Wikipedia pageviews as investors’ attention indicator for Nasdaq

Abstract

Summary The attempt to measure investors’ mood to find an early indicator of financial markets has evolved and developed with the advancement of technology over the years. The first attempts were based on surveys, a long and expensive process. Nowadays, big data has made it possible to measure the investor’s mood accurately and almost entirely online. This paper analyzes the explanatory and predictive capacity of Wikipedia pageviews for the Nasdaq index. For this purpose, two econometric models have been developed. In both models, the explanatory variable is the number of Wikipedia visits, and the endogenous variable is Nasdaq index return. As an alternative to this approach, an algorithmic trading system has been developed. It uses Wikipedia visits as investment signals for long and short positions to check the predictability power of this indicator. It is determined that the volume of queries about Nasdaq companies is a statistically significant variable for expressing the evolution of this index. However, it has no predictive capacity. Keeping in mind the capacity of Wikipedia to exemplify Nasdaq trends, further studies should be conducted to determine how to make this indicator profitable.

The dynamical relation between price changes and trading volume

“…Also, as in Gómez-Martínez et al. ( 2022 ), The Nasdaq-100 index was chosen as the subject of study because technology companies represent a significant component of the index and have become a substantial economic influence. In addition, it offers a database of excellent quality that has been utilized in a variety of analyses, as we review in the next section.…”

Do popular research topics attract the most social attention? A first proposal based on OpenAlex and Wikipedia

“…The page views of Wikipedia have been proposed as a valuable metric of broader social attention, even capable of capturing global trends (Yoshida et al, 2015). There are several cases in which a correlation has been found between Wikipedia page views and other phenomena of a markedly social nature, such as market trends (Gómez-Martínez et al, 2022), tourism (Donovan et al, 2017), or disease monitoring (Generous et al, 2014). Positive relationships have also been found with science, for example between the academic prestige of universities and their social attention (Arroyo-Machado, Díaz-Faes, et al, 2022).…”