The role of crowdfunding in promoting entrepreneurship

Crowdfunding is a collaborative initiative, usually via internet, where people network to collectively raise funds in order to invest in and support projects delivered by other people or organizations. Tools such as crowdfunding are born and thrive in a grassroots environment, with a strong potential to positively disrupt the entrepreneurial generation setting and grow to a position of significant relevance in society, namely at a time when alternatives to traditional forms of finance are welcome and the technology to deliver them is abundant. Entrepreneurship is the act of transforming ideas and projects into economic products or services. Entrepreneurship related to starting new businesses is better known as start‐up ventures. Entrepreneurs face a series of challenges, from idea conception and business plan design, to obtaining finance, promoting new products and services, generating revenues and profits and generally growing and sustaining a business for the long‐run. These challenges can be overwhelming, namely in the start‐up phase of a new venture, leaving several ideas on paper without them having a chance to “grow legs and walk”. This paper and its analysis offer important insights about the contribution of crowdfunding to facilitate the attainment of critical factors for successful entrepreneurship. With extensive use of real practical examples, leveraging previous analytical studies of other crowdfunding implications and reviewing expert literature, by interviewing entrepreneurs, crowdfunding platform owners and by benefitting from hands on experience of working in such an organization, we intend to clarify the impact of crowdfunding in what we considered to be 7 key entrepreneurial requirements detailed further in the introduction section and later in the body of the paper. The findings have implications for entrepreneurs, naturally, and for business generation theory, extending current entrepreneurial guidelines with innovative tools and methodologies capable of sustaining successful ventures in a newly highlighted cooperative world. We live in innovative times where the channels for the transfer of funds and resources suffer disruptive changes with the potential to significantly improve the ability to generate new initiatives for the well‐being of entrepreneurs and all related communities.

Previsão da estrutura temporal da taxa de juro na zona Euro : aproximação paramétrica e por métodos de aprendizagem automática

A estrutura temporal das taxas de juro, também conhecida por yield curve ou curva de rendimentos define a relação entre as taxas de juros e o prazo de vencimento (ou maturidades) dos investimentos feitos. Assim, o desenvolvimento de modelos que possibilitem a obtenção de previsões precisas sobre a estrutura temporal das taxas de juro e que permitam estudar a dinâmica da evolução das taxas de juro é de crucial importância em diversas áreas de financiamento. Neste estudo investigou-se a performance de diferentes métodos de previsão para obter a estrutura temporal das taxas de juro da Zona Euro, considerando o período entre 2009 e 2015. Em termos mais específicos, foi analisada a capacidade preditiva do modelo de Nelson-Siegel & Svensson assumindo que os parâmetros resultantes da estimação da especificação paramétrica podem ser modelizados através de métodos de séries temporais univariados (modelos ARIMA, Random walk) e multivariados (modelos VAR) e Redes Neuronais Artificiais (RNA) individuais e conjuntas. Os resultados deste estudo mostram que (i) as RNA com a previsão dos parâmetros em simultâneo exibem os valores de erro mais baixos para as maturidades de curto e médio prazo (3 meses a 5 anos); (ii) As RNAs individuais são melhores para prever as taxas de juro nas maturidades compreendidas entre os 7 e os 10 anos, e que (iii) para as maturidades de longo e muito longo prazo (15 e 30 anos respetivamente) deverá ser escolhido o modelo VAR(1). Estes resultados são robustos e consistentes para todos os horizontes de previsão analisados (1,2 e 3 meses). Contudo, no período analisado nenhum dos modelos testados apresenta valores de erro inferiores aos obtidos com o modelo Random Walk.