This article analyzes the application of crowdfunding to finance the production of music recordings. The analysis is based partially on a case study of a crowdfunding platform located in Poland (MegaTotal). The case study illustrates how pooling contributions of crowdfunders and redistributing risk makes it possible to release records that would not be released by a traditional recording company. The analysis of quantitative data shows that one of the most important factors for success in the process of capital accumulation via crowdfunding is the involvement of a significant number of backers who make repeated contributions to a project. Additional factors that may help a project succeed are also discussed, such as engagement in communication with potential backers and offering bonuses to contributors. In this way, crowdfunding helps overcome one of the structural problems of the traditional recording industry.

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Crowdfunding is an online collective action initiated by people or institutions to gather funds from a large number of contributors, usually using mediation of crowdfunding platforms to facilitate contact and flow of resources between parties. The success of Kickstarter and similar services shows that crowdfunding has great potential. This paper presents an empirical study of the crowdfunding phenomenon. It analyzes the motivation of individuals involved in supporting recording artists with voluntary payments in exchange for equity stakes. Specifically, the paper focuses on the motives driving individuals who use MegaTotal (http://www.megatotal.pl), a Polish crowdfunding platform, to contribute financial resources to selected musical projects. The analysis leads to the conclusion that individuals involved in crowdfunding are partly driven by motivation that has not been typical of fans in the history of popular music.

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In this paper, we argue that banks anticipate short‐term market rates when setting interest rates on loans and deposits. In order to include anticipated rates in an empirical model, we use two methods to forecast market rates—a level, slope, curvature model, and a principal components model—before including them in a model of retail rate adjustment for four retail rates in four major euro area economies. Using both aggregate data and data from individual French banks, we find a significant role for forecasts of market rates in determining retail rates; alternative specifications with futures information yield comparable results.