Nowcasting with Google Trends : a keyword selection method

Search engines, such as Google, keep a log of searches entered into their websites. Google makes this data publicly available with Google Trends in the form of aggregate weekly search term volume. Aggregate search volume has been shown to be able to nowcast (i.e. compute real-time assessment of current activity) a variety of variables such as influenza outbreaks, financial market fluctuations, unemployment and retail sales. Although identifying appropriate keywords in Google Trends is an essential element of using search data, the recurring difficulty identified in the literature is the lack of a technique to do so. Given this, the main goal of this paper is to put forward a method (the "backward induction method") of identifying and extracting keywords from Google Trends relevant to economic variables

Nowcasting With Google Trends in an Emerging Market

Most economic variables are released with a lag, making it difficult for policy-makers to make an accurate assessment of current conditions. This paper explores whether observing Internet browsing habits can inform practitioners about real-time aggregate consumer behavior in an emerging market. Using data on Google search queries, we introduce a simple index of interest in automobile purchases in Chile and test whether it improves the fit and efficiency of nowcasting models for automobile sales. We also examine to what extent our index helps us identify turning points in sales data. Despite relatively low rates of Internet usage among the population, we find that models incorporating our Google Trends Automotive Index outperform benchmark specifications in both in-sample and outof- sample nowcasts while providing substantial gains in information delivery times.

Nowcasting with Google Trends, the more is not always the better

[EN] National accounts and macroeconomic indicators are usually published with a consequent delay. However, for decision makers, it is crucial to have the most up-to-date information about the current national economic situation. This motivates the recourse to statistical modeling to “predict the present”, which is referred to as “nowcasting”. Mostly, models incorporate variables from qualitative business tendency surveys available within a month, but forecasters have been looking for alternative sources of data over the last few years. Among them, searches carried out by users on research engines on the Internet – especially Google Trends – have been considered in several economic studies. Most of these exhibit an improvement of the forecasts when including one Google Trends series in an autoregressive model. But one may expect that the quantity and diversity of searches convey far more useful and hidden information. To test this hypothesis, we confronted different modeling techniques, traditionally used in the context of many variables compared to the number of observations, to forecast two French macroeconomic variables. Despite the automatic selection of many Google Trends, it appears that forecasts’ accuracy is not significantly improved with these approaches.Combes, S.; Bortoli, C. (2016). Nowcasting with Google Trends, the more is not always the better. En CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics. Editorial Universitat Politècnica de València. 15-22. https://doi.org/10.4995/CARMA2016.2015.4226OCS152

Using aircraft location data to estimate current economic activity

Aviation is a key sector of the economy, contributing at least 3% to gross domestic product (GDP) in the UK and the US. Currently, airline performance statistics are published with a three month delay. However, aircraft now broadcast their location in real-time using the Automated Dependent Surveillance Broadcast system (ADS-B). In this paper, we analyse a global dataset of flights since July 2016. We first show that it is possible to accurately estimate airline flight volumes using ADS-B data, which is available immediately. Next, we demonstrate that real-time knowledge of flight volumes can be a leading indicator for aviation’s direct contribution to GDP in both the UK and the US. Using ADS-B data could therefore help move us towards real-time estimates of GDP, which would equip policymakers with the information to respond to shocks more quickly

Can Google Trends search queries contribute to risk diversification?

Portfolio diversification and active risk management are essential parts of financial analysis which became even more crucial (and questioned) during and after the years of the Global Financial Crisis. We propose a novel approach to portfolio diversification using the information of searched items on Google Trends. The diversification is based on an idea that popularity of a stock measured by search queries is correlated with the stock riskiness. We penalize the popular stocks by assigning them lower portfolio weights and we bring forward the less popular, or peripheral, stocks to decrease the total riskiness of the portfolio. Our results indicate that such strategy dominates both the benchmark index and the uniformly weighted portfolio both in-sample and out-of-sample.Comment: 11 pages, 3 figure

The Great Recession and the Great Depression: Reflections and Lessons

The Great Recession that started in 2008 has drawn constant comparisons with the Great Depression that unfolded in 1929. This paper documents how the response of policy makers in the current episode has been markedly different from the one observed in the 1920s and 1930s and to what extent this different behavior has followed the lessons from the historical analysis of the Great Depression. The historical account is also used to discuss probable changes to the world’s economic landscape regarding both trade and financial globalization.

Simulating the inconsistencies of Google Trends data

[EN] Google Trends (GT) allows users to obtain reports of the evolution of the popularity of searchers made through the Google Search engine. Its main output is the Search Volume Index (SVI), a relative measure of the popularity of a term, which is computed using a sample of the searches. Due to the sampling error, the reports are not completely consistent, as the same query produces different time series that can widely change from day to day. This paper simulates the process of generating the SVI time series in the same way as GT does. By doing this, it has been shown that the sampling error could be an important issue if the popularity of the term under study is relatively low. Averaging multiple extractions from GT can only partially alleviate this.This work was partially supported by grants PID2019-107765RB-I00 and funded by MCIN/AEI/10.13039/501100011033.Cebrián, E.; Doménech I De Soria, J. (2022). Simulating the inconsistencies of Google Trends data. En 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. 229-235. https://doi.org/10.4995/CARMA2022.2022.1509322923