The Effects of Corporate Social Responsibility on Equity Fund Returns: Evidence from China
Bibliographic record
Abstract
The purpose of this paper is to investigate whether the fund management companies in China have the corporate social responsibility (CSR) and its correlation with the fund performance. The samples are the equity funds between 2004 and 2012. The basis for assessment for the CSR is the Chinese fund company comprehensive assessment report, published by Morningstar Chinese Research Center in Jan 25th 2013. This research uses quantile regression model with three factors to measure the fund management companies with high ranking or low ranking average fund returns and provide investors some basis for investment decisions. The empirical results show the fund management companies with high ranking have better fund returns than those low ranking. Through quantile regression estimations, it is found that the market factors and fund returns have significant correlation with high ranking. The groups with low ranking and high fund returns, the funds performance has a significant positive correlation with the size factors. The group with high ranking and below medium fund returns, the fund performance has a positive relationship with the book to price ratio. Finally, in the fund management companies with extreme CSR ranking, the fund performance has a significant positive correlation with the fund net asset value.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".