Google search and stock returns: A study on BIST 100 stocks

Ekinci, Cumhur; Bulut, Ali Eray

doi:10.1016/j.gfj.2020.100518

Cited by 28 publications

(29 citation statements)

References 27 publications

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“…Individual investors access information via Google; however, corporate investors use the news resources such as Bloomberg and Reuters (Da et al, 2011, Da et al, 2015, Kostopoulos et al, 2020. The fastest and most common platform for sharing information is search engines such as Google (Ekinci & Bulut, 2020;Breitmayer et al, 2019). Therefore, Google search volume (G.S.V.)…”

Section: Literature Examining Investor Sentiment and Google Searchesmentioning

confidence: 99%

Predicting financial market returns in the presence of health crisis: evidence from conventional and Islamic stock markets

Tuna

2021

Economic Research-Ekonomska Istraživanja

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The purpose of this study is to examine the usability of the news about the COVID-19 outbreak as a predictor in financial markets. Index values of 11 different sectors in conventional and Islamic stock markets and the index values obtained from COVID-19 deaths, COVID-19 cases and health news were used for this purpose. News variables indices were calculated through Google search volume (G.S.V.) values obtained from Google trend. The daily data between 19 March 2020 and 27 July 2020 were used in the study for 25 index values in total. Regression analysis was used in the study. According to the study results, COVID-19 deaths, COVID-19 cases and health news used as predictors have higher performance than historical return values in all sectors of both conventional and Islamic financial markets. In addition, Islamic stock markets show more attention to the news about the COVID-19 outbreak than conventional stock markets. Accordingly, COVID-19 deaths, COVID-19 cases and health news can be used as effective predictors in Islamic financial markets.

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Section: Literature Examining Investor Sentiment and Google Searchesmentioning

confidence: 99%

Predicting financial market returns in the presence of health crisis: evidence from conventional and Islamic stock markets

Tuna

2021

Economic Research-Ekonomska Istraživanja

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“…Variable measurement and data collection Following Bijl et al (2016), Swamy and Dharani (2019), and Ekinci and Bulut (2021), we collected weekly data for the analysis. Weekly data are appropriate for our study because they have been available for several years, are not disrupted by the holidays, and match the raw data from Google Trends.…”

Section: Methodsmentioning

confidence: 99%

“…On the basis of the searched keyword volume recorded by the Google Trend tool within their study period, Bijl et al (2016) and Nguyen et al (2019) implied that Google searches negatively affect stock returns. In contrast, Ekinci and Bulut (2021) and Swamy and Dharani (2019) argued that the relationship between Google searches and stock returns is positive. Therefore, Google search can predict stock price movements, although the empirical results are not consistent.…”

Section: Introductionmentioning

confidence: 91%

“…Regarding the relationship between Google search and stock returns, we selected the highquality articles of Bijl et al (2016), Nguyen et al (2019), Ekinci and Bulut (2021), and Swamy and Dharani (2019). Bijl et al (2016) collected weekly data of listed companies in the S&P 500 and the search volume of company names from Google Trends from 2007 to 2013 to investigate the relationship between Google searches and stock returns.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, no evidence showed whether Google search volume has a significant relationship with stock returns in Indonesia and Malaysia. Ekinci and Bulut (2021) collected weekly data on BIST 100 stocks and Google Trends from 2012 to 2017 to investigate the relationship between Google search and stock at Borsa Istanbul. Using the Fama-French three-factor model and stock-level regressions, they explored whether a link exists between Google search and stocks.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Relationship between Google search and the Vietcombank stock

Pham

Hoang

Popesko

et al. 2021

JEECAR

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The overall objective of this research is to analyze the financial condition of failing companies prior to bankruptcy, in comparison with non-failing companies, which are matched on the industry, size, and time-period. The sample consists of 168 SMEs from the wholesale and retail industry, whose financial statements were analyzed for the 2011-2015 period. The analysis is primarily based on the ratio analysis and the models developed for bankruptcy prediction and financial statement manipulation. Mann-Whitney U test is used to compare differences between failing and non-failing SMEs for a set of twenty variables. Research findings indicate that there is a significant difference between failing and non-failing SMEs, especially in accruals, asset quality, leverage, profitability, and liquidity. For the very first time in the transition economy of CEE Bosnia and Herzegovina, the pre-bankruptcy behavior of failing SMEs is analyzed, providing insights into potentially manipulated areas, which represent the main contribution of the research.

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Stock‐induced Google trends and the predictability of sectoral stock returns

Salisu

Ogbonna

Adediran

2020

Journal of Forecasting

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In this paper, we consider Google trends (G-trends) as a measure of investors' attention in the predictability of stock returns across eleven major US sectors. The theoretical motivation for our paper is clear. In seeking information to guide investment decisions, investors' sentiments are shaped by news such as G-trends that could induce changes in the prices of stocks. Thus, we construct a predictive model that incorporates G-trends series as a predictor of stock returns and thereafter we account for evident asymmetry in G-trends to analyze the signficance of positive-and negative-worded news in the predictability of stock returns. We also compare single-and multi-factor predictive models augmented with distinctive statistical effects against the baseline time series model. We highlight three key findings: (1) G-trends record consistent negative correlations with stock returns across sectors. ( 2) The proposed predictive model with G-trends outperforms the baseline (random walk) model. (3) The inclusion of asymmetry and macroeconomic variables improves the outperformance of G-trends over the baseline model.

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Google search and stock returns: A study on BIST 100 stocks

Cited by 28 publications

References 27 publications

Predicting financial market returns in the presence of health crisis: evidence from conventional and Islamic stock markets

Predicting financial market returns in the presence of health crisis: evidence from conventional and Islamic stock markets

Relationship between Google search and the Vietcombank stock

Stock‐induced Google trends and the predictability of sectoral stock returns

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