Collective Attention and Stock Prices: Evidence from Google Trends Data on Standard and Poor's 100

Heiberger, Raphael H.

doi:10.1371/journal.pone.0135311

Cited by 33 publications

(36 citation statements)

References 39 publications

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“…Since Ginsberg et al (2009), Polgreen et al (2008), who first used Google search volumes to forecast the diffusion of influenza, the information contents of Google queries have also been analyzed for other phenomena. In particular, some studies focused on the relations between web searches and economic variables (for example, see Askitas et al, 2009;Bijl et al, 2016;Dzielinski, 2012;Heiberger, 2015;Joseph et al, 2011;Kristoufek, 2015;Li et al, 2015;Liu et al, 2015;Mondria et al, 2010;Vlastakis and Markellos, 2012;Vozlyublennaia, 2014). Before the application in finance, Askitas et al (2009) used search volumes to analyze economic conditions, such as unemployment rates.…”

Section: Introductionmentioning

confidence: 99%

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Google search volumes for portfolio management: performances and asset concentration

Maggi

Uberti

2019

Ann Oper Res

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Google search volumes have proven to be useful in portfolio management. The basic idea is that high search volumes are related to bad news and risk increase. This paper shows additional evidence about the use of Google search volumes in risk management, for the Dow Jones Industrial Average index components from 2004 to 2017. To overcome the (time-series and cross-section) limitations Google imposes on data download, a renormalization procedure is presented, to obtain a multivariate sample of volumes, which preserve their relative magnitude. The results indicate that the volume normalization is relevant for portfolio performances. Renormalized Google search volumes yield poor results when they penalize the portfolio diversification. Instead, if the portfolio diversification can be kept to an acceptable level, the renormalized Google search volumes contribute to improving risk-adjusted performances.

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Section: Introductionmentioning

confidence: 99%

“…The information contents of Google search volumes has been documented in a financial framework by, among others, Da et al (2011); Dzielinski (2012); Heiberger (2015); Kristoufek (2015); Li et al (2015); Liu et al (2015); Vlastakis and Markellos (2012); Vozlyublennaia (2014).…”

Section: Introductionmentioning

confidence: 99%

Google search volumes for portfolio management: performances and asset concentration

Maggi

Uberti

2019

Ann Oper Res

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“…Ongoing worldwide computerization provides great opportunities for scientists to enhance our understanding of the complex systems in which humans live today. Increasing availability of massive social media data abets efforts trying to explain collective behavior with methods stemming from the natural and computational sciences (Buldyrev et al, 2010;Goel et al, 2010;Golder and Macy, 2011;Heiberger, 2014Heiberger, , 2015King, 2011;Lazer et al, 2009;May et al, 2008;Schweitzer et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Installing computational social science: Facing the challenges of new information and communication technologies in social science

Heiberger

Riebling

2016

Methodological Innovations

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Today's world allows people to connect over larger distances and in shorter intervals than ever before, widely monitored by massive online data sources. Ongoing worldwide computerization has led to completely new opportunities for social scientists to conceive human interactions and relations in unknown precision and quantities. However, the large data sets require techniques that are more likely to be found in computer and natural sciences than in the established fields of social relations. In order to facilitate the participation of social scientists in an emerging interdisciplinary research branch of "computational social science," we propose in this article the usage of the Python programming language. First, we carve out its capacity to handle "Big Data" in suitable formats. Second, we introduce programming libraries to analyze large networks and big text corpora, conduct simulations, and compare their performance to their counterparts in the R environment. Furthermore, we highlight practical tools implemented in Python for operational tasks like preparing presentations. Finally, we discuss how the process of writing code may help to exemplify theoretical concepts and could lead to empirical applications that gain a better understanding of the social processes initiated by the truly global connections of the Internet era.

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“…Apart from the extensive research on market price analysis [1–7], return reversal has also attracted lots of attention. Bondt and Thaler [8, 9] initially documented long-term return reversal in the US stock market, indicating that stocks performed well in the past three to five years tend to have low future returns.…”

Section: Introductionmentioning

confidence: 99%

Identifying Key Drivers of Return Reversal with Dynamical Bayesian Factor Graph

et al. 2016

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In the stock market, return reversal occurs when investors sell overbought stocks and buy oversold stocks, reversing the stocks’ price trends. In this paper, we develop a new method to identify key drivers of return reversal by incorporating a comprehensive set of factors derived from different economic theories into one unified dynamical Bayesian factor graph. We then use the model to depict factor relationships and their dynamics, from which we make some interesting discoveries about the mechanism behind return reversals. Through extensive experiments on the US stock market, we conclude that among the various factors, the liquidity factors consistently emerge as key drivers of return reversal, which is in support of the theory of liquidity effect. Specifically, we find that stocks with high turnover rates or high Amihud illiquidity measures have a greater probability of experiencing return reversals. Apart from the consistent drivers, we find other drivers of return reversal that generally change from year to year, and they serve as important characteristics for evaluating the trends of stock returns. Besides, we also identify some seldom discussed yet enlightening inter-factor relationships, one of which shows that stocks in Finance and Insurance industry are more likely to have high Amihud illiquidity measures in comparison with those in other industries. These conclusions are robust for return reversals under different thresholds.

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Collective Attention and Stock Prices: Evidence from Google Trends Data on Standard and Poor's 100

Cited by 33 publications

References 39 publications

Google search volumes for portfolio management: performances and asset concentration

Google search volumes for portfolio management: performances and asset concentration

Installing computational social science: Facing the challenges of new information and communication technologies in social science

Identifying Key Drivers of Return Reversal with Dynamical Bayesian Factor Graph

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