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The aim of this paper is to evaluate the performance of climate-themed mutual funds, taking into consideration the structure of their asset allocation, especially their geographical focus of investment. Additionally, the influence of differences in the industry allocations on the funds’ investment results is evaluated. Our analyses are based on climate-friendly mutual funds registered in Scandinavian countries (i.e., Denmark, Norway, and Sweden) during 2010–2019. To classify the analyzed funds as climate-friendly, we use the relative carbon footprint measure, which is a novelty in studies on ESG funds (meeting the environmental, social, and corporate governance criteria). In our analyses, we use the absolute performance as well as the relative performance methods. The obtained results confirm that geographical and industrial asset allocations significantly affect the performance of Scandinavian climate-friendly funds. On the basis of our studies, we may state that analyzed mutual funds do not get abnormal returns, and their performance mostly depends on the market state. Additionally, the unconditional firm size factor did not influence the return of particular portfolios, while the conditional firm size factor was significant for European, global, and North American funds. Moreover, the firm value factor was significant. Finally, the momentum factor was only significant for the emerging markets portfolio when it reached positive values.

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Background. This study examines the dividend patterns among companies listed on the stock exchanges in selected countries from Europe, the Middle East, and Africa (EMEA), namely the Czech Republic,

The aim of this article is to analyze and evaluate the usability of discriminant models in predicting bankruptcy for companies listed on NewConnect. This market was established in 2007 and operates as an alternative trading system next to Warsaw Stock Exchange S.A., which in practice means that its regulatory regime in relation to issuers and listed companies is not as strict as the one applicable to the main market, therefore shares of small and medium-size businesses, including start-ups, can be listed on NewConnect. In this paper, discriminant models are used to analyse the financial situation of four companies removed from trading on NewConnect due to bankruptcy, Perfect Line S.A., Promet S.A., InwazjaPC S.A. and Budostal-5 S.A. The analysis is based on three models: Altman's model for emerging markets, as well as two models of the highest predictive ability according to P. Antonowicz's research, Z7INEPAN model developed in the Polish Academy of Sciences and E. Mączyńska's model, developed by Polish scientists and adapted to the Polish economy. The results confirm that these models are a valuable tool in assessing the financial condition of enterprises and allow for bankruptcy forecasting. Their application to companies listed on NewConnect, however, may be limited due to the specific profile of these entities as most of these enterprises are in fact newly formed and therefore the existing empirical data may prove  insufficient.