The Effect of the Quality of Rumors On Market Yields

I. INTRODUCTION During the last decade the world has witnessed the opening of literally countless websites, chat rooms and forums providing information on financial markets. In most cases opinions, estimates and predictions of investors and private analysts can best be described as merely rumors and not necessarily as objective and reliable information analyzed by experts. A decade ago most rumors were purely speculative and unreliable nevertheless their effect on stock prices and on abnormal returns was often substantial. Sites deal with rumors of all kinds: true or false (see Spiegel et al., 2010), single or multiple etc. Based on the wealth of data that has become available in recent years we may provide answers to additional questions: How much does each kind of rumor affect abnormal returns prior to the event day (due to a "leakage process"), on the event day, and afterwards. What is the different impact of initial rumors that are later proven to be true and those that have turned out to be untrue? These issues are discussed below where we adopt an event study approach to examine them, based on data sets of the Israeli stock market. The common well-known approach regarding financial market performance is the Efficient Market Hypothesis (EMH) that states: "Stock prices fully reflect all relevant up-to-date information at any given time". However we find in our empirical study that investors often find opportunities to achieve abnormal returns. Such instances represent an anomaly in the market that contradicts the EMH. In order to prove the existence of such anomalies we use data sets from the most authoritative and important Israeli sites: spouser.co.il, dbursa.com, and trading4living.com, since they are the largest financial gossip and rumor sites in Israel. We focus on published rumors in those sites and investigate their effects on the selected stocks' performance using data sets of events prior to and after the rumor(s) becoming public knowledge. The latest literature deals with particular rumors transmitted by way of the Internet, as discussed by Werner and Murray (2004). They found that a positive rumor usually leads to a positive return on the following trading day, while a negative message leads to a negative return on the following trading day. Kiymaz (2001) examines good and bad rumors and finds that the good rumors generate abnormal returns beginning four days before their publication, while the effect of negative rumors begins only after publication. Wysocki (1999) found increasing returns and trade values the day following the rumor, especially when it is published at night while markets are closed. Tumarkin and Whitelaw (2001) examined the influence of Internet financial announcements on stock yields and trade volume by branches based on only one site. Their main conclusion is that we cannot predict volume and yield by branch type. In this sense we believe that our results are uniquely accurate since we base our analysis and results on data sets that include three different and independent sites for the years 2005-2007, a period during which financial rumors had become very popular and widespread. In our previous work (Spiegel et al., 2010), we explored the financial market's response to reliable and true information as well as false ones, and its impact on changes in yields before and after the event. Here we continue the investigation by analyzing and estimating the impact of a single rumor and multiple rumors on yields, as well as the effects of initial and subsequent rumors on those yields. II. SAMPLE DATA On March 1st 2007 we looked at three sites: Sponsor, The Bursa, and Trading for Living, searching for general rumors concerning various stocks trading on the Israeli stock market. We selected rumors that predicted higher expected prices than the current market price of those stocks. These kinds of rumors are different from the rumors used by Lerman (2011) who examines rumors regarding items of financial accounts such as balance sheets, or periodic financial reports etc. …