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ISOM Seminar: Michael Wagner, University of Washington, OM
WhenFriday, Apr 7, 2017, 10:30 a.m. – 12 p.m.
Campus locationPACCAR Hall (PCAR)
Campus roomPaccar 290
Event typesLectures/Seminars
Event sponsorsISOM Department
(206) 543-1043

Paccar Hall, Room 290

Seminar Speaker:
 Michael Wagner
Affiliation: University of Washington
Area: Operations Management

Name of Presentation: Crowdfunding via Revenue-Sharing Contracts

In this paper we analyze a new model of crowdfunding recently introduced by Bolstr and Localstake. In this model, a platform acts as a matchmaker between a firm needing funds and a crowd of investors willing to provide capital. Once the firm is funded, it pays back the investors using revenue sharing contracts, with a pre-specified investment multiple (investors will receive M ≥ 1 dollars for every dollar invested) and a revenue-sharing proportion, over an investment horizon of uncertain duration. The firm determines its optimal contract parameters to maximize its expected net present value, subject to investor participation constraints and platform fees. A natural multi-period formulation results in a non-convex stochastic optimization problem, which we approximate using a deterministic model; we are able to solve the approximate model in closed form. Parameterized on real data from Bolstr, our approximation typically gives solutions within 1% of the simulation-based optimal solution to the stochastic problem (with a maximum observed error of 2%). We use the closed-form approximate firm solutions in a game-theoretic setting to analyze the platform's problem, which sets its optimal pricing structure, in the form of origination and servicing fees, to maximize its net present value, subject to firm participation constraints. The last part of our paper considers the design of a crowdfunding campaign for this new model. Assuming that participating investors arrive according to a non-homogeneous Poisson process, with a minimum investment-dependent rate, we derive the minimum campaign deadline for a target probability of funding success.…
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