convey their moods. From a behavioral finance perspective, researchers have shown that the stock market can be driven by the emotions of market participants. Moreover, some sentiment measures, such as investor and consumer surveys, have been proposed to forecast share returns [17–20]. Although prior studies have recognized the impact of social media

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Corresponding author. E-mail addresses: [email protected], [email protected] (Y. Sun), [email protected] (X. Liu), [email protected], [email protected] (G. Chen), [email protected] (Y. Hao), [email protected] (Z.J. Zhang). https://doi.org/10.1016/j.im.2019.103181 Received 28 August 2018; Received in revised form 21 July 2019; Accepted 26 July 2019 0378-7206/ © 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Yuan Sun, et al., Information & Management, https://doi.org/10.1016/j.im.2019.103181

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volume [29], advertise expenditure [30,31], and media reports [32]. However, stock return can be influenced by other factors that are not directly related to investor attention. For instance, a news article in the Wall Street Journal does not guarantee that investors will pay attention to it unless they actually read it [33]. Da, Engelberg and Gao [33] proposed a new direct measure of investor attention using aggregate search frequency in Google. They consider the search behavior as a revealed attention measure, for instance, if you search for a stock in Google, you are undoubtedly paying attention to it. However, we consider that users who search for stocks are not necessarily investors because there may exist many other types of users. For example, some people may just search for a stock to finish a report or find a figure, so they may not pay attention to the stock like investors. Therefore, using the search frequency as a measure of attention is not rigorous enough. In this paper, we propose a different measurement to overcome the limitation. Specifically, we classify the potential investor groups from a large number of Weibo users. We believe that the comments or investment strategies about particular stocks published by investors on Weibo indicate that they have paid attention to the stocks. On the basis of this rationale, we measure an investor’s attention by the number of microblogs that he(she) has posted. Similarly, we use the number of reports on a stock published by the news media as a measure of media attention.

on the stock market and explored their relationships from some perspectives, they are still subject to the following limitations. First, in most studies, each microblog message is weighed equally. However, certain microblog users (e.g., government news, news media, and financial analysts) may have more influence on the stock market than regular microblog users [8]. Second, although prior research has emphasized the influence of investor attention [14,15] as well as media attention [21–23] on stock returns, very few studies have conceptually and empirically studied the joint role of these two types of attention and their combined effect on stock returns. Third, prior research mostly assumes the neutrality of news media. However, in reality, news media cannot be absolutely objective because it is inevitable for journalists of the news media to attach subjective opinions to their reports [22]. Our paper attempts to fill the research gap by addressing these limitations. Specifically, we explore the relationship between microblog mood and the stock market. First, we classify all the microblog accounts into several clusters and select the following two as our research object: individual investors and the news media. Second, we develop an empirical model and formulate corresponding hypotheses to investigate the moderating effects of the two types of attention. Examining the model and hypotheses, we investigate how news media coverage influences investors’ attention, affecting their trade decisions. Third, in our sentiment analysis, we incorporate the influence of sentiment from news media reports on the stock market. Finally, we study how emotional contagion contributes to the interactions among microblog users, ultimately influencing the stock market. People tend to transfer their emotion as a result of facial, vocal, and postural feedback; such phenomenon is recognized as emotional contagion in social science [24], which enables people to experience the same emotion without being aware of the transfer [42]. Social media such as Twitter and Weibo typically supports the following three types of user interactions: repost, praise, and comment [25], with which mood levels can be easily influenced by other people. Focusing on this feature, we study how the interactions among microblog users facilitated by emotional contagion strengthen the effects of sentiment of investors and news media on the stock market. Our research makes the following three major contributions to the existing literature. First, our paper is the first to validate how different types of microblogging users affect the stock market based on the classification of their various identities from the perspective of the stakeholder theory. Second, our study examines the role of social media in the Chinese financial market using the two dimensions of attention and sentiment. Third, our paper contributes to the emotional contagion literature by exploring the interactions among microblog users, characterized by the unique features of microblogs. The rest of this paper is organized as follows. The next section provides the theoretical background of the study. Section 3 proposes the research hypotheses. Section 4 introduces the methodology, including model specification and data collection, processing, and measurement. Section 5 presents our empirical results. Section 6 summarizes our findings and implications and highlights the directions for future research.

2.2. Emotion and financial market The foundation of traditional finance is associated with the modern portfolio theory [34] and the Efficient Market Hypothesis [35]. The Efficient Market Hypothesis argues that the price of any asset must immediately incorporate all available information and can be regarded as an optimal estimate of true investment value at all times [36]. Behavioral finance is the psychological study of market participants and their interactions with the financial markets, where market participants may be either individual households or organizations [37–39]. According to the behavioral finance theory, researchers have shown that the emotions of market participants will have a strong influence on the stock market. The role that investor sentiment plays in asset pricing has shown its great importance in financial economics. Various investor sentiment proxies have been proposed to investigate the contemporaneous correlations between investor sentiment and marketwide variables [40]. In numerous recent studies, mood levels have been used as a proxy for investor sentiment [41–43,40]. According to these findings, mood states can spread among Internet users through textbased communication. In our study, we develop a sentiment index to capture the sentiment of investors and news media. Our goal is to categorize a tweet into a three-dimensional daily time series of mood levels, namely, positive, neutral, or negative. Based on Antweiler and Frank [44], we establish a daily mood index for both investors and news media. 2.3. Emotional contagion and social interaction Emotional contagion has been defined as the phenomenon that “when people are in a certain mood, whether elation or depression, that mood is often communicated to others. When we are talking to someone who is depressed it may make us feel depressed, whereas if we talk to someone who is feeling self-confident and buoyant we are likely to feel good about ourselves” [45]. Emotional states can be transferred to others through emotional contagion, leading people to experience the same emotion without being aware of the transfer [42]. As a result of emotional contagion, selection and influence processes are proposed to explain the following phenomenon [46]: people befriend others who are similar to them [47] or they become more similar to their friends over time. Therefore, to some extent, emotional contagion can enhance social interaction. Social interaction refers to as the interdependence of individuals,

2. Literature Review 2.1. Attention and the stock market The Efficient Market Hypothesis assumes that investors are rational and the market is effective, which implies that share prices reflect all information [26]. This assumption requires that investors allocate enough attention to their assets. However, investors have limited attention, as it is a scarce resource [27]. Therefore, people make most of their decisions under time pressure, which will result in biased investment decisions [9]. Attention measurement is a difficult task. The traditional proxy variables of investor attention usually include price limit [28], trading 2

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and stock returns.

under which the preferences, beliefs, and budget constraints of a person engaging in both social and economic behavior are directly influenced by the characteristics and choices of other people [48]. Xia, Lei, Jiliang and Huan [2] showed that social interaction can help sentiment analysis by proposing a sociological approach to handle noisy and short texts for sentiment classification. Kramer, Guillory and Hancock [42] conducted an experiment with people who used Facebook, and the results showed that emotion expressed by others on Facebook affected our own mood, producing experimental evidence for massive-scale contagion through social interaction. Manski [49] divided social interaction into endogenous interaction and situational interaction. Hong, Kubik and Stein [50] studied how individuals obtained information through social interaction to influence individual ownership behavior decisions. Different from traditional media texts, microblog texts are noisy, short, and embedded with social relations [2]. Sina Weibo provides three types of user interactions (repost, praise, and comment) that allow users to form threaded conversations around a single message [25]. Thus, microblog users’ mood levels can be easily influenced by other people. In this paper, we consider the social interactions of Weibo users when exploring the relationship between mood levels and stock returns.

H1b. There is an inverse U-shaped curve between investor attention and stock returns. As mentioned above, increased media attention has a direct influence on stock returns because the dissemination of news media information will reduce information asymmetry among the public [58]. At the same time, the direct impact of investor attention on stock returns is a result of “over-attention underperformance” [57]. Firms frequently publish mandatory news announcements, and the business press reports firm-specific news and events through the news media [59]. When a firm is recognized by investors from a mass media report, the reactions of different types of investors to that media coverage will be reflected in both trading decisions and stock prices [60]. However, much more attention should be paid to whether and how attention mechanisms moderate with each other in response to the stock market. There is likely to be a mechanism in which media coverage changes investors’ trade decisions by affecting their attention. Zhang, Wan and Fu [22] used a security market experiment to measure investor attention and found that the amount of media coverage was positively related to investor attention, confirming that media reports influenced investor behavior as an attention allocation mechanism. Thus, we develop another hypothesis as follows：

3. Hypotheses

H1c. News media attention plays a positive moderating role in the relationship between investor attention and the stock return.

3.1. Relationship between attention and stock return An increasing amount of research has focused on the media’s effect on the stock market because the news media provides constant information to the public. The media effect is referred to as a phenomenon that stocks with no or low mass media coverage earn higher returns than those with high coverage, even after controlling for wellknown risk factors [11]. Given the influence of the news media, the empirical conclusion in most of the literature is that media coverage of listed companies will promote either people’s preference for stock investment or increased stock prices. For instance, Chen, Goldstein and Jiang [51] showed that media attention on the capital market was mainly reflected in stock prices. Investor attention is the premise and objective condition of market reaction. However, limitations of time and professional skills make investor attention a scarce resource [52]. Dellavigna and Pollet [14] compared investors’ response to earnings announcements on Friday when their inattention was more likely, with the response on other weekdays. Their results supported the existence of post-earnings announcement drift based on an under-reaction to information caused by limited attention. Aouadi, Arouri and Teulon [53] found that Google search volume was a reliable proxy for investor attention. Interestingly, they showed that investor attention was strongly correlated to trading volume and was a significant determinant of stock market illiquidity and volatility. Thus, social media works as an attention-allocation mechanism in the modern world, in which social media can attract the maximum attention from investors. Current research shows that stocks with media coverage or investor attention will lead to attention-driven buying [54] and overtrading. “Attention-driven buying behavior” means that the attention of market participants is easily affected by the media, thus forming “excessive attention” [55], so market participants will buy more attention-grabbing stocks. This excessive attention will cause an overreaction to new information and an overestimation of stock value in the short term [56], and a long-term reversal of subsequent earnings, which is called “over-attention underperformance” [57]. This paper attempts to examine whether the impact of attention on stock market returns gradually disappears after concentration increases to a certain degree, which is consistent with the “over-attention underperformance hypothesis.” Therefore, the following hypotheses are established:

3.2. Relationship between sentiment and stock returns An emotion is a kind of feeling directing one toward anything intuitively appraised as good or away from anything intuitively appraised as bad. Elster [61] defined emotion as a physiological state of arousal triggered by a belief. A large body of literature shows that a person’s emotional state may influence his(her) financial decision making [62]. Lo and Repin [63] studied the physiological characteristics of professional stock traders and found that emotion can affect a trader’s ability to survive in the financial market. If an individual is in a good mood because of recent life experience, it will help him/her take a positive outlook to the task at his/her hand. The evidence also indicated that good moods resulting from morning sunshine led to higher stock returns [64]. Tetlock [65] used daily content from the Wall Street Journal and found that high media pessimism predicted downward pressure on market prices followed by a reversion to fundamentals, and unusually high or low pessimism predicted high market trading volume. Wu and Lin [59] conducted a textual analysis of news content and found that positive news generated positive abnormal returns and negative news caused negative abnormal returns. Walker [66] found that there was a significant relationship between the total number of housing market articles published in the Financial Times and the return premium of companies involved in the housing market. Thus, it is reasonable to assume that public sentiment can affect stock market values. Different sentiment measures have been proposed to forecast share returns, such as investor and consumer surveys [17–19], trading volume, or market volatility [67]. In this study, we use mood levels as a proxy for investor sentiment [68,43,69]. Categorizing the emotional color of a tweet into a three-dimensional daily time series of mood levels, including positive, neutral, or negative, we develop a daily mood index for both investors and the news media based on Antweiler and Frank [44] The formula for the daily mood index is given in Section 4.1. Hence, we establish the following two hypotheses: H2a. An increased daily mood index in the news media will have a positive effect on stock returns. H2b. An increased daily mood index among investors will have a positive effect on stock returns.

H1a. There is an inverse U-shaped curve between news media attention 3

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sentiment on stock returns. However, they have not investigated the difference between positive and negative sentiment, which play totally different roles with regard to their influences on human behavior [76]. Based on the study of Antweiler and Frank [44] we develop a daily mood index for both investors and the news media. Specifically, we denote Mt positive as the number of tweets related to “positive” mood and Mt negative as that related to “negative” mood. Therefore, the DMI (daily mood index) is calculated as follows:

3.3. Moderating role of social interaction Emotional states can be transferred to other people through emotional contagion, resulting in they experiencing the same emotion without knowing the transfer [42]. Studies have found evidence for social interaction based on emotional contagion. For instance, Neumann and Strack [70] showed that mood contagion can be conceived of as a mechanism with which feelings are transferred between persons who listen to each other. Emotional contagion can enhance social interaction. However, social interaction has also been considered as a critical factor of social media information and a potentially important tool for mobilizing people. Wang, Wang, Li, Zheng and Zhao [71] found that users of the Sina microblog could comment on or reply to any publicly released tweet. Even if users did not pay attention to the publisher, user interaction behavior would not be affected, and a stronger social connection would still be established. In China, Sina Weibo provides users with the platforms to interact with friends and followers. There are three types of user interactions in Sina Weibo: repost, praise, and comment [22]. A user can post a new tweet, mention another user in a tweet, or comment on an existing tweet. A Sina Weibo user can comment on or reply freely to any published microblog, which means that Weibo supports a specific kind of social interaction [25]. Wang, Chen, Wang, Wang, Yang, Li, Zheng and Zhao [25] found that users who made comments on a tweet were often friends with the original tweeter, and they usually built a strong social link. In this paper, we also focus on the social interactions of Weibo users when exploring the relationship between mood levels and stock returns. In particular, we extend previous research of Nofer and Hinz [43] by including the total number of reposts, comments, and praises per tweet in the analysis. Therefore, the following hypotheses are proposed:

DMI = Ln [(1 + Mt positive )/(1 + Mt negative )].

(5)

A positive value of the DMI indicates that an overall positive signal is delivered on that day. Moreover, the larger the value, the more positive the sentiment is delivered by the tweets. In contrast, a negative value of the DMI implies that an overall negative signal is delivered on that day. The intraday stock return is calculated using the following formula:

RETURNit = Ln (PRICEit ) − Ln (PRICEi, t − 1)

(6)

where PRICEit is the closing price of a stock i on a given day t and PRICEit − 1 is the closing price on the given day before (t − 1) . In addition, we add the number of forwarding, comments, and likes for each tweet on each day and then multiply it by the daily mood index. The result is defined as social interaction [77]. Finally, we control for numerous variables. First, we consider the stock market factors, such as trade volume and currency value; after taking the logarithm, we use the data obtained from the CSMAR (China Stock Market and Accounting Research) database. Second, by considering company features, we control for leverage, earnings per share, the book-to-market value ratio, and Tobin’s Q [21]. Table 1 summarizes the descriptions of our variables.

H3a. Social interaction plays a positive moderating role in the relationship between the news media mood index and the stock return.

4.2. Model specification

H3b. Social interaction plays a positive moderating role in the relationship between the investor mood index and the stock return.

The purpose of our study is to investigate the relationship between stock return and investor attention. We use the number of tweets published by investors and news media as a proxy for attention. Thus, we develop the following equations to test hypotheses H1a, H1b, and H1c:

4. Methodology and data This section introduces our research model and methods for data collection, processing, and measurement. Beginning with a summary of our attention and mood measurement, we develop different empirical models to determine whether microblog attention or mood will affect stock returns. Additionally, we demonstrate our data-gathering process by explaining how we collect historical capital data and why microblogs are chosen over other Chinese social media. Using different combinations of data as the input, we show how the methods of identity classification and sentiment analysis were employed to extract public mood states from microblog feeds.

RETURNit = αi + β1 ATTENTION + β2 ATTENTION 2 +β3 CURRENCYVAL + β4 TRAVOL + β5 BOOKTMARKET + β6 RETURNTOTA + β7 LEVERAGE + β8 RETURNTONA + β9 TOBINQ + β10 EARNINGS + εit ,

(1)

Table 1 Variables definition. Variables

4.1. Measurement

Controls TRAVOL CURRENCYVAL BOOKTMARKET RETURNOTA LEVERAGE RETURNONA EARNINGS TOBINQ

Prior research has proposed different theories of attention, but it remains difficult to directly measure attention. Different kinds of indirect proxies have been used in recent studies to measure investor attention, such as advertising expense [72–74], media coverage [55,11], extreme returns [32], trading volume [32], and the Baidu Index [21,75]. In this paper, we develop a new measurement of attention based on the rationale that the comments or investment strategies about particular stocks published by investors on Weibo indicate that they have paid attention to the stocks. Based on this reasoning, we measure an investor’s attention by the number of microblogs that he (she) has posted. Similarly, we use the number of reports on a stock published by the news media to measure media attention. Recent studies have analyzed the effect of microblog participants’

Study Variables RETURN ATTENTION DMI INTERACTION

4

Definition

The daily trade volume per stock The daily stock currency value per stock Book to market ratio monthly per stock Return on total assets monthly per stock Total liabilities to total assets monthly per stock Return on net assets monthly per stock Earnings per share monthly per stock The ratio between a physical asset's market value and its replacement value monthly per stock The daily return per stock The number of tweets released by each investor or media each day The sentiment delivered by per tweet, see above formula The sum of forwarding, likes, and comments per tweet

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large amounts of capital and significant proportions of the workforce. Realtors hold the view that information about properties belongs only to their firm or its network [80]. However, this poses a problem owing to the lack of information transparency. Social media has played a dominant role in the information society. In China, there are many social media platforms for people to express their opinions, such as Sina Weibo, Wechat, Renren (which is more like Facebook), Stock BBS, and so on. Microblogs have advantages over other social media platforms. First, they attract attention from both adults and young people. Second, many financial reviewers, well-known economists, and stock market commentators have microblog accounts [81]. Therefore, we collected data in the following ways. Web data. To extract data from Weibo, we designed and customized the method of website crawling based on an existing data crawling approach. In this study, we used “Octopus Collector”1 software to capture massive Weibo data. A microblog contains 7 parts: “microblog_content,” “microblog_author,” “author_id,” “microblog_time,” “comment_count,” “repost_count,” and “praise_count,” all of which are needed for our research. The experiments verified the efficiency of our web crawling method and the accuracy of our page classifications. Overall, we extracted 22,504 tweets from Sina Weibo from October 2015 to December 2015. Stock data. For these firms, we obtained financial data from the CSMAR database for the period from October 2015 to December 2015, which allowed us to empirically investigate market behavior for those three months. We deleted stocks with invalid samples that do not truly represent the name of the business, stocks about which fewer than 5 people tweeted during the three months, and stocks for which no company-related tweets were published by either investors or the news media. In our time span, there was some missing data because of the stock market closing on a national holiday or other specific reasons. Thus, we applied imputation methods for missing value. If the missing data were for one day, then we used the following formula [82]:

RETURNit = αi + β1 ATTENTION + β2 NEWS MEDIA × INVESTOR + β3 CURRENCYVAL + β4 TRAVOL + β5 BOOKTMARKET + β6 RETURNTOTA + β7 LEVERAGE + β8 RETURNTONA + β9 TOBINQ + β10 EARNINGS + εit

(2)

Different sentiment measures have been proposed to forecast share returns, such as investor and consumer surveys [17,18,7], trading volume, and market volatility [67]. Using mood levels as a proxy for investor sentiment, we study the relationship between sentiment and stock return with the following regression model to verify hypotheses H2a and H2b:

RETURNit = αi + β1 DMI + β2 CURRENCYVAL + β3 TRAVOL + β4 BOOKTMARKET + β5 RETURNTOTALA + β6 LEVERAGE + β7 RETURNTOTALA + β8 TOBINQ + β9 EARNINGS + εit

(3)

As we have explained in Section 3, interactions with others have a strong influence on people, leading to a shared emotion or social mood. Collectively shared opinions and beliefs shape individuals’ decisions and aggregate them into social trends, fashion, and actions [78]. Sina Weibo provides users with platforms to interact with friends and followers, so users’ mood levels can be easily influenced by other people. Therefore, we further our research by considering the moderating effect of social interaction. In particular, we calculate social interaction by totaling the number of forwards, comments, and likes for each tweet on a daily basis and then multiplying it with the daily mood index. Taking social interaction as a moderating variable, we establish the following model to verify hypotheses H3a and H3b:

RETURNi = αi + β1 DMI + β2 DMI × INTERACTION + β3 CURRENCYVAL + β4 TRAVOL + β5 BOOKTMARKET + β6 RETURNTOTA + β7 LEVERA + β8 RETURNTONA + β9 TOBI NQ + β10 EARNINGS + εit

X = 1/2(x t − x t − 1),

(4)

(7)

where x represents all valid variables and t represents time series. If the data was missing for several consecutive days, then the above formula was unsuitable. Based on Zhou, Zhang, Li and Yang [82] the following method was applied to make up the balance between the fixed time interval of the time series data and the panel data:

4.3. Sample and data Since Twitter was blocked in China in 2009, several Chinese media companies launching similar services have rapidly gained popularity. Sina Weibo, launched on August 14, 2009, is the largest microblog platform in China with more than 100 million messages posted each day. By the third quarter of 2015, Sina Weibo had 222 million subscribers and 100 million daily users. Sina Weibo enables users to post short tweets or messages that are displayed on a user’s Weibo page. As each character in Chinese represents a whole word, the content of Chinese tweets is richer than what can be communicated through 140 characters in English. In addition to posting short messages and sharing pictures, Weibo users can upload videos, play games, and communicate through private instant messaging [79]. Sina Weibo provides a platform for us to investigate the relationship between user groups with different identities based on users’ attention, mood levels, and social interaction from a stakeholder’s perspective. Moreover, with Sina Weibo’s advanced search capability, we can customize our data by searching for a more specific expression in combination with topic keywords, date, types of document, and source of document. For instance, we can input our keywords (the real estate company’s short name) and define the time span. In a few seconds, the web will display all the required data.

X = Random*(x a − xb) + xb

(8)

We used the random method to return a random number between 0 and 1, where xb is the last value and x a is the first value between the time intervals. If there was missing data for three months, we directly removed the sample stock. 4.3.2. Identity classification A microblog account can be considered either subjective or objective. Subjective microblog accounts normally belong to individual users with different backgrounds, jobs, and genders, whereas objective microblog accounts mainly include the news microblog media, such as “Sina Finance,” “Sina sports,” “Beijing News,” and so on [81]. Our research weighs microblog opinions based on users’ influence on stock returns. In particular, we focus on two groups of users (individual investors and news media) from seven categories because these two groups account for the largest proportion of our original sample and the 1 Octopus Collector is invented by Shenzhen vision information technology co. LTD (Shenzhen, China). It is based on a distributed cloud computing platform that can obtain data in a very short period of time from various websites. It can handle extremely large-scale web crawling tasks and achieve automatic data acquisition, editing, and standardization, enabling us to get rid of dependence on manual data collection, thereby reducing the cost of access to information and improving the efficiency of data collection.

4.3.1. Data collection In many parts of the developed world, the real estate industry has become a symbol of wealth creation that can foster the local economy. Real estate companies are important sectors of the economy that invest 5

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users from these two groups have a great impact on stock price. We find that “individual investors” tend to pay close attention to the stock market with respect to the following features, such as stock price movement, stock recommendation, and earning announcement. “News media” was easy to be categorized based on their Weibo titles, for instance, “Sina Real Estate,” “Live News,” “24 h News,” and “Guiyang Leju.” At the beginning, we applied an exploration-based method to collect users’ data from Weibo. The heuristic rule evaluated if a user had published messages with keywords containing a company’s abbreviation such as “Wanke,” “Greenland Group,” and “Poly Real Estate.” After obtaining the tweets, we performed propagation to obtain more users’ data. Two research assistants then helped to determine whether the collected users could fall into one of the two groups. If there were inconsistent results about classification, the assistants would discuss the results to reach a final conclusion. Overall, we collected 8099 tweets for “individual investors” and 2313 tweets for “news media.”

Chi uses the TF-IDF method to select certain words as polar words for training models. Tencent NLP has excavated more than 0.12 million polarity words, and through artificial verification of more than 0.8 million of those words, approximately 100 polar words are excavated from the corpus each day. 4.3.3.3. Polar words judgment. The task of polarity judgment is to determine the positive, negative, or neutral polarity of the corpus, which is a complex three-classification problem. To simplify the problem, Tencent NLP first makes a subjective-or-objective judgment on the corpus; the objective corpus is a neutral one, and the subjective corpus is then either positive or negative. For the purpose of classifier selection, Tencent NLP adopts the Support Vector Machine (SVM) model3 for subjective and objective judgments. In terms of the polar judgment, both the Naive Bayesian Model4 and the Support Vector Machine model are used. 5. Empirical analyses

4.3.3. Sentiment analysis With the collected users’ tweets, we conducted a sentiment analysis for the two types of users. Substantial research exists in the field of emotion analysis, among which many systems have been implemented to automatically detect sentiment in texts [83,84]. The major solutions to emotion analysis include the emotional knowledge-based method and the feature-based classification method. The emotional knowledgebased method aims to create the emotion dictionary or field emotion lexicon and then determine the polarity of text using a combination of emotional words [85,83,86]. The method based on feature classification primarily uses machine learning to conduct feature extraction and make judgments [83]. In this paper, we used the API (Application Programming Interface) provided by the Chinese Tencent NLP platform2 to conduct microblog content text processing and sentiment analysis. Our goal was to classify a tweet as being either “positive,” “negative,” or “neutral” by applying the sentiment classification to all the Weibo messages in the two clusters. Detailed principles of emotion analysis are divided into the following three parts.

Having described our sample data collection and preprocessing processes, we next shift our focus to the empirical analysis to test our hypotheses. In this section, we first summarize the descriptive statistics to identify the basic features of variables, report the correlations between various variables, and then test the validity of our hypothesis based on proposed empirical models. Finally, we conduct a further robustness check to ensure the reliability of the research results. 5.1. Descriptive statistics We report the descriptive statistics of key variables, including the mean, observations, and standard deviation for our final sample (Appendix A, Table A1). After deleting invalid samples, we obtain 1371 observations of investors and 712 observations of news media. The mean of DMI is positive for both investors and news media, which indicates that positive sentiment predominates. The maximum and mean of attention for investors are larger than those for news media. To avoid the multicollinearity problem among variables, we analyze the explained variables and the explanatory variables using the variance inflation factor (VIF) [87]. VIF quantifies the severity of multicollinearity in an ordinary least squares (OLS) regression analysis. It measures how much the variance (the square of the estimate's standard deviation) of an estimated regression coefficient will change because of collinearity. The larger the VIF value, the higher degree is the collinearity. The empirical judgment method shows that when 0 < VIF < 10, there is no multiple collinearity. Our results demonstrate that the maximum VIF is 8.52, which is less than 10, implying that our variables do not have a serious multicollinearity problem. Moreover, we provide the correlation analysis of all variables, which is shown in Appendix A, Table A2. As shown in the table, for either investors or the news media, there is a positive correlation between attention and stock return. In addition, the correlation also becomes positive between Daily Mood Index and return. Fig. 1 demonstrates that individual investors tend to be more sensible. In particular, they exhibit more positive sentiments than negative moods. This finding is compatible with those reported in previous studies that extract sentiment from Internet messages. For instance, Rao

4.3.3.1. Construction of corpus. To date, Tencent NLP has been collecting texts from more than 20 sites, including e-commerce, news, film, music, APP, and other categories, with a cumulative number of 400 million tagged materials and approximately 2,000,000 new annotations per day. 4.3.3.2. Polar words mining. Tencent NLP will dig out certain words or phrases from the document that can represent positive and negative polarities. The construction of the polar vocabulary database can be divided into the following two steps. 1) Text preprocessing. As there is too much noise in the text preprocessing corpus, we must preprocess the text before mining polar words. This preprocessing includes the participle, denoising, best matching, and other related technologies. The participle uses the participle system of the Tencent TE199. Denoising must remove unrelated information in the document, such as “@jjhuang,” HTML labels, along with words that do not have a classification meaning, for instance, pronouns such as “the”, “ah”, “I”, and so on. 2) Selection of polar words. After text preprocessing, Tencent Wen-

3 SVM was first proposed by Corinna Cortes and Vapnik in 1995. It has many unique advantages in solving for small sample, nonlinear, and high-dimensional pattern recognition. In machine learning, SVM is a supervised learning model related to learning algorithms, which can analyze data and identify patterns for classification and regression analysis. 4 The Naive Bayes Model is a classification method based on the Bayes theorem and the independent assumption of characteristic conditions. It is widely used in machine learning and artificial intelligence.

2

Tencent NLP is based on a parallel computing system and a distributed crawler platform combined with unique semantic analysis technology. It provides one station service to satisfy the open platform of Chinese semantic analysis requirements, such as user NLP, transcoding, extraction, full network data grabbing, and so on. Furthermore, users can conduct semantic searches, obtain recommendations and public opinion, mine, and so on, given the semantics of the Application Programming Interface provided by the platform. 6

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Fig. 1. Distribution of mood levels.

significantly and positively related to attention but is significantly and negatively associated with the square of attention, which supports our Hypotheses 1a and 1b. With regard to the moderating effect in H1c, our findings indicate that interaction of the two types of attention on stock returns is positive and significant (β = 0.16, p < 0.05). The results indicate that media attention exerts a moderating effect on investor attention to influence the stock return, in other words, media coverage that focuses on specific stocks can magnify the effect of investor attention. The study further strengthens the evidence that an investor’s attention and investment behavior caused by the news media information transmission have a direct impact on stock returns. Therefore, we can accept Hypothesis 1c.

and Srivastava [88] studied stock and commodity discussions on Twitter and found that 67.14% of tweets were positive. The phenomenon in which positive words are used more often than negative words is known as the “Pollyanna effect” in the literature [89]. In contrast, the news media is always neutral. Objectivity and authenticity are the basic characteristics of the media because it is widely open for public communication. News media should be consistent with the objective facts to the greatest extent possible when releasing tweets so that the subjective sense of those tweets can be eliminated. There are typically two sides to every story; if news reports show only one side of views, media reports will be biased and may mislead the audience, hence damaging the authenticity of the media. However, it is impossible for the news media to be completely objective because journalists will inevitably introduce subjective factors. Although the positive and negative emotions of the news media may be relatively insignificant, they can still affect the stock market; therefore, it is necessary to consider the mood of the news media.

5.2.2. Relationship between mood and stock return We further explore whether sentiment on Sina Weibo is related to stock return (see Table B2 in Appendix B). We find that both news media’s DMI (β = 0.12, p < 0.001) and investors’ DMI (β = 0.05, p < 0.001) have a significant positive relation to intraday stock return, indicating that positive sentiment is associated with higher stock prices. This result is consistent with evidence from psychology research, which proves that positive sentiment causes investors to trade more as they attempt to sustain a positive outcome. For instance, Siganos, VagenasNanos and Verwijmeren [91] examined the relation between daily sentiment and trading behavior in 20 international markets using Facebook’s Gross National Happiness Index and found that sentiment has a positive contemporaneous relation to stock returns. Therefore, we can accept Hypotheses 2a and 2b.

5.2. Empirical results We use OLS to examine our empirical models of H1, H2, and H3 and show the results in the following subsections. 5.2.1. Relationship between attention and the stock return Examining linear relationships, we find a positive relationship between attention and the stock return for both investors and the news media (see Table B1). However, the relationship between stock returns and attention could be nonlinear. To better address this nonlinear effect, we add attention-squared to investigate whether this kind of relationship is reflected in the U-shaped curve. Table B1 in Appendix B shows that the coefficient of the attention square (β= -0.21, p < 0.05) is negative and investor attention-squared has a significant impact on stock return. Our result indicates that there is an inverse U-shaped curve between attention and stock return. Furthermore, this shows that the impact of news media attention on stock returns gradually disappears, after that attention increases to a certain degree. This implies that stocks with no or low social media coverage earn higher returns than those with high coverage even after well-known risk factors are controlled for [11]. There is also evidence from Zhang, Wan and Fu [90] who took all IPO firms as their research sample and found that short-term media reports between the filing date and the listing date positively correlate with IPO underpricing and firstday turnover. However, the long-term media reports released one year before the filing date significantly positively correlate with IPO underpricing, but not to the first-day turnover. With regard to investor attention, the statistical significance (β= -0.26, p < 0.001) suggests the inverted U-shaped relationship between attention and stock return, which is consistent with the over-attention underperformance hypothesis. In summary, the stock return is

5.2.3. Relationship between sentiment and social interaction To investigate whether social interaction will enhance DMI effects on the stock return, we consider the moderating effect of the social interactions of Weibo users (see Table B2 in Appendix B). We find that social interactions positively promote both news media (β = 0.08, p < 0.001) and investors (β = 0.04, p < 0.01) on the relationship between the mood index and stock return. Our results show that the mood contagion can be considered as a mechanism with which affective feelings are transferred between persons who listen to each other [70]. Thus, we can accept Hypothesis 3, which indicates that social interaction plays a positive moderating role in the relationship between mood index and the stock market return. 5.3. Robustness check Finally, we perform a robustness test on the research results. First, we change the explained variable by considering the abnormal return, which is calculated as follows (see Table C1 in Appendix C):

ABNORMAL RETURNit = STOCK RETURNit − MARKET RETURNit . (9) Second, we find that both news media’s DMI and investors’ DMI 7

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media attention [21–23] for stock returns. Very little research exists on the joint effect of different types of attention on stock returns. In addition, previous empirical results mostly indicate a linear correlation between attention and the stock return [15,16]. The “attention-driven buying behavior” means that the attention of market participants is easily affected by the media, resulting in “excessive attention” [55]. Such attention will cause overreaction to new information and overestimation of stock value in the short-term [56] and long-term reversals of subsequent earnings, which is called “over-attention underperformance” phenomenon [57]. Our finding contributes to a better understanding of such phenomenon. Third, we find that both investors’ and the news media’s Daily Mood Index have a significant positive relation to intraday stock return. Additionally, it is reasonable to consider the impact of news media on the stock return but not that of investors. Based on the behavioral finance theory, researchers have shown that the stock market can be driven by the emotions of market participants [61,78]. The role that investor sentiment plays in asset pricing has shown its great importance in financial economics [65,96,43,97]. Moreover, researchers have been less concerned about the social interactions between Internet users when showing the relationship between mood levels and stock returns [48]. People tend to transfer emotions as a result of facial, vocal, and postural feedback, which the social science has recognized as emotional contagion [24,70]. As one of the most influential microblogs in China, Sina Weibo provides users platforms to interact with friends and followers [8] through three types of user interactions: repost, praise, and comment [25]. Collecting and studying users’ data from Sina Weibo, we develop daily mood index for both investors and the news media and explore their influence on stock return. In particular, our study focuses on emotional contagion and investigates how it contributes to interactions and enhances the effects of investor and news media sentiment on the stock return. Our results show that social interaction plays a positive moderating effect in the relationship between mood index and the stock return, thus indicating that social interaction on social media can affect people’s sentiments or behavior. Our findings contribute to a better understanding of the relationship between social media and the stock return by considering the theory of emotion contagion in sentiment analysis.

have a significant positive relation to intraday stock return, indicating that positive sentiment is associated with higher stock prices. To evaluate this result, we examine the relationship between stock returns and the positive mood of investors and the news media. As shown in Table C2 in Appendix C, positive sentiments have a significant impact on stock returns, which is consistent with Hypothesis 2. This could indicate that these results have been accepted by the robustness tests, proving stability and reliability. There is no substantial change in the main test and robustness test results, implying that our study’s conclusion is robust. 6. Discussion 6.1. Major findings This paper investigates the relationship between Sina Weibo and stock returns. Microblog users play an important role in the dissemination of organization information. Among different categories of users, we select investors and news media for our study. We develop our research models based on the two dimensions of attention and emotion and investigate the impact of social media on the stock market from a stakeholder perspective. Our findings are set forth below. First, we model the attention for both investor and news media and examine the nonlinear relationship between attention and stock returns. Our results confirm an inverse U-shaped curve between stock returns and the attention of both investors and the news media, thus supporting the over-attention underperformance hypothesis. In addition, we demonstrate the positive moderating role of the news media in strengthening the relationship between investor attention and the stock return. Second, we find that the stock market can be influenced by the emotions of market participants (most of whom are investors). Our research also incorporates the sentiment of news media because media journalists will inevitably mix in their personal opinions in their reports. We show that both news media’s DMI and investors’ DMI have a significant positive relationship with intraday stock return, indicating that positive sentiment is associated with higher stock prices. Our result aligns with the finding from the psychology literature that shows the effect of positive sentiment on more trading from investors. Finally, we extend previous research by including the interaction variable in the analysis. Considering the social interaction of Internet users, our results verify that social interaction has a positive moderating effect on reinforcing the relationship between mood index and the stock return. In practice, Sina Weibo provides users with the platforms to interact with their friends and followers; hence, their mood levels can be easily influenced by other people.

7. Conclusions 7.1. Managerial implications This study also has several managerial implications for both organizations and the stock market. By classifying microblog users into seven categories, we find that both investors and the news media have a great impact on the stock returns. There are many stakeholders involved in an organization, including shareholders, employees, customers, suppliers, and political groups [95]. It is necessary to consider stakeholder analysis in conflict resolution, project management, and business administration. Managers should identify which groups are most likely to be influenced by a proposed action so as to prioritize groups according to their impacts on the action [98]. In reality, companies are more focused on shareholders, ignoring the interests of other stakeholders when making business decisions. Only when convening the general meeting can the enterprises receive feedback on the investment project, and there is usually very little of such type of feedback information obtained and transferred in a short time. The emergence of social media significantly facilitates the amount, speed, and range of information transmission [99,79]. People use these social platforms to write, share, comment, discuss, interact, and communicate [6]. Our findings suggest that the prominent characteristics of microblogs have made them the center for collecting public opinions. They have extended the ways for the public to express their opinions, resulting in a new platform for public participation [100,25]. Therefore, we suggest that enterprises should pay more

6.2. Theoretical implications This study has several theoretical implications. First, we study how users with different identities influence the stock return from a stakeholder perspective. Previous studies mostly focus on the impact of investors or news media on the stock market from a single perspective [92,11,93,94,91]. They have not considered the different opinions expressed by users on the same social media platform. Microblog users play an important role in transmitting organizational information; information disseminators are not limited to investors or the press. Other groups are also important to organizations, as there exist different relationships between such groups and organizations [81,8]. Therefore, the stakeholder theory provides a new perspective for our research [95]. Second, we establish and test the hypothesis to verify an inverse Ushaped curve between news media attention and stock return. Prior studies show that stocks with news media coverage or investor attention will lead to attention-driven buying [54,23]. However, they only focus on the importance of either investor attention [14,15] or news 8

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attention to the information released by their users and understand their thoughts in a timely fashion through social media, for example, delivering positive news about a company to the public will enhance the company’s image. Social media not only has provided a channel for communication between companies and the public but also has created a platform for establishing companies’ reputation and brand [101]. In the long term, social media will have an impact on a company’s performance. Stock market regulators should pay more attention to the impact of social media information on the stock market and prevent companies or individuals from using the Internet to illegally manipulate the stock market. For example, a person might maliciously release adverse information about a company by distorting or exaggerating facts, which could mislead investors in their investment decisions. Moreover, insiders’ information disclosed through Weibo or other social media outlets will have a great impact on the stock market [102]. Therefore, the relevant regulators should take measures to reduce the negative impacts of social media on the stock market. First, establishing relevant laws and regulations can be an effective way to regulate the behavior of the news media. Second, regulators should strengthen the self-discipline of the media industry and guide the appropriate direction of the development of the media.

Acknowledgments

7.2. Limitations and future research

References

This work was supported by grants from the National Natural Science Foundation of China (71772162), Natural Science Foundation of Zhejiang Province (LQ19G020004), Contemporary Business and Trade Research Center, and the Collaborative Innovation Center of Contemporary Business and Trade Circulation System Construction of Zhejiang Gongshang University (16YXYP01), Key Project of National Social Science foundation of China (19AGL015), Major Project of Key Research Institute of Humanities and Social Sciences of the Ministry of Education (17JJD790019), Special Funds Project for Promoting the Level of Running Local Colleges and Universities in Zhejiang Province (Interdisciplinary Innovation Team Building of Internet and Management Change) and the Zhejiang Provincial Scientific and Technological Innovation activities for Xin Miao Talents Program (ID: 2017R408025). This research is the achievement of New Key Specialized Think Tank of Zhejiang Province (Zheshang Research Institute). Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.im.2019.103181.

Despite our significant findings, this research still has some limitations that require more future work. First, we only focus on the Chinese real-estate stock market, which is limited in its sample size. Further research should increase the sample size to further corroborate the hypotheses. Second, we believe that the effect on intraday stock returns (i.e., the lagging effects of the stock market) should also be taken into account to produce a more accurate result [91]. Third, we select only two most active groups as our research object: investors and news media. Other users could also be taken into account [17]. Therefore, we can further discuss the impact of different users on organizations or the stock market. Future research could focus on important topics from a new shareholder’s perspective [6] and investigate related information about an organization and the stock market. We expect to discover hot topics for shareholders based on Social Network Analysis. Moreover, future papers can study the social interactions underlying the Hot-topic Network Graph. By constructing the Hot-topic Network Graph, we can measure social interactions from the perspective of the social network so that the abstract interactions can be transformed into a vivid interactive network. Moreover, based on the social network, this kind of social interaction can also be quantified. We can calculate network metrics, such as centrality and density, which can be achieved by UCINET5 . We can add these network indicators into the equation and construct the Stakeholder-Stock Market model to more specifically discuss either the impact of each kind of users on the stock of the enterprise or which kind of users has the greatest impact on the stock market. Thus, companies could be aware of such users and discuss their impact on the stock market. This approach would provide a new perspective for future research.

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Yuan Sun is professor at Zhejiang Gongshang University, China. His main research interests include IT/IS usage, IT/IS Innovation, enterprise systems, performance evaluations, IS success, and online social network. He is also interested in various research methodologies. His research has been published in various journals, such as Information & Management, International Journal of Information Management, Journal of Enterprise Information Management, Enterprise Information Systems, Electronic Markets, Computers in Human Behavior, Computers in Industry, Computers & Education, Journal of Computer Information Systems, and various conference proceedings. Prof. Sun is currently serving as a Senior Editor for Information Technology & People, an Associate Editor for Journal of Electronic Commerce Research and Journal of Global Information Management. He is a member of the council of CNAIS. Xuan Liu is lecturer at Zhejiang Gongshang University, China. Her main research interests include social media, online social network, and user-generated content. Her research has been published in various journals, such as Corporate Social Responsibility and Environmental Management, Robotica, European Journal of International Management, Journal of Pacific Rim Psychology, and various conference proceedings. Guangyue Chen is a master student at Fudan University, China. Her main research interests include corporate governance, accounting theory, and social media. Her research has been published in some journals and conference proceedings. Yunhong Hao is professor at Zhejiang Gongshang University, China. His main research interests include corporate governance, accounting theory, and social media. His research has been published in various journals, such as Corporate Social Responsibility and Environmental Management, International Journal of Information Management, International Journal of Environmental Science and Technology. Zuopeng (Justin) Zhang is a faculty member at the Coggin College of Business, University of North Florida. He was previously associate professor of Management, Information Systems, and Analytics at State University of New York, Plattsburgh. He received his Ph.D. degree in Business Administration with specialization in Management Science and Information Systems from Pennsylvania State University, University Park. His research interests include economics of information systems, knowledge management, electronic business, business process management, information security, and social networking. He is the editor-in-chief of the Journal of Global Information Management, an ABET program evaluator, and an IEEE senior member.

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