Forecasting stock market movements using Google Trend searches

Huang, Yingyu; Rojas, Randall R.; Convery, Patrick D.

doi:10.1007/s00181-019-01725-1

Cited by 62 publications

(36 citation statements)

References 42 publications

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“…Penggunaannya menjadi signifikan, meskipun datanya sederhana. Studi menjelaskan dalam penelitiannya mengevaluasi kemampuan prediksi data pencarian internet dengan menggunakan google dengan proxy menggunakan google trend menghasilkan bahwa hasilnya 40 persen lebih baik untuk menentukan pergerakan positif atau negatif sebuah objek dibandingkan dengan menggunakan data secara actual [5].…”

Section: Pendahuluanunclassified

Forecasting the Existence of Chocolate with Variation and Seasonal Calendar Effects Using the Classic Time Series Approach

Diksa

2022

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Chocolate is the raw material for making cakes, so consumption of chocolate also increases on Eid al-Fitr. However, this is different in the United States where the tradition of sharing chocolate cake is carried out on Christmas. To monitor the existence of this chocolate can be through the movement of data on Google Trends. This study aims to predict the existence of chocolate from the Google trend where the use of chocolate by the community fluctuates according to the calendar variance and seasonal rhythm. The method used is classic time series, namely nave, double exponential smoothing, multiplicative decomposition, addictive decomposition, holt winter multiplicative, holt winter addictive, time series regression, hybrid time series, ARIMA, and ARIMAX. Based on MAPE in sample, the best time series model to model the existence of chocolate in Indonesia is ARIMAX (1,0,0) while for the United States it is Hybrid Time Series Regression-ARIMA(2,1,[10]). For forecasting the existence of chocolate in Indonesia, the best models in forecasting are ARIMA (([11],[12]),1,1) and Naïve Seasonal. In contrast to the best forecasting model for the existence of chocolate in the United States, namely Hybrid Naïve Seasonal-SARIMA (2,1,0)(0,0,1)12 Hybrid Time Series Regression- ARIMA(2,1,[10]), Time Series Regression, Winter Multiplicative, ARIMAX([3],0,0).

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Section: Pendahuluanunclassified

Forecasting the Existence of Chocolate with Variation and Seasonal Calendar Effects Using the Classic Time Series Approach

Diksa

2022

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“…Solusi atas masalah ini dapat diatasi dengan menggunakan suatu leading sebagai leading indicator terkini yang digunakan sebagai eksogen dalam model ARIMAX, untuk mendapatkan prediksi TPK pada bulan berjalan dan yang akan datang, sehingga kondisi sektor perhotelan terkini dapat diketahui karena TPK salah satu proxy output yang dihasilkan sektor perhotelan. Adapun model yang menggunakan data google trends sebagai variabel eksogen menunjukkan hasil prediksi yang relatif baik (Anggraeni dan Aristiani, 2016;Fajar et al, 2020;Huang et al, 2019;Woo dan Owen, 2019).…”

Section: Pendahuluanunclassified

Peramalan Tingkat Penghunian Kamar Dengan Memanfaatkan Data Google Trends Di Provinsi Banten

Fajar¹,

Madinah²,

Prayitno³

2021

SCI TECH ED MATH

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The purpose of this paper is to forecast the room occupancy rate (ROR) of star hotels by utilizing Google Trends data, in addition to historical ROR data. The data used is the ROR (%) which comes from the Badan Pusat Statistik-Statistics Indonesia and the Google Trends query index using the search word "hotel". The method used is the ARIMAX model. The results of this study indicate that the ARIMAX model has excellent forecasting capabilities. This is shown by the MAPE value which reached 6.541%, which means the added value of the hotel sector in the first quarter of 2021 is not expected to increase significantly.

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“…Their empirical results show that there exist a strong co-movement between the realized volatility and investors' attention. Huang et al (2019) study indicates that the directional movement of S&P 500 stock index due to changes in search volume is dependent on the specific term being searched for and the sentiment itself. Their study also validates GT as a measure of investors' attention.…”

Section: Introductionmentioning

confidence: 99%

Investors’ attention: does it impact the Nigerian stock market activities?

Osabuohien-Irabor¹

2020

JED

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PurposeThe author investigates whether investors’ online information demand measured by Google search query and the changes in the numbers of Wikipedia page view can explain and predict stock return, trading volume and volatility dynamics of companies listed on the Nigerian Stock Exchange.Design/methodology/approachThe multiple regression model which encompasses both the univariate and multivariate regression framework was employed as the research methodology. As part of our pre-analysis, we test for multicollinearity and applied the Wu/Hausman specification test to detect whether endogeneity exist in the regression model.FindingsWe provide novel and robust evidence that Google searches neither explain the contemporaneous nor predict stock return, trading volume and volatility dynamics. Similarly, results also indicate that trading volume and volatility dynamics have no relationship with changes in the numbers of Wikipedia pages view related to stock activities.Originality/valueThis study opens new strand of empirical literature of “investors' attention” in the context of African stock markets as empirical evidence. No evidence from previous studies on investors' attention exist, whether in Google search query or Wikipedia page view, with respect to African stock markets, particularly the Nigerian stock market. This study seeks to bridge these knowledge gaps by examining these relations.

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Forecasting stock market movements using Google Trend searches

Cited by 62 publications

References 42 publications

Forecasting the Existence of Chocolate with Variation and Seasonal Calendar Effects Using the Classic Time Series Approach

Forecasting the Existence of Chocolate with Variation and Seasonal Calendar Effects Using the Classic Time Series Approach

Peramalan Tingkat Penghunian Kamar Dengan Memanfaatkan Data Google Trends Di Provinsi Banten

Investors’ attention: does it impact the Nigerian stock market activities?

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