Selected work
06 · Empirical finance research
HKEX IPO Performance Study
A 441-IPO regression study that treats an imprecise result as a finding, not a narrative to force.
HKEX IPO sample2020–2025
441
OLS · log transforms · clustered inference
Executive summary
An empirical finance research paper examining how industry classification, assets, and revenue relate to ROE for HKEX IPO firms from 2020 to 2025.
Problem
IPO-performance narratives often over-interpret cross-sectional correlations. The study tests a compact, specified model and communicates uncertainty directly.
Approach
- 01Compile a 441-firm IPO sample from the Wind Financial Database.
- 02Estimate OLS models of ROE using industry controls and log transformations for size and revenue variables.
- 03Compare conventional and cluster-robust standard errors to assess sensitivity of inference.
- 04Present coefficients, confidence intervals, and data limitations alongside the headline findings.
02
Data & methodology
Data sources
- Wind Financial Database
- HKEX IPO sample, 2020-2025
Methods
- Cross-sectional OLS regression
- Log transformations
- Industry controls
- Cluster-robust inference
Workflow
Input→Collection→Analysis→Output
The result is intentionally shown with its uncertainty; the original academic report is not distributed in the public asset bundle.
Outputs & findings
Key outputs
- 17-page research report
- Regression outputs
- Confidence-interval visualizations
- Methodology and limitation notes
What the work demonstrates
- The reported adjusted R-squared was -0.0045.
- In the documented specification, size, revenue, and industry effects were not estimated precisely enough to support strong claims.
Limitations
- ROE is only a proxy for post-IPO success.
- The cross-sectional design does not establish causality or capture all market and firm-level drivers.
- The report itself advises cautious interpretation of the broad confidence intervals.