Wikipedia pageviews as investors’ attention indicator for Nasdaq

Q1 Economics, Econometrics and Finance
Raúl Gómez-Martínez, Carmen Orden-Cruz, Juan Gabriel Martínez-Navalón
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引用次数: 1

Abstract

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.

Abstract Image

维基百科页面浏览量是投资者关注纳斯达克的指标
多年来,随着科技的进步,衡量投资者情绪、寻找金融市场早期指标的尝试不断演变和发展。第一次尝试是基于调查,这是一个漫长而昂贵的过程。如今,大数据已经使准确衡量投资者情绪成为可能,而且几乎完全是在线的。本文分析了维基百科页面浏览量对纳斯达克指数的解释和预测能力。为此目的,开发了两个计量经济模型。在这两个模型中,解释变量为维基百科访问量,内生变量为纳斯达克指数收益率。作为这种方法的替代方案,一种算法交易系统已经被开发出来。它使用维基百科访问量作为多头和空头头寸的投资信号,以检验该指标的可预测性。确定对纳斯达克公司的查询量是表示该指数演变的统计显著变量。然而,它没有预测能力。记住维基百科对纳斯达克趋势的示范能力,应该进行进一步的研究,以确定如何使这个指标有利可图。
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来源期刊
Intelligent Systems in Accounting, Finance and Management
Intelligent Systems in Accounting, Finance and Management Economics, Econometrics and Finance-Finance
CiteScore
6.00
自引率
0.00%
发文量
0
期刊介绍: Intelligent Systems in Accounting, Finance and Management is a quarterly international journal which publishes original, high quality material dealing with all aspects of intelligent systems as they relate to the fields of accounting, economics, finance, marketing and management. In addition, the journal also is concerned with related emerging technologies, including big data, business intelligence, social media and other technologies. It encourages the development of novel technologies, and the embedding of new and existing technologies into applications of real, practical value. Therefore, implementation issues are of as much concern as development issues. The journal is designed to appeal to academics in the intelligent systems, emerging technologies and business fields, as well as to advanced practitioners who wish to improve the effectiveness, efficiency, or economy of their working practices. A special feature of the journal is the use of two groups of reviewers, those who specialize in intelligent systems work, and also those who specialize in applications areas. Reviewers are asked to address issues of originality and actual or potential impact on research, teaching, or practice in the accounting, finance, or management fields. Authors working on conceptual developments or on laboratory-based explorations of data sets therefore need to address the issue of potential impact at some level in submissions to the journal.
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