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Introduction/Main Objectives: The purpose of this paper is to examine the effect of the Google Search Volume Index (GSVI), as the moderating variable, on underpriced IPOs, as the independent variable, on the divergence of opinions, as the dependent variable. Background Problems: A divergence of opinions may arise when an error occurs while estimating the right price due to the unavailability of information or only having limited information. Before a company conducts an IPO, potential investors will look for information about the company and each one may interpret the data differently, which results in disagreements between the investors. The investors’ attention is a disagreement mechanism. Research Methods: This study employs the regression analysis of moderation variables with an absolute difference method (ADM) on a sample of 79 Indonesian companies that conducted an IPO between 2015 and 2019. Finding/Results: This study discovered a negative relationship between the initial return and market-adjusted turnover without an interaction effect in the model. The investors’ attention reduces disagreements about underpriced IPOs in the aftermarket. Conclusion: The result of this study found that investors’ attention reduces disagreements about underpriced IPOs proxied by the initial return, because investors closely monitor other information available on the Internet.

This study investigates whether the aggregate investor information demand for all stocks in a sector demonstrated in the Google search volume index (SVI) can predict the sector’s performance. The evidence shows that a sector’s abnormal SVI can predict the sector’s performance next month, even after controlling for the sector’s contemporaneous standardized unexpected earnings and lagged returns on both the market and the sector. Also found is a partial reversal in the sector’s long-run performance that is not completely consistent with the price pressure hypothesis. This indicates that some fundamental information about a sector can be captured by the sector’s abnormal SVI on a timely basis.

We propose a climate change attention (CCA) index based on Google search volume index (GSVI) from 2004 to 2021 and show that it is an economically and statistically significant negative predictor for next month’s energy stock returns. The index is extracted using principal component analysis (PCA), but the results are similar by using the equal-weighted average method. Compared with 14 traditional macroeconomic predictors, CCA performs the best and provides complementary information when added into bivariate and multivariate macro predictive models. When further considering the effect of CCA’s forecasting power over different periods, strong evidence is shown that this outperformance is especially prominent in economic depressions and down market conditions. From the asset allocation perspective, CCA can provide a mean-variance investor with significant economic gains under alternative risk aversions. Our empirical results prove that investors’ attention to climate change contains predictive information for excess returns of global traditional energy stock index.

We study the fundamental differences that separate: Litecoin; Bitcoin Gold; Bitcoin Cash; Ethereum; and Zcash from Bitcoin, and draw some analysis to how these features are appreciated by the market, to ultimately make an inference as to how future successful cryptocurrencies may be invented and behave. We use Google Trend data, as well as price, volume and market capitalization data sourced from coinmarketcap.com to support this analysis. We find that Litecoin’s shorter block times offer benefits in commerce, but drawbacks in the mining process through orphaned blocks. Zcash holds a niche use for anonymous transactions, benefitting areas of the world lacking in economic freedom. Bitcoin Cash suffers from centralization in the mining process, while the greater decentralization of Bitcoin Gold has generally left it to stagnate. Ether’s greater functionality offers the greatest threat to Bitcoin’s dominance in the market. A coin that incorporates several of these features can be technically better than Bitcoin, but the first-to-market advantage of Bitcoin should keep its dominant position in the market.

This research examines whether Google search volume index (GSVI), a proxy of investor attention, can predict the excess returns and abnormal trading volumes of TPEx 50 index constituents. It also explores the motive underlying GSVI based on positive or negative shocks to stock prices. The empirical data include 48 companies from TPEx 50 index constituents and cover a period from 1 September 2016 to 31 August 2019. The empirical results present that (1) lagged GSVI negatively affects current excess returns, perhaps due to the characteristics of TPEx, in which there are a higher proportion of retail investors, smaller listed companies, and a higher information asymmetry problem. (2) Lagged GSVI can positively affect abnormal current trading volumes. (3) If GSVI is driven by positive shocks, then it can predict excess returns and abnormal trading volumes positively.

PurposeThis study examines herd behavior in the cryptocurrency market at the aggregate level and the determinants of herd behavior, such as asymmetric market returns, the coronavirus disease 2019 (COVID-19) pandemic, 2021 cryptocurrency's bear market and the network effect.Design/methodology/approachThe authors applied the Google Search Volume Index (GSVI) as a proxy for the network effect. Since investors who are interested in a particular issue have a common interest, they tend to perform searches using the same keywords in Google and are on the same network. The authors also investigated the daily returns of cryptocurrencies, which are in the top 100 market capitalizations from 2017 to 2022. The authors also examine the association between return dispersion and portfolio return based on aggregate market herding model and employ interactions between herding determinants such as, market direction, market trend, COVID-19 and network effect.FindingsThe empirical results indicate that herding behavior in the cryptocurrency market is significantly captured when the market returns of cryptocurrency tend to decline and when the network effect of investors tends to expand (e.g. such as during the COVID-19 pandemic or 2021 Bitcoin crash). However, the results confirm anti-herd behavior in cryptocurrency during the COVID-19 pandemic or 2021 Bitcoin crash, regardless of the network effect.Practical implicationsThese findings help investors in the cryptocurrency market make more rational decisions based on their determinants since cryptocurrency is an alternative investment for investors' asset allocation. As imitating trades lead to return comovement, herd behavior in the cryptocurrency has a direct impact on the effectiveness of portfolio diversification. Hence, market participants or investors should consider herd behavior and its underlying factors to fully maximize the benefits of asset allocation, especially during the period of market uncertainty.Originality/valueMost previous studies have focused on herd behavior in the stock market. Although some researchers have recently begun studying herd behavior in the cryptocurrency market, the empirical results are inconclusive due to an incorrectly specified model or unclear determinants.

Does internet search intensity (ISI) for a company’s product affect the company’s stock returns? How about the ISI for its rival product? How does ISI for the company’s product affect the ISI for the rival and vice versa? How is the evolution and persistence of these effects over time? To answer these questions, this study examines three pairs of rival products: Apple’s iPhone versus Samsung Galaxy, Intel versus AMD processors, and Netflix versus Hulu. Guided by psychological and marketing theories, Vector Autoregressive models were constructed to estimate the effects of ISI for the rival products (1) on the stock returns, and (2) on each other. Results showed that (1) ISI for the products significantly impacts the stock returns, and (2) the effects of ISI for one product on the other are not only significant but also asymmetrical. This multidisciplinary study integrates marketing analytics and financial phenomena and thus contributes to multiple research streams. It also finds that companies’ stock returns can be affected by the consumer online information search, which is responsive to marketing activities. Thus, marketers stand to benefit from leveraging this study’s findings to elevate their role in enhancing one of their companies’ most critical performance metrics—stock returns.

This paper examines the causal relationship between global stock market performance and Google search volume index (SVI) surrounding the disastrous event of the coronavirus (COVID-19) outbreak. Based on 6,106 stock index-day observations of 71 countries during the period from 1 January 2020 to 29 May 2020, we find that both the SVI and the growth in confirmed cases lower the global stock market returns. Consistent with the information discovery theory, we find when the confirmed cases increase, retail investors search for more information, improving their returns on stock indices during the outbreak. Finally, our further instrumental-variable analysis shows that our results are unlikely confounded by endogeneity.

In this paper, I introduce a user’s guide to Google Trends, a service created by Google to make statistics about online searches available to everyone at no cost. I review the service’s advantages over conventional sources of data from a researcher’s point of view. I also cover the most important stages of a real estate study that employs online search statistics from Google in a step-by-step user’s guide. In the guide, I discuss how to compose and refine a list of search terms and how to access, download, process, and apply online search data in real estate research. I illustrate each step of an empirical real estate study. In the study, I test whether the intensity of online searches for specific keywords in metropolitan statistical areas (MSAs) can help to forecast future arson incidents in those areas. The findings reveal that lagged searches for “foreclosure” are significantly positively associated with the number of arson incidents in the same MSA where online searches have been conducted. The findings also show that lagged searches for “arson,” “restructuring,” and “strategic default” are negatively related to the number of intentional property fires.

Purpose The purpose of this paper is to examine whether internet search intensity, as captured by Google’s search volume index (SVI), predicts house price changes in an emerging market like India. Design/methodology/approach Using data on Google’s SVI for four Indian cities and their corresponding house price index values, the authors examine whether abnormal SVI (growth in search intensity normalized by the national average) impacts abnormal house prices (house price change normalized by the national average). Findings Like developed markets such as the USA, the authors find that internet search intensity strongly predicts future house price changes. A simple rebalancing strategy of buying a representative house in the city with the greatest change in search intensity and selling a representative house in the city with the smallest change in search intensity each quarter yields an annualized excess (over risk-free government T-bills) return of 4 percent. Originality/value Emerging markets have low internet penetration and high information asymmetry with a dominant unorganized real estate market. The results are interesting as it sheds light on the nature and role of the internet as an infomediary even in emerging markets