On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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4 results for “ChiNext”

The Impact of Regulation Regime Changes on ChiNext IPOs: Effects of 2013 and 2020 Reforms on Pricing and Overreactionq-fin.GN

This study examines how ChiNext IPOs' initial returns are influenced by regulation regime changes.

problem Investors' behavior and pricing of ChiNext IPOs under different regulation regimes.

method Analysis of three time periods with two different regulation regimes and three sets of listing day trading restrictions.

result Regulation regime changes significantly impact ChiNext IPO pricing and overreaction.

ChiNext IPOs Regulation regime changes IPO pricing Overreaction Intrinsic value

2022-11-19abs ↗pdf ↗

The Impacts of Registration Regime Implementation on IPO Pricing Efficiencyq-fin.GN

Study shows registration regime improves IPO pricing efficiency in China.

problem Improving IPO pricing efficiency in China's entrepreneurial boards.

method Examined the impacts of regime changes and rule implementations on IPO pricing efficiency.

result The registration regime is most efficient in Chinese IPO pricing.

IPO pricing regulatory changes STAR ChiNext Chinese entrepreneurial boards

2023-07-18abs ↗pdf ↗

Analysis of Contagion in China's Stock Market: A Hawkes Process Perspectiveq-fin.ST

Study uses Hawkes processes to analyze stock market contagion in China.

problem Understanding contagion in Chinese stock market.

method Fitting Hawkes processes to daily returns and sector indices.

result Identifies long-term dependencies and trending patterns in sector indices.

contagion Hawkes processes Chinese stock market sector indices spatiotemporal analysis

2025-12-08abs ↗pdf ↗

Machine Learning Enhanced Multi-Factor Quantitative Trading: A Cross-Sectional Portfolio Optimization Approach with Bias Correctionq-fin.PM

A new method corrects bias in machine learning for trading by filtering out non-executable prices.

problem Contaminated data from price limits causes misleading performance metrics in trading models.

method Mask-first design with tradability filters, GPU optimization, and penalized losses.

result The system achieves Sharpe ratios of 1.63 on real data, significantly outperforming existing methods.

machine learning quantitative trading portfolio optimization bias correction upstream contamination

2025-06-02abs ↗pdf ↗