While equity crowdfunding provides ventures with an opportunity to collect funding from a large base of investors, many campaigns tend to remain unsuccessful. We draw from two fields of financing adjacent to equity crowdfunding, venture capital (VC) and angel investing, as well as rewards-based crowdfunding, to develop an understanding of the drivers of investment decisions in equity crowdfunding. Using data from a leading equity crowdfunding platform in Northern Europe, we explore factors that drive the number of investors and amount of funding attracted by equity crowdfunding campaigns. The results suggest that the investment decision criteria traditionally used by VCs or business angels are not of prime importance for success in equity crowdfunding. Instead, success is related to pre-selected crowdfunding campaign characteristics and the utilization of private and public networks. The findings are relevant for the decision making of entrepreneurs and crowdfunding platforms, as both parties benefit from campaign success. Many crowdfunding campaigns tend to remain unsuccessful.We develop an understanding of the success drivers underlying equity crowdfunding campaigns.We find that the criteria used by VCs or business angels are not of prime importance for success in equity crowdfunding.Success is instead related to pre-selected crowdfunding campaign features and the utilization of private and public networks.The findings can support the decision making of entrepreneurs and crowdfunding platforms, as both aim for campaign success.

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Success drivers of online equity crowdfunding campaigns

Instance-based credit risk assessment for investment decisions in P2P lending

From the wisdom of crowds to my own judgment in microfinance through online peer-to-peer lending platforms

Predicting whether a borrower will default on a loan is of significant concern to platforms and investors in online peer-to-peer (P2P) lending. Because the data types online platforms use are complex and involve unstructured information such as text, which is difficult to quantify and analyze, loan default prediction faces new challenges in P2P. To this end, we propose a default prediction method for P2P lending combined with soft information related to textual description. We introduce a topic model to extract valuable features from the descriptive text concerning loans and construct four default prediction models to demonstrate the performance of these features for default prediction. Moreover, a two-stage method is designed to select an effective feature set containing both soft and hard information. An empirical analysis using real-word data from a major P2P lending platform in China shows that the proposed method can improve loan default prediction performance compared with existing methods based only on hard information.

Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6788&context=lkcsb_research

Optimal Stackelberg strategies for financing a supply chain through online peer-to-peer lending

In setting up, and bidding in online auctions, people face difficult strategic decisions. In this study, a Borrower Decision Aid is introduced, which will help formalize the decision making process of the sellers, or borrowers in this case, in one particular P2P loan auction site, Prosper.com. The vast amount of real-life bidding data available in this online auction enables us to build new kinds of tools for decision makers. The Borrower Decision Aid helps the borrower to quantify her strategic options, such as starting interest rate, and the amount of loan requested. We identify which variables concerning the borrower are related to the probability of successfully securing a loan and the final interest rate.

Borrower Decision Aid for people-to-people lending

We define and identify bidding strategies in real-life small loan auctions (Prosper.com). In such auctions, lenders bid for borrowers' loan listings and the winners get to fund the loan at an interest rate determined by the auction. The exceptionally large empirical database provided by Prosper.com offers a unique opportunity to test and further develop the theory of online auctions. This study shows that bidding behavior is not homogeneous among bidders, as the traditional auction theory suggests. Instead, bidders use many different bidding strategies. Moreover, learning and bidders' consistency over time in different auctions is studied.

Lauri Puro

Papers

Borrower Decision Aid for people-to-people lending

Bidding strategies for real-life small loan auctions