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Category Archives: G33

Investigating Underpricing in Venture-Backed IPOs Using Statistical Techniques

By Michael Tan   

This paper concerns applying statistical methods to investigate under-pricing in VC-backed technology Initial Public Offerings (IPOs) since the great recession. In particular, firm, market, and IPO-specific variables were explored to determine if there were any significant relationships to under-pricing. The paper focused on the Bank Preference theory of under-pricing, where under-pricing is said to occur because investment banks running IPO processes are incentivized to under-price to decrease the risk that they will not be able to allocate all the issuance to price-sensitive public markets investors.

View Thesis

Advisors: Professor Daniel Xu, Professor Shawn Santo, Professor Grace Kim| JEL Codes: G3, G33, G24

Corporate Financial Distress and Bankruptcy Prediction in North American Construction Industry

By Gang Li

This paper seeks to explore the application of Altman’s bankruptcy prediction model in the construction industry by measuring its percentage accuracy on a dataset consisting of 108 bankrupt & non-bankrupt firms selected across the timeline of 1985-2013. Another main goal this paper is to explore the predictive power of an expanded variable set tailored to the construction industry and compare the results. Specifically, this measuring process is done using machine learning algorithm based on scikit-learn library that transforms a raw .csv file into clean vectorized dataset. The algorithm provides various classifiers to cross-validate the training set, which produces mixed statistics that favors neither variable set but provides insight into the reliability of the non-linear classifiers.

Honors Thesis

Data Set

Advisor: Connel Fullenkamp | JEL Codes: C38, C5, G33, G34 | Tagged: Bankruptcy, Corporate, Discriminant Analysis, Distress, Machine Learning

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