Three essays on asset pricing and behavioral finance
Bibliographic record
Abstract
This thesis includes three essays. The first essay examines the pricing of sentiment commonality in the cross-section. First, we propose a novel option-implied firm-level investor sentiment measure—the open interest weighted implied volatility ratio of out-of-the-money (OTM) calls over OTM puts. A long-short portfolio strategy, based on a long position in the high-sentiment portfolio and a short position in the low-sentiment portfolio, generates a significant abnormal return of 8.73% per annum. This sentiment effect on stock returns is more pronounced for hard-to-value stocks, which are small, young, highly volatile, and less liquid. Next, we validate the existence of sentiment commonality at the market and industry levels. The quintile portfolio with the highest sentiment commonality outperforms the portfolio with the lowest sentiment commonality. We find a positive and significant risk premium on the sentiment commonality risk. In the second essay, a secondary data study and a controlled experiment reveal the gender bias that exists in the relationship between CEO tweets and investor risk perceptions. We construct a unique measure of CEO Twitter professional disclosure using the cosine similarity between key words in tweets and key words in earnings call transcripts. We find that a higher level of professional disclosure via CEO social media accounts reduces daily implied volatility, a measure of market risk perception. This association is more pronounced for attractive female CEOs than for male CEOs or less attractive female CEOs. The controlled experiment also validates that more attractive female CEOs are rewarded when posting more professional tweets on social media, which reduces investors’ (subjects’) risk perceptions. In the third essay, we use a large sample of individual Chinese investors to demonstrate that they are more likely to trade stocks for short-term speculation after experiencing trauma such as natural disasters, serious illness, or death in their immediate family. Investors exhibited higher impulsivity, a greater desire for immediate gratification, a greater willingness to follow trends, and more risk-taking behaviors as a result of a trauma experience. We also find that the relationship between trauma experience and investment horizon is less pronounced for older and married individuals.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".