The Economics of Attention: Maximizing User Value in Information Rich Environments August 2008 SSRN Electronic Journal A distinguishing feature of the information era is the saliency of people's attention as a scarce resource. Abstract The investors’ attention has been extensively used to predict the stock market. Different from existing proxies of the investors’ attention, such as the Google trends, Baidu index ( BI), we argue the collective attention from the stock trading platforms could reflect the investors’ attention more closely. By calculated the increments of the attention volume for each stock ( IAVS) from the stock trading platforms, we investigate the effect of investors’ attention measured by the IAVS on the movement of the stock market. The experimental results for Chinese Securities Index 100 (CSI100) show that the BI is significantly correlated with the returns of CSI100 at 1% significance level only in 2014. However, it should be emphasized that the correlation of the new proposed measure, namely IAVS, is significantly at 1% significance level in 2014 and 2015. ![]() It shows that the effect of the measure IAVS on the movement of the stock market is more stable and significant than BI. This study yields important invest implications and better understanding of collective investors’ attention. Citation: Yang Z-H, Liu J-G, Yu C-R, Han J-T (2017) Quantifying the effect of investors’ attention on stock market. PLoS ONE 12(5): e0176836. Editor: Wei-Xing Zhou, East China University of Science and Technology, CHINA Received: December 7, 2016; Accepted: April 18, 2017; Published: May 23, 2017 Copyright: © 2017 Yang et al. Picture of 1000 dollar bill. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are included within the paper and its Supporting Information files. ![]() Funding: This work is supported by the National Natural Science Foundation of China (Nos. 61374177, 71371125 and 71271026), the Project of Teaching Reform for Higher Education in Zhejiang Province (No:kg2015409), JGL is supported by The Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, Supported by Shuguang Program Project of Shanghai Educational Committee (Grant No.14SG42), and the Sino Swiss Science and Technology Cooperation(No. Competing interests: The authors have declared that no competing interests exist. Introduction The investors’ attention plays an important role in predicting the movement of the stock market, which has attracted much attention recently [–]. The attention is not only a scarce cognitive resource [] but also the hard currency of cyberspace []. In fact, it is hard for most investors, especially retail investors, to access the market information timely and accurately. Therefore, most investors would like to pay more attention to the attracted information to adjust their investment behavior, leading to the movement of the stock market [, ]. The proxies of investors’ attention for predicting the movement of the stock market could be roughly classified into two categories. The indirect proxies of investors’ attention mainly include extreme return [], trading volume [–], turnover [, ], etc. [–], which have been extensively analyzed by professional investors for many years. On the other hand, direct proxies of investors’ attention, such as search volume index ( SVI) [, –], social network (Twitter feeds, blogs, forum, Wikipedia etc.) [–], news [–], etc. Have been introduced to predict the movement of the stock market. In particular, the massive data sources resulting from human interaction with the Internet have offered a new perspective on the behavior of market participants besides investors in the stock market. For example, the SVI has been used to predict the movement of the stock market [, –,–]. By introducing the search volume in Google of a sample of Russell 3000 stocks, Da et al. [] found that the increase in the SVI could successfully predict higher stock prices in a short term and eventual price reversal. Based on the Google trends or Baidu index, the similar results of French, Japanese and Chinese stock markets have been found [–]. [] found that the search volume of the Google trends for financial related words could be used to predict the stock market volatility. Besides the SVI, the sentiment detected from the social network users also could affect the movement of the stock market [–]. By the inclusion of specific public mood dimensions, Bollena et al. [] found that the prediction accuracy of the Dow Jones Industrial Average can be significantly improved. These works help us to predict the movement of the stock market in the new perspectives.
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