Google searches and stock market activity: Evidence from Norway

Kim, Neri; Lučivjanská, Katarína; Molnár, Péter; Villa, Roviel

doi:10.1016/j.frl.2018.05.003

Cited by 128 publications

(86 citation statements)

References 28 publications

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“…As demonstrated by Yao et al [4], there is a negative correlation between Google search interest and crude oil prices, which even still was shown not to be useful for forecasting purposes. Another recent study came to a similar conclusion; as demonstrated by Kim et al [5], Google search interest is not useful for predicting future abnormal returns on the Norwegian stock market. Other studies have examined the effects which exogenous variables such as news frequency have on the prices of financial securities.…”

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confidence: 58%

“…For all other stocks, the mean accuracy of the same three approaches is 74.0767% or less. Regarding this point, the work of Yao et al [4] and is invoked again, as well as that of Kim et al [5]. Both of these studies suggest that Google search interest is not useful for forecasting trends in prices or returns for various tradable securities including crude oil and stocks.…”

Section: Discussionmentioning

confidence: 98%

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A method of trend forecasting for financial and geopolitical data: inferring the effects of unknown exogenous variables

Jacaruso

2018

J Big Data

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IntroductionImproving the accuracy of forecasting methods is extremely valuable in a plethora of areas including (but not limited to) financial trading, as well as anticipating geopolitical patterns which are relevant to national policies and economies. This study proposes and examines the results of a new trend forecasting method for specific financial security price and asylum seeker datasets. By now, there have been many studies on ways to integrate exogenous variables into existing forecast models in areas such as electricity prices and migrant flows [1,2]. Others have shown the importance of forecasting migrant phenomenon, and AbstractThis paper intends to contribute to the field of trend forecasting by proposing a new forecasting approach for stock market prices and geopolitical time series data of economic, financial and geopolitical importance. Designing models which account for every possible exogenous variable of relevance to a time series in question can often be an onerous and impractical task. Instead, this paper explores a new method which uses periods of decreased significance in the variable of foremost importance as a window of opportunity to observe the possible effects other variables may be having in a general way for the purpose of trend forecasting. When the latter variables are too unquantifiable to be accounted for in a model, having the ability to nonetheless discern their overall influence can be useful for anticipating trend changes. The proposed method was used in conjunction with the existing method of exponential smoothing to generate forecasts. It was also applied alone and contrasted with the results of exponential smoothing when used separately. This paper specifically addresses the ability of the newly proposed method to forecast the upwards/downwards extrapolation of the weekly trend for 9 weeks on stock closing prices for five companies of interest (Apple Inc, Amazon.com Inc, General Electric Company, Intel Corporation, and Alcoa Corporation). It was also applied to forecasting the annual trend for 9 years of Afghan asylum seeker data. These differing areas were chosen in order to demonstrate applications in finance as well as international relations. The empirical results and 95% confidence intervals indicate a clear advantage when the newly proposed method is used both in conjunction with exponential smoothing and on its own. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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confidence: 58%

Section: Discussionmentioning

confidence: 98%

A method of trend forecasting for financial and geopolitical data: inferring the effects of unknown exogenous variables

Jacaruso

2018

J Big Data

View full text Add to dashboard Cite

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“…Our paper fits into a growing literature aimed at developing novel finance-related and macro-related uncertainty indicators based on the volume of internet searches and exploring their implications for financial and macroeconomic dynamics. In this respect, our work is most closely related to Da et al (2011Da et al ( , 2015, Dzielinski (2012), Donadelli (2015), Bijl et al (2016), Castelnuovo and Tran (2017), Bontempi et al (2017) and Kim et al (2018).…”

Section: Related Literature: a Focus On Google-search-based Uncertaintymentioning

confidence: 84%

“…Moreover, they develop a trading strategy based upon search volume indexes: the portfolio based upon this strategy is able to outperform the market portfolio, but not enough to outweigh the associated transaction costs. More recently, Kim et al (2018) focus on the Norwegian stock market, and build search volume indexes based on the names of listed companies. They find no statistical evidence of any relationship between rising interest in listed companies and associated stock returns, while they find weak but significant evidence of a contemporary relationship and, to a larger extent, also predictive ability of search volume indexes on volatility and trading volume.…”

Section: Related Literature: a Focus On Google-search-based Uncertaintymentioning

confidence: 99%

“…Differently from filtering the perception of sentiment through journalists' feelings, measuring sentiment and uncertainty via rising Google searches on specific finance-related and economicsrelated keywords has the advantage to intercept the actual behaviour of internet users, which (today) constitute a relevant portion of the population. Recent empirical studies going in this direction are: Da et al (2011Da et al ( , 2015, Dzielinski (2012), Donadelli (2015), Bijl et al (2016), Castelnuovo and Tran (2017), Bontempi et al (2017), Tran (2017) and Kim et al (2018).…”

Section: Introductionmentioning

confidence: 99%

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Non-macro-based Google searches, uncertainty, and real economic activity

Donadelli

Gerotto

2019

Research in International Business and Finance

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We propose a set of novel non-macro-based uncertainty indicators that rely on the frequency of Google searches (NM-GSIs) for the following health-, environmental-, security-, and politicalrelated topics: "Symptom", "Pollution", "Terrorism", and "Election". By means of VAR investigations, we document that an intensification of people interest in non-macro-based topics harms the US real economic activity. In particular, NM-GSI shocks generate (i) a significant drop in consumer credit and (ii) a mild decrease (increase) in production (unemployment) levels. Noteworthy, rising non-macro-based uncertainty is found to have stronger influence on the outstanding level of consumer credit than rising macro-based uncertainty. Our findings suggest that increasing interest in specific non-macro-based topics might be associated with raising people's anxiety. A battery of robustness checks confirms our main findings.

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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 searches and stock market activity: Evidence from Norway

Cited by 128 publications

References 28 publications

A method of trend forecasting for financial and geopolitical data: inferring the effects of unknown exogenous variables

A method of trend forecasting for financial and geopolitical data: inferring the effects of unknown exogenous variables

Non-macro-based Google searches, uncertainty, and real economic activity

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

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