代写Determinants of IPO Performance in Hong Kong: A Data-Driven Analysis代写Python编程

Project title: Determinants of IPO Performance in Hong Kong: A Data-Driven Analysis

Overview

- This project analyzes Hong Kong IPOs to identify the firm-, deal-, and market-level factors that drive IPO underpricing (first-day returns) and aftermarket performance (5-day, 10-day, 15-day, 3, 6, months etc).

- It leverages comprehensive data from HKEX and commercial databases, applying econometric and machine learning methods to produce actionable insights for issuers, underwriters, and investors.

Sample research questions

- What explains cross-sectional variation in first-day returns and post-IPO abnormal returns in Hong Kong?

- How do HK-specific features (cornerstone investors, WVR/dual-class shares, Chapter 18A pre-revenue biotech listings, H-share/red-chip structures) affect pricing and performance?

- Do market cycles (hot vs. cold periods), liquidity, and sentiment mediate IPO outcomes?

Data and sources

- IPO universe: All main board and GEM IPOs from recent period from HKEX filings and prospectuses.

- Deal characteristics: offer price/size, allocation split (retail vs. institutional), over-allotment, subscription multiples, cornerstone participation, underwriter/sponsor reputation, lock-up terms.

- Firm characteristics: industry, age, financials, profitability, leverage, ownership/SOE status, auditors, pre-IPO investors, WVR/18A flags.

- Market variables: HSI returns and volatility, turnover, short-selling ratio, interest rates, RMB/USD, global risk (VIX), China macro indicators.

- Price and liquidity: first-day and subsequent prices, volume, Amihud illiquidity, bid-ask spreads, research coverage initiation.

- Data sources: HKEXnews, Bloomberg/Refinitiv/Wind/Capital IQ, CCASS, HKSFC, CEIC, public prospectuses, news/sentiment feeds.

Methodology

- Event study to compute underpricing, CARs and BHARs over multiple windows with Fama-French/Carhart risk adjustments.

- Cross-sectional and panel regressions with fixed effects; robust/clustered errors; controls for industry, year/quarter, and issuance waves.

- Address endogeneity via instruments (e.g., exogenous allocation policy changes) and Heckman selection where relevant.

- Survival/hazard models for delisting or index inclusion; mediation analysis for the role of liquidity and sentiment.

Key variables of interest

- Cornerstone share, retail subscription multiple, institutional coverage initiation, underwriter reputation, valuation (P/E, P/B), proceeds, free float, lock-up length, WVR/18A dummy, biotech flag, SOE status, offer-to-grey premium, issuance timing, macro volatility.

Expected outcomes

- A ranked set of determinants with effect sizes and economic significance.

- Predictive models out-of-sample validation.

- HK-specific insights on the impact of cornerstone investors, 18A biotech, and WVR on pricing efficiency and aftermarket dynamics.

- Policy and practitioner recommendations for pricing, allocation, and disclosure.

Deliverables and timeline

- Data dictionary and cleaned dataset; code repository (Python and Excel).

- final paper and presentation.