Investors’ Information Sharing and Use in
Virtual Communities
Research demonstrates that information disseminated and circulated in online forums may have a significant impact on investors and on the securities market, so an understanding of that environment is critical. This article reports on an analysis of information sharing and use in three investment discussion forums. Threads containing 1,787 posts were coded using previously developed typologies for Internet-based discussion. Citations were studied in their context and sources were categorized into types. A high degree of collaborative information behavior was identified, but the study also reveals some areas of information use that may compromise investors’ decision making, including heavy reliance on personal sources of information and other sources that vary greatly in trustworthiness, including commercially sponsored information, blogs, and investor guru sites. These challenges are discussed and recommendations are made for improving services to investors. Questions for additional research are also identified. Introduction Investors throughout the world, including an estimated 80% of investors in China and India, are now trading online (FinWeek, 2011). Low transaction costs, easy access, and aggressive marketing by online brokerage companies are attracting investors in unprecedented numbers. Online brokers also recently have launched a dizzying array of services available through mobile technologies, including applications for the iPhone and BlackBerry, guaranteed to make online trading more accessible, convenient and attractive than ever. The impact online trading has on investors is complex, and there is reason for concern. According to Barber and Odean (2002), investors earn less when they move to the online environment. They write, “Those who switch to online trading perform well prior to going online, beating the market by more than 2% annually. After going online, they trade more actively, more speculatively, and less profitably than before—lagging the market by more than 3% annually” (Barber & Odean, 2002, p. 455). Frith (2011) agrees that “the speed and volatility of such instant trading has made it riskier for small investors” (p. 52). How investors find and use financial information is also transformed in the online environment (Barber & Odean, 2001). Online investors, who are more likely to be new to investing, avoid interacting with brokers (Barber & Odean, 2001). Forgoing the counsel of professional advisors places the burden for finding, evaluating and using information squarely on the shoulders of investors. Online investors are also more likely to restrict their information search to online sources (Williamson, 2008). Williamson and Smith (2010) conclude that online investors need help “dealing with information overload, learning to balance the need for speedy delivery of information with making considered investment decisions, undertaking systematic analysis using information, [and] using advice from interpersonal sources of information judiciously” (p. 72). Although a burgeoning body of research on investors’ information behavior exists to provide such an understanding, less is known about it in virtual environments. Understanding information behavior in online discussion environments is important not only because of its impact on the success or failure of individual investors, but also because collectively their results affect the entire market (Barber & Odean, 2001). In fact, research demonstrates that information disseminated in online forums may have a direct and significant impact on stock price movement (Antweiller & Frank, 2004; Regnier, 1999). Furthermore, information or misinformation can be introduced and circulated in chat forums for the express purpose of manipulating stock prices, so it is critically important online investors know how to evaluate the quality of information they find there (Langevoort, 2002). This article reports on a study of investors’ information sharing and use in virtual discussion Received: December 14, 2011; revised July 27, 2012; accepted July 30, 2012 © 2012 ASIS&T • Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/asi.22791
forums conducted by analyzing the content and context of citations to formal information sources. It describes the types of sources used, analyzes the collaborative information behavior exhibited, and demonstrates how Internet discussion groups function as investment information channels. Literature Review Investor Information Behavior Much of what is known about investor information behavior is based on individual investors and often focuses exclusively on information use. This research nonetheless provides a helpful foundation for this study. Reporting on their study utilizing a telephone survey of 911 randomly sampled U.S. investors, Hira and Loibl (2006, 2009) demonstrate the types and constellations of sources investors use. They found that the investors in their study (65% of whom were male and 35% of whom were female) fell into five clusters of information use: high, online, moderate, workplace, and low. Investors in the high and online categories were self-reliant, information driven and practice multisource, diversified, high-information use strategies. Unfortunately, these investors constituted only 22% of investors studied. They were also highly likely to be male, well-educated, and possess the largest financial assets. The moderate group (24%) practiced a broad but less frequent information use strategy and was also highly likely to be male and well educated. The remaining 54% of investors, who constitute the low and work-place oriented information users, were primarily female with the lowest educational levels and financial assets. Their strategies were characterized by the use of few, if any, formal, mediated information sources and the nonadoption of technology for investment information. Older studies, including McKay et al. (1996), Peter D. Hart Research Associates (1997), and Mezick (2001), suggested that investors prefer printed sources of information, such as newspapers, magazines, and annual reports. For example, Mezick found 77% of investors cited magazine and newspapers as the most frequently used sources of information for investors. Friends and relatives (69%), web pages (54%) and search engines (36%) followed. Financial advisors, television and stockbrokers were each cited by less than 6% of those surveyed as primary information sources. Hira & Loibl’s (2006) findings were quite different: investors in their study cited financial advisors (28%), magazines and newspapers (22%), the Internet (21%), and the workplace (10%) as the most often used sources of financial information. Friends, television, radio, and classes all followed with less than a 5% response for each. Even as early as 2001, however, Mezick found that the growing popularity of the Internet was having an impact on investors’ information use. Fifty-five percent of respondents in her study reported using the Internet daily or weekly, whereas only 6% used library sources with that frequency for investment information. Williamson (2008, 2010) and Williamson and Kingsford Smith (2010), who studied information use by 520 online investors (most of whom were male, outnumbering women five to one) found that 82% of their participants used the Internet for investment-related information: brokers’ websites were the most often used sources of information, followed by company investor relations websites, advice from brokers or analysts via email, general financial information portals, and financial data or charting services. Traditional media were used as well; 79.2% of online investors indicated they obtained information from newspapers and 55.5% from electronic media (radio and TV) at least “frequently.” The majority of participants in their study (62.8%) reported rarely receiving information or advice from family, friends and acquaintances. However, Williamson (2008) reported that “this result turned out to be questionable during the individual interviews. There was much more discussion with family and friends than people either wanted to admit, or thought of admitting” (p. 10). Mezick’s participants described convenience (38%), currency (14%) and ease of use (14%) as primary reasons for Internet adoption. Williamson (2008, 2010) and Williamson and Kingsford Smith (2010) also provide some insight into how online investors select information. In interviews with 26 investors selected from their larger pool of 520, they found that convenience/ease of access and content were important criteria to all their participants, whereas reliability/accuracy, currency/timeliness, and speed of access were important to the majority of them. It is important to note that adoption of technology for investing and investment information seeking varies markedly by sex. In one study, women were 20% less likely to use the Internet for investing information and three times less likely to trade on the Internet (Hira & Loibl, 2006). When asked why they do not use the Internet for investing and investment information, 85% of female investors surveyed indicated they preferred working with people, 49% worried about security, and 44% found financial websites confusing. Barber and Odean (2001) discovered that the Internet may transform investors’ information behaviors. Because the Internet reduces the costs of some information, but not others, it may impact source selection. They write: The Internet especially facilitates comparisons of real time data, and thus has changed investors’ focus by emphasizing the importance of speed and immediacy. While the serious individual investor of a decade ago may have checked stock positions once a day in the morning paper, casual investors now check theirs several times a day. Many more investors pay attention to short term—even intraday—returns than ever before. (p. 48). Other effects of Internet adoption are clear. According to Barber and Odean (2001) new communication channels on the Internet and the popularity of online trading are closely related: the explosion in web-based investment information
is “substituted for brokerage firm guidance, supporting (if not inflating) the sense of confidence for the retail investor” (Barber & Odean, 2001, p. 42). Their research indicates that “when people are given more information on which to base a forecast or assessment, the accuracy of their forecasts tends to improve much more slowly than their confidence in the forecasts. Although the improved accuracy of forecasts yields better decisions, additional information can lead to an illusion of knowledge and foster overconfidence, which leads to biased judgments” (Barber & Odean, 2001, p. 46). The trend to bypass professional investment advice is particularly troublesome because inexperienced nonprofessional investors earn lower returns as their use of unmediated information rises relative to their use of mediated-information (Elliott, Hodge, & Jackson, 2008). Furthermore, in lieu of professional advice, investors “turn to numerous sources of fundamental and technical market information, to chatroom gossip, to online journalists, and to sophisticated advice engines. However the quality of such cyber-resources varies greatly. If investors are unable to distinguish high quality advice from low, they are unlikely to pay more for quality. Indeed with so much information available for free on the Internet, many investors will be unlikely to pay anything for information alone” (Barber & Odean, 2001. p. 44). Thus the abundance and immediacy of Internet-based information strengthens the illusion of being informed (Barber & Odean, 2001). Because investors are heavily influenced by mass media, which lures them to purchase “attention grabbing” stock, greater exposure to more information may also alter the types of companies in which they choose to invest (Barber & Odean, 2008). Williamson (2008, 2010) and Williamson and Kingsford Smith (2010) also identified several problematic effects of investors going online, including participants’ higher levels of trust in information sources than might be warranted, a predilection for speed in the delivery of information and the impact of information overload, which they speculate may be greater on information seekers less experienced than most of those who participated in their study. Overall, Williamson and Kingsford Smith conclude, however, that online investors are a “relatively engaged and knowledgeable group,” which, they add, is “by contrast to the wider populations of investors who research demonstrates are extremely difficult to engage, even in their own self-interest” (Williamson and Kingsford Smith, 2010, p. 69). Online Information Sharing and Use In addition to literature on investor’s information use, research on information sharing and use in virtual communities provides a useful context for this study. Several authors have examined the exchange of health information in online groups (see, for example, Donnelle & Hoffman-Goetz, 2009; Burnett & Buerkle, 2004; Wikgren, 2001, 2003. Savolainen (2001) studied consumer information exchange in a Finish newsgroup. These studies generally find richer information environments than were expected. Strandberg (2008) and O’Connor and Rapchack (2012) examined online information use in political discussion forums and found that virtual environments are not always collaborative. Political discussion boards often included “negative comments, superficial topics, and unsubstantiated claims rather than true conversations” (Strandberg, 2008, p. 83). Clearly information behavior in online communities varies with the make-up of their membership and the nature of the interests that unite them. A few studies have examined investors’ online forums. Antweiller and Frank (2004) examined 1.5 million postings to assess the impact of investors’ online discussions on the market. They found that contentious discussions induce trading, that forum discussions predict market volatility and that forum content reflects public information extremely rapidly. They discovered that discussions about news preceded and predicted treatment of that same topic in print newspapers by about 48 hours and concluded that internet investor boards do contain useful information. However, some studies describe negative aspects of investor chat forums. Langevoort (2002) asserts that the “illusion of control provided by the Internet combined with an immediate audience for hype, fraud, or even ordinary opinion can make investors in chat groups more vulnerable” (Langevoort, 2002, p. 15). The Financial Industry Regulatory Authority (FINRA), an organization that regulates the behavior of professional securities brokers, does delineate acceptable behavior of professionals within social media. They prohibit professionals from posting content that is “unbalanced, overly positive or predicts an imminent price increase” (FINRA, 2011, p. 2). But the agency is still concerned about investor manipulation in chat forums and plans to issue more regulations in the near future (FINRA). Park, Konana, Gu, Kumar, and Raghunathan (2010) analyzed 502 postings from the largest finance message board in South Korea. They found that investors exhibit confirmation bias, the tendency to seek out information that confirms what they already believe, when they select and use information from message boards. This well-documented tendency is exacerbated in virtual communities, because they enable people to interact with individuals who share their beliefs and opinions (Frick, 2011). Barber and Odean (2001) agree that “investors are more likely to visit chatrooms of like-minded investors and, if controversies ensue, they are likely to be convinced by those with whom they already agree. Investors who believe that additional information makes them better investors are unlikely to seek out or attend to evidence that indicates otherwise” (p. 47). Park et al. (2010) also demonstrate that investors with stronger confirmation bias also exhibit greater overconfidence. Consistent with the findings of other studies, overconfident investors in their study also had higher expectations about their performance, traded more frequently, and realized lower returns. They conclude that “these results suggest that participation in virtual communities increases investors’ propensity to commit investment mistakes and is likely to be
detrimental to their investment performance” (Park, et al., 2010, p. 1). Additional literature on group investing is also relevant to this study, although research in this area is slim and its findings are ambiguous and even contradictory. Barber and Odean (2000) demonstrate that overall, investment clubs do not perform well. During their 18-month study, 60% (n = 100) of participating clubs underperformed the market by an average of 4. Club returns were also consistently lower than individual returns by 2 pps (points per share) per year. Hens (2008) supported Barber and Odean’s findings. However, Gort and Gerber (2008) compared the returns of individual investors to those of groups of investors and came to different conclusions. They note “large performance discrepancies across groups” and concluded that “the best groups significantly outperform individuals” (Gort & Gerber, 2008, p. 24). They found that a high level of information exchange (where members not only share information but also evaluate and weigh contradictory information) was the best predictor of strong market performance. “Only if the group members’ opinions are communicated and discussed, do groups outperform individuals” (Gort & Gerber, 2008, p. 24). Though investors’ group information behaviors have been studied in face-to-face contexts, little is known about it in virtual communities. Existing studies about investors in online forums tend to focus on individual information behavior instead. This study will begin to address that gap. Burnett (2000) categorized the types of interactions in online communities and, with Buerkle, revised them in a 2004 study. He separated them into two broad categories; interactive and noninteractive behaviors. Although noninteractive behaviors, often called “lurking” are important, they are beyond the scope of this study. Burnett categorized interactive behaviors, which require active posting of messages, as fundamentally either hostile or collaborative. He furthermore divided collaborative behaviors into those that are explicitly information-oriented and those that are not. These typologies will be used for data analysis in this study and will be discussed at greater length in the Methods and Findings sections of this article. Collaborative Information Behavior Although both collaborative and noncollaborative interactions will be considered, this article will focus on analyzing collaborative, information-oriented behavior. Thus, a brief discussion of research on collaborative information behavior (CIB) provides a useful context for this work. Karunakaran, Spence, and Reddy (2010) define CIB as “the totality of behavior exhibited when people work together to identify an information need, retrieve, seek and share information, evaluate synthesize and make sense of the found information, and then utilize the found information” (p. 2). Reddy and Jansen (2008) and Reddy, Jansen, and Spence (2010) describe four triggers for CIB: (1) complexity of information need, (2) lack of immediately accessible information, (3) lack of domain expertise, and (4) fragmented information resources. Collaborative information behavior has been identified as occurring in both organizational and nonorganizational contexts (Karunakaran et al., 2010). When CIB occurs in investor forums, where participation is voluntary, rather than in organizations, where it is mandatory, it occurs within a greater context of community building. Burnett (2000) describes these online exchanges of texts as virtual communities that “function as social spaces supporting textual ‘conversations’ through which participants can find both socio-emotional support and an active exchange of information” (p. 3). Burnett and Jaeger (2008) argue that these communities can be viewed as “computer-mediated small worlds” with the same types of normative attitudes and behaviors that shape information behavior found in the nonvirtual world (p. 10)1 . These social norms “provide a shared understanding of propriety and correctness of those visible aspects of social activities within the world,” including information sharing and use (Burnett & Jaeger, 2008, p. 6). Burnett and Buerkle (2004) note that the variance between communities can provide an important means for understanding the small worlds that exist in the communities. This study will extend the literature of virtual CIB by describing a previously unknown social information environment and providing an additional point for comparison. Methods As previously stated, the purpose of this study is to describe investors’ behavior in online forums and analyze their information sharing and use. Specifically, it seeks to answer three research questions: R1: What types of communication occur in online investing forums and to what degree can the forums be considered information environments? R2: What motivates collaborative information behavior in online investing forums? R3: What types of information are cited and valued by participants in online investing forums? The first significant challenge in a study like this is selecting data sources from an overwhelming array of existing investing forums. Big Boards (search.big-boards.com) was used to identify top discussion groups by subject. Morningstar Discussion Boards, Market Thoughts, and Finance Forums were the most active boards that were exclusively investment related and moderated. Unmoderated forums were considered, but they were too spam heavy to yield good data. The organization of each of the boards also had to offer reasonable options for data collection. It was also important to balance these discussion groups, so effort was made to select forums that differed from one another in the 1 Burnett and Jaeger (2008) provide an excellent discussion of Chatman’s concept of small worlds as it relates to online communities.
apparent sophistication of their discussions. Although every effort was made to select three forums representative of the array of existing types of forums, the selection of just three sites should be understood as a limitation of this study. Because unmoderated sites were avoided, the data in this study may provide a more sophisticated picture of information use and sharing than would have otherwise resulted. Because financial cycles can swing so much, and conceivably have a real impact on the discussions, data were collected across four years, from 2007 (prior to the recession) through 2010 (as the economy began to rebound). Sampling procedures were used to select a reasonable data set. Each thread in that time period was assigned a number from 1 to the last thread in the forum. A random number generator was used to select a stratified random sample 20% of the threads per year, which, as Table 1 illustrates, yielded a total 358 threads for all three forums. Four of the threads in Morningstar had no postings (presumably they had been deleted by the moderator), so a total of 354 threads containing 1,787 posts were analyzed. Postings were copied to a Word file for data analysis. Individual posts, which were the unit of analysis for this study, were coded according to Burnett’s (2000) and Burnett and Buerkle’s (2004) typologies and placed into one category. As described previously, these typologies are separated into two broad categories; interactive and noninteractive behaviors. This study focuses on interactive behaviors, which are defined as the active posting of messages. Interactive behaviors are coded as fundamentally either hostile, or collaborative. Hostile postings include flaming, which Burnett defines as “ad-hominem argumentation, aiming neither for logic nor for persuasion, but purely and bluntly at insult” (Burnett, 2000, p. 14); trolling, which is “posing a message or the purpose of eliciting an intemperate response” (Burnett, 2000, p. 15) and spamming, which is “the online equivalent of unsolicited junk mail” (Burnett, 2000, p. 15) and cyber-rape, which is “unsolicited, unwelcome (and violently assaultive) information that transgressed the behavioral norms and any shared sense of subject scope held by the community” (Burnett, 2000, p. 16). As previously stated, collaborative information behavior is broadly defined as the “totality of behavior” in identifying and meeting an information need (Karunakaran, Spence, & Reddy, 2010). Burnett defines collaborative behavior more generally and in contrast to hostile behavior as those positive behaviors that “reinforce the community” (Karunakaran, Spence, & Reddy, 2010; p. 17). Collaborative information behaviors are further classified as either explicitly information-oriented or noninformation-oriented or neutral. According to Burnett and Buerkle, neutral behaviors include pleasantries and gossip, humorous behaviors, and empathetic behaviors For this study, collaborative information behavior was coded when the “exchange of information—in terms of seeking or offering of information—is an explicitly motivating factor in an interaction” (Burnett, 2000, p. 18). Coding was conducted first by the author of this study. To assess consistency and reliability of coding, a graduate student was asked to code a data sample. Because Burnett’s definitions are clear and well-defined, coding was highly consistent between coders. Other than the “neutral” classification, which was expanded as will be discussed in the findings section, Burnett’s (2000) and Burnett and Buerkle’s (2004) typologies accurately describe the variety of communication identified by this study. Examples from the data will help clarify coding distinctions; they are presented and discussed in the findings section, where they will simultaneously help illustrate investors’ information behavior. Posts that were coded as collaborative and informationoriented were further analyzed using a content analysis method. Specifically, content was examined to learn whether or not one or more of Reddy, Jansen, and Spence’s (2010) triggers could be identified as motivating collaborative information behavior. Possible triggers are lack of domain knowledge, complexity of information, lack of access to information and fragmentation of information (Reddy & Jansen, 2008; Reddy, Jansen, & Spence, 2010). This study then used methods employed by Wikgren (2003) to examine health information citation behavior in Internet discussion groups. She sampled 30 discussion threads from English-language Internet groups based on two criteria; the threads contained at least one citation to a formal source and the discussion revolved around their topic, nutrition. Posts with citations were extracted, and cited sources were coded as a single source type. Source type codes (for example, books, journals, and websites) were informed by those used by Wikgren (2003), Kingsford-Smith and Williamson (2004), and O’Connor (2013), but because the investing environment is unique and the online environment is ever-changing, new information categories (such as stock and bond rating services) became necessary. These were developed iteratively as source types were coded and type codes were assessed for how well they functioned to describe identified sources.
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Research demonstrates that information disseminated and circulated in online forums may have a significant impact on investors and on the securities market, so an understanding of that environment is critical. This article reports on an analysis of information sharing and use in three investment discussion forums. Threads containing 1,787 posts were coded using previously developed typologies for Internet-based discussion. Citations were studied in their context and sources were categorized into types. A high degree of collaborative information behavior was identified, but the study also reveals some areas of information use that may compromise investors’ decision making, including heavy reliance on personal sources of information and other sources that vary greatly in trustworthiness, including commercially sponsored information, blogs, and investor guru sites. These challenges are discussed and recommendations are made for improving services to investors. Questions for additional research are also identified. Introduction Investors throughout the world, including an estimated 80% of investors in China and India, are now trading online (FinWeek, 2011). Low transaction costs, easy access, and aggressive marketing by online brokerage companies are attracting investors in unprecedented numbers. Online brokers also recently have launched a dizzying array of services available through mobile technologies, including applications for the iPhone and BlackBerry, guaranteed to make online trading more accessible, convenient and attractive than ever. The impact online trading has on investors is complex, and there is reason for concern. According to Barber and Odean (2002), investors earn less when they move to the online environment. They write, “Those who switch to online trading perform well prior to going online, beating the market by more than 2% annually. After going online, they trade more actively, more speculatively, and less profitably than before—lagging the market by more than 3% annually” (Barber & Odean, 2002, p. 455). Frith (2011) agrees that “the speed and volatility of such instant trading has made it riskier for small investors” (p. 52). How investors find and use financial information is also transformed in the online environment (Barber & Odean, 2001). Online investors, who are more likely to be new to investing, avoid interacting with brokers (Barber & Odean, 2001). Forgoing the counsel of professional advisors places the burden for finding, evaluating and using information squarely on the shoulders of investors. Online investors are also more likely to restrict their information search to online sources (Williamson, 2008). Williamson and Smith (2010) conclude that online investors need help “dealing with information overload, learning to balance the need for speedy delivery of information with making considered investment decisions, undertaking systematic analysis using information, [and] using advice from interpersonal sources of information judiciously” (p. 72). Although a burgeoning body of research on investors’ information behavior exists to provide such an understanding, less is known about it in virtual environments. Understanding information behavior in online discussion environments is important not only because of its impact on the success or failure of individual investors, but also because collectively their results affect the entire market (Barber & Odean, 2001). In fact, research demonstrates that information disseminated in online forums may have a direct and significant impact on stock price movement (Antweiller & Frank, 2004; Regnier, 1999). Furthermore, information or misinformation can be introduced and circulated in chat forums for the express purpose of manipulating stock prices, so it is critically important online investors know how to evaluate the quality of information they find there (Langevoort, 2002). This article reports on a study of investors’ information sharing and use in virtual discussion Received: December 14, 2011; revised July 27, 2012; accepted July 30, 2012 © 2012 ASIS&T • Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/asi.22791
forums conducted by analyzing the content and context of citations to formal information sources. It describes the types of sources used, analyzes the collaborative information behavior exhibited, and demonstrates how Internet discussion groups function as investment information channels. Literature Review Investor Information Behavior Much of what is known about investor information behavior is based on individual investors and often focuses exclusively on information use. This research nonetheless provides a helpful foundation for this study. Reporting on their study utilizing a telephone survey of 911 randomly sampled U.S. investors, Hira and Loibl (2006, 2009) demonstrate the types and constellations of sources investors use. They found that the investors in their study (65% of whom were male and 35% of whom were female) fell into five clusters of information use: high, online, moderate, workplace, and low. Investors in the high and online categories were self-reliant, information driven and practice multisource, diversified, high-information use strategies. Unfortunately, these investors constituted only 22% of investors studied. They were also highly likely to be male, well-educated, and possess the largest financial assets. The moderate group (24%) practiced a broad but less frequent information use strategy and was also highly likely to be male and well educated. The remaining 54% of investors, who constitute the low and work-place oriented information users, were primarily female with the lowest educational levels and financial assets. Their strategies were characterized by the use of few, if any, formal, mediated information sources and the nonadoption of technology for investment information. Older studies, including McKay et al. (1996), Peter D. Hart Research Associates (1997), and Mezick (2001), suggested that investors prefer printed sources of information, such as newspapers, magazines, and annual reports. For example, Mezick found 77% of investors cited magazine and newspapers as the most frequently used sources of information for investors. Friends and relatives (69%), web pages (54%) and search engines (36%) followed. Financial advisors, television and stockbrokers were each cited by less than 6% of those surveyed as primary information sources. Hira & Loibl’s (2006) findings were quite different: investors in their study cited financial advisors (28%), magazines and newspapers (22%), the Internet (21%), and the workplace (10%) as the most often used sources of financial information. Friends, television, radio, and classes all followed with less than a 5% response for each. Even as early as 2001, however, Mezick found that the growing popularity of the Internet was having an impact on investors’ information use. Fifty-five percent of respondents in her study reported using the Internet daily or weekly, whereas only 6% used library sources with that frequency for investment information. Williamson (2008, 2010) and Williamson and Kingsford Smith (2010), who studied information use by 520 online investors (most of whom were male, outnumbering women five to one) found that 82% of their participants used the Internet for investment-related information: brokers’ websites were the most often used sources of information, followed by company investor relations websites, advice from brokers or analysts via email, general financial information portals, and financial data or charting services. Traditional media were used as well; 79.2% of online investors indicated they obtained information from newspapers and 55.5% from electronic media (radio and TV) at least “frequently.” The majority of participants in their study (62.8%) reported rarely receiving information or advice from family, friends and acquaintances. However, Williamson (2008) reported that “this result turned out to be questionable during the individual interviews. There was much more discussion with family and friends than people either wanted to admit, or thought of admitting” (p. 10). Mezick’s participants described convenience (38%), currency (14%) and ease of use (14%) as primary reasons for Internet adoption. Williamson (2008, 2010) and Williamson and Kingsford Smith (2010) also provide some insight into how online investors select information. In interviews with 26 investors selected from their larger pool of 520, they found that convenience/ease of access and content were important criteria to all their participants, whereas reliability/accuracy, currency/timeliness, and speed of access were important to the majority of them. It is important to note that adoption of technology for investing and investment information seeking varies markedly by sex. In one study, women were 20% less likely to use the Internet for investing information and three times less likely to trade on the Internet (Hira & Loibl, 2006). When asked why they do not use the Internet for investing and investment information, 85% of female investors surveyed indicated they preferred working with people, 49% worried about security, and 44% found financial websites confusing. Barber and Odean (2001) discovered that the Internet may transform investors’ information behaviors. Because the Internet reduces the costs of some information, but not others, it may impact source selection. They write: The Internet especially facilitates comparisons of real time data, and thus has changed investors’ focus by emphasizing the importance of speed and immediacy. While the serious individual investor of a decade ago may have checked stock positions once a day in the morning paper, casual investors now check theirs several times a day. Many more investors pay attention to short term—even intraday—returns than ever before. (p. 48). Other effects of Internet adoption are clear. According to Barber and Odean (2001) new communication channels on the Internet and the popularity of online trading are closely related: the explosion in web-based investment information
is “substituted for brokerage firm guidance, supporting (if not inflating) the sense of confidence for the retail investor” (Barber & Odean, 2001, p. 42). Their research indicates that “when people are given more information on which to base a forecast or assessment, the accuracy of their forecasts tends to improve much more slowly than their confidence in the forecasts. Although the improved accuracy of forecasts yields better decisions, additional information can lead to an illusion of knowledge and foster overconfidence, which leads to biased judgments” (Barber & Odean, 2001, p. 46). The trend to bypass professional investment advice is particularly troublesome because inexperienced nonprofessional investors earn lower returns as their use of unmediated information rises relative to their use of mediated-information (Elliott, Hodge, & Jackson, 2008). Furthermore, in lieu of professional advice, investors “turn to numerous sources of fundamental and technical market information, to chatroom gossip, to online journalists, and to sophisticated advice engines. However the quality of such cyber-resources varies greatly. If investors are unable to distinguish high quality advice from low, they are unlikely to pay more for quality. Indeed with so much information available for free on the Internet, many investors will be unlikely to pay anything for information alone” (Barber & Odean, 2001. p. 44). Thus the abundance and immediacy of Internet-based information strengthens the illusion of being informed (Barber & Odean, 2001). Because investors are heavily influenced by mass media, which lures them to purchase “attention grabbing” stock, greater exposure to more information may also alter the types of companies in which they choose to invest (Barber & Odean, 2008). Williamson (2008, 2010) and Williamson and Kingsford Smith (2010) also identified several problematic effects of investors going online, including participants’ higher levels of trust in information sources than might be warranted, a predilection for speed in the delivery of information and the impact of information overload, which they speculate may be greater on information seekers less experienced than most of those who participated in their study. Overall, Williamson and Kingsford Smith conclude, however, that online investors are a “relatively engaged and knowledgeable group,” which, they add, is “by contrast to the wider populations of investors who research demonstrates are extremely difficult to engage, even in their own self-interest” (Williamson and Kingsford Smith, 2010, p. 69). Online Information Sharing and Use In addition to literature on investor’s information use, research on information sharing and use in virtual communities provides a useful context for this study. Several authors have examined the exchange of health information in online groups (see, for example, Donnelle & Hoffman-Goetz, 2009; Burnett & Buerkle, 2004; Wikgren, 2001, 2003. Savolainen (2001) studied consumer information exchange in a Finish newsgroup. These studies generally find richer information environments than were expected. Strandberg (2008) and O’Connor and Rapchack (2012) examined online information use in political discussion forums and found that virtual environments are not always collaborative. Political discussion boards often included “negative comments, superficial topics, and unsubstantiated claims rather than true conversations” (Strandberg, 2008, p. 83). Clearly information behavior in online communities varies with the make-up of their membership and the nature of the interests that unite them. A few studies have examined investors’ online forums. Antweiller and Frank (2004) examined 1.5 million postings to assess the impact of investors’ online discussions on the market. They found that contentious discussions induce trading, that forum discussions predict market volatility and that forum content reflects public information extremely rapidly. They discovered that discussions about news preceded and predicted treatment of that same topic in print newspapers by about 48 hours and concluded that internet investor boards do contain useful information. However, some studies describe negative aspects of investor chat forums. Langevoort (2002) asserts that the “illusion of control provided by the Internet combined with an immediate audience for hype, fraud, or even ordinary opinion can make investors in chat groups more vulnerable” (Langevoort, 2002, p. 15). The Financial Industry Regulatory Authority (FINRA), an organization that regulates the behavior of professional securities brokers, does delineate acceptable behavior of professionals within social media. They prohibit professionals from posting content that is “unbalanced, overly positive or predicts an imminent price increase” (FINRA, 2011, p. 2). But the agency is still concerned about investor manipulation in chat forums and plans to issue more regulations in the near future (FINRA). Park, Konana, Gu, Kumar, and Raghunathan (2010) analyzed 502 postings from the largest finance message board in South Korea. They found that investors exhibit confirmation bias, the tendency to seek out information that confirms what they already believe, when they select and use information from message boards. This well-documented tendency is exacerbated in virtual communities, because they enable people to interact with individuals who share their beliefs and opinions (Frick, 2011). Barber and Odean (2001) agree that “investors are more likely to visit chatrooms of like-minded investors and, if controversies ensue, they are likely to be convinced by those with whom they already agree. Investors who believe that additional information makes them better investors are unlikely to seek out or attend to evidence that indicates otherwise” (p. 47). Park et al. (2010) also demonstrate that investors with stronger confirmation bias also exhibit greater overconfidence. Consistent with the findings of other studies, overconfident investors in their study also had higher expectations about their performance, traded more frequently, and realized lower returns. They conclude that “these results suggest that participation in virtual communities increases investors’ propensity to commit investment mistakes and is likely to be
detrimental to their investment performance” (Park, et al., 2010, p. 1). Additional literature on group investing is also relevant to this study, although research in this area is slim and its findings are ambiguous and even contradictory. Barber and Odean (2000) demonstrate that overall, investment clubs do not perform well. During their 18-month study, 60% (n = 100) of participating clubs underperformed the market by an average of 4. Club returns were also consistently lower than individual returns by 2 pps (points per share) per year. Hens (2008) supported Barber and Odean’s findings. However, Gort and Gerber (2008) compared the returns of individual investors to those of groups of investors and came to different conclusions. They note “large performance discrepancies across groups” and concluded that “the best groups significantly outperform individuals” (Gort & Gerber, 2008, p. 24). They found that a high level of information exchange (where members not only share information but also evaluate and weigh contradictory information) was the best predictor of strong market performance. “Only if the group members’ opinions are communicated and discussed, do groups outperform individuals” (Gort & Gerber, 2008, p. 24). Though investors’ group information behaviors have been studied in face-to-face contexts, little is known about it in virtual communities. Existing studies about investors in online forums tend to focus on individual information behavior instead. This study will begin to address that gap. Burnett (2000) categorized the types of interactions in online communities and, with Buerkle, revised them in a 2004 study. He separated them into two broad categories; interactive and noninteractive behaviors. Although noninteractive behaviors, often called “lurking” are important, they are beyond the scope of this study. Burnett categorized interactive behaviors, which require active posting of messages, as fundamentally either hostile or collaborative. He furthermore divided collaborative behaviors into those that are explicitly information-oriented and those that are not. These typologies will be used for data analysis in this study and will be discussed at greater length in the Methods and Findings sections of this article. Collaborative Information Behavior Although both collaborative and noncollaborative interactions will be considered, this article will focus on analyzing collaborative, information-oriented behavior. Thus, a brief discussion of research on collaborative information behavior (CIB) provides a useful context for this work. Karunakaran, Spence, and Reddy (2010) define CIB as “the totality of behavior exhibited when people work together to identify an information need, retrieve, seek and share information, evaluate synthesize and make sense of the found information, and then utilize the found information” (p. 2). Reddy and Jansen (2008) and Reddy, Jansen, and Spence (2010) describe four triggers for CIB: (1) complexity of information need, (2) lack of immediately accessible information, (3) lack of domain expertise, and (4) fragmented information resources. Collaborative information behavior has been identified as occurring in both organizational and nonorganizational contexts (Karunakaran et al., 2010). When CIB occurs in investor forums, where participation is voluntary, rather than in organizations, where it is mandatory, it occurs within a greater context of community building. Burnett (2000) describes these online exchanges of texts as virtual communities that “function as social spaces supporting textual ‘conversations’ through which participants can find both socio-emotional support and an active exchange of information” (p. 3). Burnett and Jaeger (2008) argue that these communities can be viewed as “computer-mediated small worlds” with the same types of normative attitudes and behaviors that shape information behavior found in the nonvirtual world (p. 10)1 . These social norms “provide a shared understanding of propriety and correctness of those visible aspects of social activities within the world,” including information sharing and use (Burnett & Jaeger, 2008, p. 6). Burnett and Buerkle (2004) note that the variance between communities can provide an important means for understanding the small worlds that exist in the communities. This study will extend the literature of virtual CIB by describing a previously unknown social information environment and providing an additional point for comparison. Methods As previously stated, the purpose of this study is to describe investors’ behavior in online forums and analyze their information sharing and use. Specifically, it seeks to answer three research questions: R1: What types of communication occur in online investing forums and to what degree can the forums be considered information environments? R2: What motivates collaborative information behavior in online investing forums? R3: What types of information are cited and valued by participants in online investing forums? The first significant challenge in a study like this is selecting data sources from an overwhelming array of existing investing forums. Big Boards (search.big-boards.com) was used to identify top discussion groups by subject. Morningstar Discussion Boards, Market Thoughts, and Finance Forums were the most active boards that were exclusively investment related and moderated. Unmoderated forums were considered, but they were too spam heavy to yield good data. The organization of each of the boards also had to offer reasonable options for data collection. It was also important to balance these discussion groups, so effort was made to select forums that differed from one another in the 1 Burnett and Jaeger (2008) provide an excellent discussion of Chatman’s concept of small worlds as it relates to online communities.
apparent sophistication of their discussions. Although every effort was made to select three forums representative of the array of existing types of forums, the selection of just three sites should be understood as a limitation of this study. Because unmoderated sites were avoided, the data in this study may provide a more sophisticated picture of information use and sharing than would have otherwise resulted. Because financial cycles can swing so much, and conceivably have a real impact on the discussions, data were collected across four years, from 2007 (prior to the recession) through 2010 (as the economy began to rebound). Sampling procedures were used to select a reasonable data set. Each thread in that time period was assigned a number from 1 to the last thread in the forum. A random number generator was used to select a stratified random sample 20% of the threads per year, which, as Table 1 illustrates, yielded a total 358 threads for all three forums. Four of the threads in Morningstar had no postings (presumably they had been deleted by the moderator), so a total of 354 threads containing 1,787 posts were analyzed. Postings were copied to a Word file for data analysis. Individual posts, which were the unit of analysis for this study, were coded according to Burnett’s (2000) and Burnett and Buerkle’s (2004) typologies and placed into one category. As described previously, these typologies are separated into two broad categories; interactive and noninteractive behaviors. This study focuses on interactive behaviors, which are defined as the active posting of messages. Interactive behaviors are coded as fundamentally either hostile, or collaborative. Hostile postings include flaming, which Burnett defines as “ad-hominem argumentation, aiming neither for logic nor for persuasion, but purely and bluntly at insult” (Burnett, 2000, p. 14); trolling, which is “posing a message or the purpose of eliciting an intemperate response” (Burnett, 2000, p. 15) and spamming, which is “the online equivalent of unsolicited junk mail” (Burnett, 2000, p. 15) and cyber-rape, which is “unsolicited, unwelcome (and violently assaultive) information that transgressed the behavioral norms and any shared sense of subject scope held by the community” (Burnett, 2000, p. 16). As previously stated, collaborative information behavior is broadly defined as the “totality of behavior” in identifying and meeting an information need (Karunakaran, Spence, & Reddy, 2010). Burnett defines collaborative behavior more generally and in contrast to hostile behavior as those positive behaviors that “reinforce the community” (Karunakaran, Spence, & Reddy, 2010; p. 17). Collaborative information behaviors are further classified as either explicitly information-oriented or noninformation-oriented or neutral. According to Burnett and Buerkle, neutral behaviors include pleasantries and gossip, humorous behaviors, and empathetic behaviors For this study, collaborative information behavior was coded when the “exchange of information—in terms of seeking or offering of information—is an explicitly motivating factor in an interaction” (Burnett, 2000, p. 18). Coding was conducted first by the author of this study. To assess consistency and reliability of coding, a graduate student was asked to code a data sample. Because Burnett’s definitions are clear and well-defined, coding was highly consistent between coders. Other than the “neutral” classification, which was expanded as will be discussed in the findings section, Burnett’s (2000) and Burnett and Buerkle’s (2004) typologies accurately describe the variety of communication identified by this study. Examples from the data will help clarify coding distinctions; they are presented and discussed in the findings section, where they will simultaneously help illustrate investors’ information behavior. Posts that were coded as collaborative and informationoriented were further analyzed using a content analysis method. Specifically, content was examined to learn whether or not one or more of Reddy, Jansen, and Spence’s (2010) triggers could be identified as motivating collaborative information behavior. Possible triggers are lack of domain knowledge, complexity of information, lack of access to information and fragmentation of information (Reddy & Jansen, 2008; Reddy, Jansen, & Spence, 2010). This study then used methods employed by Wikgren (2003) to examine health information citation behavior in Internet discussion groups. She sampled 30 discussion threads from English-language Internet groups based on two criteria; the threads contained at least one citation to a formal source and the discussion revolved around their topic, nutrition. Posts with citations were extracted, and cited sources were coded as a single source type. Source type codes (for example, books, journals, and websites) were informed by those used by Wikgren (2003), Kingsford-Smith and Williamson (2004), and O’Connor (2013), but because the investing environment is unique and the online environment is ever-changing, new information categories (such as stock and bond rating services) became necessary. These were developed iteratively as source types were coded and type codes were assessed for how well they functioned to describe identified sources.
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