Forecasting Financial Distress With Machine Learning – A Review

Purpose – Evaluate the various academic researches with multiple views on credit risk and artificial intelligence (AI) and their evolution.Theoretical framework – The study is divided as follows: Section 1 introduces the article. Section 2 deals with credit risk and its relationship with computational models and techniques. Section 3 presents the methodology. Section 4 addresses a discussion of the results and challenges on the topic. Finally, section 5 presents the conclusions.Design/methodology/approach – A systematic review of the literature was carried out without defining the time period and using the Web of Science and Scopus database.Findings – The application of computational technology in the scope of credit risk analysis has drawn attention in a unique way. It was found that the demand for identification and introduction of new variables, classifiers and more assertive methods is constant. The effort to improve the interpretation of data and models is intense.Research, Practical & Social implications – It contributes to the verification of the theory, providing information in relation to the most used methods and techniques, it brings a wide analysis to deepen the knowledge of the factors and variables on the theme. It categorizes the lines of research and provides a summary of the literature, which serves as a reference, in addition to suggesting future research.Originality/value – Research in the area of Artificial Intelligence and Machine Learning is recent and requires attention and investigation, thus, this study contributes to the opening of new views in order to deepen the work on this topic

Is the Value Relevance relevant?

A adoção das IFRS pelas companhias de capital aberto no Brasil trouxe a discussão se as referidas normas melhoraram a qualidade das informações contábeis com efeito destas no preço das ações. Os estudos empíricos atualmente demandam uma nova análise dos resultados encontrados sobre o value relevance do lucro líquido (LL) e do Patrimônio Líquido (PL). Foi realizada uma meta-análise dos estudos publicados sobre o tema, examinando separadamente os modelos de preço e retorno propostos por Ohlson (1995). A amostra constituiu-se de 26 artigos, que analisaram empresas brasileiras no período de 1997 a 2014, totalizando 18.562 observações consideradas pelos artigos que testaram o período pré IFRS e 17.381 observações utilizadas nos testes pós IFRS, para os modelos de preço e retorno. Os resultados indicaram queda do value relevance do PL e ganho na relevância da informação do LL após a adoção das IFRS.The IFRS adoption by listed companies in Brazil brought the discussion on whether these standards have improved the quality of accounting information through their effect on stock prices. The empirical studies demands a renewed analysis of the accumulated evidences on the value relevance of net income (NI) and shareholders' equity (SE). A meta-analysis of the published studies on the subject was carried out, examining separately the price and return models proposed by Ohlson (1995). The sample consisted of 26 articles that analyzed Brazilian companies from 1997 to 2014, covering 18,562 observations for the pre-IFRS period, and about 17,381 observations for the post IFRS, for the price and return model. The results evidenced a decline in the value relevance of the SE and the gain in the relevance of the NI after the adoption of IFRS

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora