Extrapolation in China

Extrapolation in China’s Stock Market: Returns, Price Crash Risk and Price Informativeness

AuthorsSiyuan Yang; Siyang Li

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3978914

Universe: Chinese stocks in the CSMAR database with Eastmoney Guba (stock forum) sentiment data from CNRDS.

Sentiment measure: Expectation=Positive – Negative postsPositive + Negative posts\text{Expectation} = \frac{\text{Positive – Negative posts}}{\text{Positive + Negative posts}}Expectation=Positive + Negative postsPositive – Negative posts​

Modeling:

Compute cross-sectional rank of sentiment expectations.

Estimate a non-linear regression with past 12 weekly returns (t to t–11).

Use rolling estimation periods (m–18 to m–7, m–22 to m–7, m–26 to m–7) and validation (m–6 to m–1).

Parameters are weighted averages across estimation windows, with weights = inverse MSFE (normalized).

Portfolio construction:

Portfolios are value-weighted, rebalanced weekly.

Each week, estimate predicted and residual expectations.

Double sort into terciles (30–40–30) by predicted & residual expectation → 9 portfolios.

Long: lowest predicted, highest residual.

Short: highest predicted, lowest residual.

Investor Sentiment via Social Media: Builds on established evidence (e.g., StockTwits literature) that online sentiment is predictive of returns.

Decomposition of Expectations: Separating predicted vs. residual expectations reveals hidden information not captured by raw sentiment.

Robust Predictive Power: Both economic (portfolio performance) and statistical (regressions with controls) tests confirm significant return predictability.

Practical Strength: Strategy remains highly statistically significant even when value-weighted, important given China’s large number of microcaps.