Success factors of initial coin offerings

A survey of deep learning applications in cryptocurrency

This study aims to comprehensively review a recently emerging multidisciplinary area related to the application of deep learning methods in cryptocurrency research. We first review popular deep learning models employed in multiple financial application scenarios, including convolutional neural networks, recurrent neural networks, deep belief networks, and deep reinforcement learning. We also give an overview of cryptocurrencies by outlining the cryptocurrency history and discussing primary representative currencies. Based on the reviewed deep learning methods and cryptocurrencies, we conduct a literature review on deep learning methods in cryptocurrency research across various modeling tasks, including price prediction, portfolio construction, bubble analysis, abnormal trading, trading regulations and initial coin offering in cryptocurrency. Moreover, we discuss and evaluate the reviewed studies from perspectives of modeling approaches, empirical data, experiment results and specific innovations. Finally, we conclude this literature review by informing future research directions and foci for deep learning in cryptocurrency.

Expanding corporate finance perspectives to equity crowdfunding

In equity crowdfunding, firms raise capital online from a large pool of heterogeneous investors, thereby providing primary market opportunities similar to traditional public markets, such as initial public offerings. The development of secondary markets is instead still limited, making the post-offering perspectives of both crowdfunding investors and firms closer to private equity deals. We believe that equity crowdfunding markets provide an interesting setting where to test existing corporate finance and financial economics theories, as well as to develop new theoretical insights. Relatedly, our understanding of crowdfunding mechanisms can largely benefit from increased attention from finance scholars. This paper and special issue are an attempt in this direction.