4/5/2023 0 Comments Cleanspark wikipedia![]() Beyond, Joseph et al. (2011) find that stock ticker searches on Google predict abnormal portfolio returns and trading volume. (2019) discover a similar result for individual-level stocks. Besides, Dimpfl and Jank (2016) find that Google searches can be used to augment forecasts of realized volatility on the index level, and Audrino et al. Similarly, Preis et al. (2013) and Moat et al. (2013) provide empirical evidence that Google and Wikipedia searches for financial terms, respectively, predict stock market movements. For instance, Da et al. (2011) show that the Google Search Volume Index constitutes a more convincing measure of investor attention than other indirect proxies, such as extreme returns and abnormal trading volume (e.g., Barber, Odean, 2008). Online investor attention measured by online search queries, primarily Google and Wikipedia searches, has been at the center of an extensive empirical literature in finance. The latter are generally perceived to be at an informational disadvantage compared to institutional investors. In line both with noise trader theories in the spirit of Kyle (1985) and Black (1986) as well as with behavioral models inspired by De Long et al. (1990) and Shleifer and Vishny (1997), such online information gathering is often attributed to retail investors. In recent years, online searches have increasingly been associated with the informational demand of investors and, consequently, have been used as a measure of online investor attention. ![]() ![]() However, it is also a latent variable, and several different proxies for investor attention have been employed in the past. Investor attention is a potentially valuable input factor for many financial forecasting exercises. ![]()
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