---
title: Investor Sentiment and IPO Flipping in China
url: https://www.ml-quant.com/papers/repec/taf-reroxx-v-36-y-2023-i-1-p-2113739/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:taf:reroxx:v:36:y:2023:i:1:p:2113739
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F1331677X.2022.2113739%3Bh%3Drepec%3Ataf%3Areroxx%3Av%3A36%3Ay%3A2023%3Ai%3A1%3Ap%3A2113739
featured: 2024-03-13
citations: unknown
topic: LLMs & Text
---


# Investor Sentiment and IPO Flipping in China

The research reveals that investor sentiment, risk-free interest rates, and broad market indices significantly affect IPO first-day flipping in China.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F1331677X.2022.2113739%3Bh%3Drepec%3Ataf%3Areroxx%3Av%3A36%3Ay%3A2023%3Ai%3A1%3Ap%3A2113739
- Identifier: RePEc:taf:reroxx:v:36:y:2023:i:1:p:2113739
- Released: 2023-03-07
- First featured: Quant Letter No. 40 (2024-03-13): https://www.ml-quant.com/issues/2024-03-13/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: LLMs & Text

## Related

- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://www.ml-quant.com/papers/ssrn/4412788/): ChatGPT predicts stock market returns using sentiment analysis, outperforming traditional methods.
- [Designing Heterogeneous LLM Agents for Financial Sentiment Analysis](https://www.ml-quant.com/papers/arxiv/2401.05799/): A study suggests using large language models without fine-tuning for financial sentiment analysis, offering a design framework that enhances accuracy.
- [Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models](https://www.ml-quant.com/papers/arxiv/2306.12659/): A new approach improves financial sentiment analysis by addressing limitations of language models.
- [Sentiment trading with large language models](https://www.ml-quant.com/papers/doi/10-1016-j-frl-2024-105227/): The OPT model, a large language model, has proven superior in predicting stock market returns using sentiment analysis of U.S. financial news, outdoing traditional methods like the Loughran-McDonald dictionary model.
- [Uncertainty in Sentiment Analysis with LLMs using QCM (Quantiles of Correlation Matrices) - Distance](https://www.ml-quant.com/papers/ssrn/4780192/): The study explores the uncertainty in sentiment scores derived from text using advanced language processing models, finding moderate uncertainty in the results.
- [BioFinBERT: Finetuning Large Language Models (LLMs) to Analyze Sentiment of Press Releases and Financial Text Around Inflection Points of Biotech Stocks](https://www.ml-quant.com/papers/arxiv/2401.11011/): LLMs for Financial Sentiment Analysis: BioFinBERT, a finetuned Large Language Model, is introduced for financial sentiment analysis of biotech press releases and financial texts, which greatly impact biotech stock prices.
