C51 - Model Construction and EstimationReturn

This article examines whether investing in ESG impacts banks’ stock returns, aiming to illustrate the value generated by ESG investment in the MSCI Emerging Markets Index. The fiscal year-end data of thirty-five financial companies within the Index, covering the period from December 31, 2015, to December 31, 2022, have been utilised. The analysis employs a pooled panel regression model utilising robust least squares estimation. Firm-specific and market-specific variables are used as independent variables. We have observed a significant positive direct relationship between the social pillar score and stock returns. Banks may initiate investments in social pillars in the Index. In addition, firm-specific variables such as market capitalisation, return on equity, capital adequacy, and price-earnings ratio influence the relationship between ESG pillar scores and stock returns. We recommend that portfolio managers closely monitor improvements in ESG pillar scores alongside firm-specific variables to predict banks’ stock returns in the index.

Meteorological conditions exert a considerable influence on traffic patterns. This paper examines the influence of meteorological variables on the daily number of traffic accidents requiring fire brigade intervention. The influence of meteorological variables, including maximum temperature, wind speed, air pressure, precipitation, snow cover and sunshine, was examined. A Generalized Additive Model for variables with a Poisson distribution was employed for modelling purposes, as this allows for the representation of non-linear dependencies. The analysis demonstrates that the lowest incidence of accidents occurs at temperatures approximating 10 °C. The average daily number of accidents increases with windy weather, the minimum number of accidents occurs at zero precipitation, and the accident rate rises with higher levels of sunshine. In the Czech Republic, the period of greatest risk in terms of road traffic accidents is the summer and winter months. The findings may have several practical applications, for example, in the improvement of meteorological warnings in traffic.

This paper proposes a straightforward and intuitive computational mechanism for the economic adjustment of default probabilities, allowing the extension of the original (usually one-year) probability of default estimates for more than one period ahead. The intensity of economic adjustment can be flexibly modified by setting the appropriate weighting parameter. The proposed mechanism is designed to be useful especially in the context of lifetime expected credit losses calculation within the IFRS 9 requirements.