School of Business and Management Department of Finance 207 Hong Kong IPO Market Supervisor: ZHANG Chu / FINA Student: LAM Ching Long / ECOF NGAI Ho Fai Calvin / QFIN Course: UROP1100, Spring UROP1100, Spring This research project aims to find out a model to predict the offer price of IPOs in HKEx, given the publicly available information right before the listing. Using financial data consolidated from IPO prospectuses and DataStream, two distinct models for price prediction were built using Microsoft Excel and Python. Factors regarding company size, time and market sentiment indeed have a significant effect on the IPO offer price. A counterintuitive finding is that the price-to-book ratio of the industry at the time of issue has a negative relation with the IPO offer price. The methodology of the research and the results of the models will be elaborated in detail in the report. More other important findings and implications of the results would also be discussed. Hong Kong IPO Market Supervisor: ZHANG Chu / FINA Student: LIU Muyuan / QFIN Course: UROP1100, Spring Before listing a company on Hong Kong Stock Exchange, companies need to hold a roadshow to collect information from the institutional investors to help with the offer price determination. However, there hasn’t been a model which reveals the relationship between IPO offer price and the underlying factors. We gathered the data from 2772 companies that got listed on Hong Kong Stock Exchange, whose date of listing range from 2002 to 2020. We finally find that the firm’s offer price is associated with the company’s debt ratio, return on equity, return on asset, total equity, industry average equity value, and the industryaverage market-to-book ratio. The model shows a good prediction accuracy in the data. Hong Kong IPO Market Supervisor: ZHANG Chu / FINA Student: SUN Beini / RMBI Course: UROP1100, Spring The goal of this research is to find out a model that can describe the offer price given information from the prospectus. We have built two kinds of prediction models, linear regression models and nonlinear (decision tree) models to observe which factor is likely to have an impact on the IPO offer price. Findings from all the linear models we have constructed are that the financial fundamental ratios (ROA ROE and EquityMultiplier) in the latest year tend to have largest coefficients and smallest p-value, suggesting that the most recent financial status play a vital role in IPO pricing. For nonlinear models, the most informative feature is the average ROA-ratio over the past three years.
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