Essays on Empirical Asset Pricing
Bibliographic record
Abstract
In the first chapter, our empirical tests use data from three sources. First, we obtain the Loughran and McDonald’s (hereafter LM wordlist) positive/negative wordlist and from the authors’ website. Second, we obtain the monthly Fama and French (1993 and 2015) factors (i.e. SMB, HML, Rm-Rf, CMA, and RMW) and momentum factor (MOM) from Kenneth French’s website for the sample period January 1994 through December 2016. Third, we obtain the monthly stock returns, monthly index returns, month end market value from the Center for Research in Security Prices (CRSP) as well as accounting information such as annual bookI the second chapter, we utilize five main datasets in this study. The first dataset is the stock market transaction level data for S&P 500 stocks, obtained from Trades and Quote (TAQ). The second dataset is the corporate bond transaction data from Trade Reporting and Compliance Engine (TRACE) through Wharton Research Data Services (WRDS) for the S&P 500 firms. The TRACE data provides over the counter (OTC) corporate bond market real-time prices.To examine the price discovery of bonds in equity prices we use a sample period of over 1,000 trading days from January 2004 through December 2008.Our third data source is the institutional level transaction data from ANcerno, which provides transactional level trade data for corporate bonds and stocks for the first quarter of 2006 through the third quarter of 2010. Several studies have used equity transaction dataset to examine the ANcerno institutional trading behavior. See for example Puckett and Yan (2011), Bethel, Hu and Wang (2009), Chemmanur, He and Hu (2009), Goldstein, Irvine, Kandel and Wiener (2009). Additionally, Hu, Jo, Wang and Xie (2018) provide a comprehensive review of ANcerno dataset. The fourth source of data comes from Mergent Fixed Income Security Database (FISD), which provides details of bond characteristics and credit ratings from standard and poor’s (S&P) and Moody’s. Finally, we obtain the daily stock returns data from center for security prices (CRSP) database and match it with the daily bond returns to examine the lead-lag relationships.<br>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".