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Narita, Futoshi; Yin, Rujun

doi:10.5089/9781484390177.001

Cited by 14 publications

References 46 publications

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Where should we go? Internet searches and tourist arrivals

Cevik

2020

Int. J Fin Econ

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The widespread availability of internet search data is a new source of high‐frequency information that can potentially improve the precision of macroeconomic forecasting, especially in areas with data constraints. This paper investigates whether travel‐related online search queries enhance accuracy in the forecasting of tourist arrivals to The Bahamas from the United States. The results indicate that Google Trends‐augmented forecast models improve forecast accuracy by about 30% compared to the traditional autoregressive integrated moving average (ARIMA) model and more than 20% compared to the multivariate model incorporating macroeconomic indicators.

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Where should we go? Internet searches and tourist arrivals

Cevik

2020

Int. J Fin Econ

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Big data tools for Islamic financial analysis

Mnif

Jarboui

Hassan

et al. 2020

Intell Sys Acc Fin Mgmt

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Summary Behavioural science states that emotions, principles and the manner of thinking can affect the behaviour of individuals and even investors in their decision making on financial markets. In this paper, we have tried to measure the investor sentiment by three means of big data. The first is based on a search query of a list of words related to Islamic context. The second is inferred from the engagement degree on social media. The last measure of sentiment is built, based on the Twitter API classified into positive and negative directions by a machine learning algorithm based on the naive Bayes method. Then, we investigate whether these sensations and emotions have an impact on the market sentiment and the price fluctuations by means of a vector autoregression model and Granger causality analysis. In the final step, we apply the agent‐based simulation by means of the sequential Monte Carlo method with the control of our Twitter measure on Islamic index returns. We show, then, that the three social media sentiment measures present a remarkable impact on the contemporaneous and lagged returns of the different Islamic assets studied. We also give an estimation of the parameters of the latent variables relative to the agent model studied.

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Massive Data Analytics for Macroeconomic Nowcasting

Cheng

Ferrara

Froidevaux³

et al. 2021

Data Science for Economics and Finance

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Nowcasting macroeconomic aggregates have proved extremely useful for policy-makers or financial investors, in order to get real-time, reliable information to monitor a given economy or sector. Recently, we have witnessed the arrival of new large databases of alternative data, stemming from the Internet, social media, satellites, fixed sensors, or texts. By correctly accounting for those data, especially by using appropriate statistical and econometric approaches, the empirical literature has shown evidence of some gain in nowcasting ability. In this chapter, we propose to review recent advances of the literature on the topic, and we put forward innovative alternative indicators to monitor the Chinese and US economies.

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In Search of Information:

Cited by 14 publications

References 46 publications

Where should we go? Internet searches and tourist arrivals

Where should we go? Internet searches and tourist arrivals

Big data tools for Islamic financial analysis

Massive Data Analytics for Macroeconomic Nowcasting

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