The Impact of Investor Sentiment on the Cost of Equity Capital: A Study from the Perspective of Financial Social Media
Bibliographic record
Abstract
Investor sentiment has lately emerged as one of the key variables that can explain the patterns of the given contemporary financial markets. This paper aims to establish a relationship between investor sentiment derived from financial social media and the cost of equity capital. Our primary dataset is collected from public data sources such as CSMAR, Wind, Resset, and Wingo and it is completed by web scraping and manual data collection. We employ a sample of A-share firms for the period 2010-2020 and find that bearish sentiment increases a firm’s cost of equity while bullish sentiment decreases it. Besides, this research does much more than merely incorporate the cost of capital models with the behavioral finance stream; it also offers potential recommendations for both the corporate management, as well as the policy regulator. Therefore, our empirical method is based on multivariate regression analysis and various tests such as subgroup analysis, mediation analysis, and endogeneity test that are run under different market conditions. Finally, the paper examines the limitations of the study and identifies areas for future research on the relationship between digital investor communications and traditional financing.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".