[EN] Recent literature shows an increasing interest in considering alternative sources of information for predicting Small and Medium Enterprises default. The usage of accounting indicators does not allow to completely overcome the information opacity that is one of the main barriers preventing these firms from accessing to credit. This complicates matters both for private lenders and for public institutions supporting policies. In this paper we propose corporate websites as an additional source of information, ready to be exploited in real-time. We also explore the joint use of online and offline data for enhancing correct prediction of default through a Kernel Discriminant Analysis, keeping the Logistic Regression and the Random Forests as benchmark. The obtained results shed light on the potentiality of these new data when accounting indicators lead to a wrong prediction., This work was supported by grant PID2019-107765RB-I00 (funded by MCIN/AEI/10.13039/501100011033). The Open Access publishing option was funded by the Ca Foscari University of Venice, Italy.

[EN] We propose the use of online indicators, scraped from the firms¿ websites, to predict default risk for a sample of Spanish firms via nonlinear discriminant analysis and the logistic regression model., This work was partially supported by the Ca' Foscari University of Venice, Italy and by Agencia Estatal de Investigacion, Spain under grant PID2019107765RBI00. We also acknowledge helpful comments by an anonymous referee.

[EN] Google Trends reports the evolution of the popularity of internet searches. Its main output is the Search Volume Index (SVI), a relative measure of the popularity of a term computed using a sample of the searches. Due to the sampling, the SVI series are not entirely consistent, as the same query produces different results that can widely change from day to day. This paper investigates the nature of these inconsistencies by modeling and simulating the data-generating process. Simulations are applied to describe how a typical time series is distorted due to the sampling process and to quantify how averaging extractions smoothes the series. Finally, a relationship between term popularity, series dispersion, and the averaged extractions is derived, so recommendations for constructing consistent SVIs can be provided., This work was partially supported by grants PID2019-107765RBI00, PID2023-152106OB-I00, PEJ2018-003267-A-AR and CIAICO/
2023/272, funded by MCIN/AEI/10.13039/501100011033, ESF Investing in your future , and the Generalitat Valenciana.

[EN] In the European traditional manufacturing sector (TMS), there is a vigorous debate concerning the real impact of international trade liberalization on firm profitability. Based on firm-level data from five European countries, we analyze how location, productive subsector, and firm characteristics are associated with the profitability of the European textile-clothing industry in the pre- and post-trade liberalization period. Our results reveal that the externalities derived from geographical proximity were diluted after international trade liberalization. Moreover, larger companies and those that focus on high value-added products, as represented by Northern European firms, show a stronger association with profitability. Accordingly, this observation calls into question the future of some TMS activities in a globalized world. Therefore, manufacturing strategies and industrial policies should be location- and context-specific.

[EN] Google Trends is a valuable tool for measuring popularity since it collects a large amount of information related to Google searches. However, Google Trends has been underused by sports analysts. This research proposes a novel method to calculate several popularity indicators for predicting players¿ market value. Google Trends was used to calculate six popularity indicators by requesting information about two football players simultaneously and creating popularity layers to compare players of unequal popularity. In addition, as the main idea is to obtain the popularity indicators of all players on the same scale, a cumulative conversion factor was used to rescale these indicators. The results show that the proposed popularity indicators are essential to predicting a player¿s market value. In addition, using the proposed popularity indicators decreases the transfer fee prediction error for three different models that are fitted to the data using the multiple linear regression, random forest, and gradient boosting machine methods. The popularity indicator Min, which is a robust reflection of the popularity that represents a player¿s popularity during the periods when they are less popular, is the most important popularity indicator, with a significant effect on the market value. This research provides practical guidance for developing and incorporating the proposed indicators, which could be applied in sports analytics and in any study in which popularity is relevant., P.M-S. Economic support through the FPI-UPV scholarship (PAID-01-19) to the Universitat Politecnica de Valencia. http://www.upv.es/entidades/VINV/indexc.html P.M-S, A.D., and J.D. Grant PID2019-107765RB-I00, funded by MCIN/AEI/10.13039/501100011033. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

[EN] We develop a unifying framework to investigate the effects of firms' internet presence on productivity and market structure. Using information on website adoption as an indicator of online trading, we treat the decision of entering an e-commerce market equivalent to the decision of entering a foreign market. Our theoretical framework draws from a dynamic model of international trade, which accounts for firms' heterogeneity in productivity levels and in the returns to productivity enhancing investments. We test the predictions of our model using UK and Spanish company account data, over the 1995¿2010 period merged with information of companies' online status. The period analysed is associated with the early stage of internet diffusion and our sample countries represent fast (the UK) and slow (Spain) diffusion. Our results show that website adoption is associated with higher productivity growth and with a reduction in market concentration in both countries. The increase in competition operates via a negative selection mechanism, whereby productivity growth is inversely related to the pre-entry productivity levels. We also find that productivity gains decline over time., Josep Domènech acknowledges that this research was partially funded by MCIN/AEI/10.13039/501100011033 under grant PID2019- 107765RB-I00

[EN] Google Trends (GT) has become a popular data source among researchers in a wide variety of fields. In economics, its main use has been to forecast other economic variables such as tourism demand, unemployment or sales. This paper questions the quality of these data by discussing the main data quality aspects according to the literature. Our analysis evidences some non-negligible issues related to the measurement accuracy of GT, which potentially affects the results obtained with GT data and therefore the decisions made with this information. These issues are illustrated with an example in which some queries to GT are repeated on six different days., This work was supported by the Ministry of Finance, Industry and Competitiveness and the European Social Fund [PEJ2018-003267-A-AR]; Agencia Estatal de Investigacion [PID2019-107765RB-I00].