Bibliographic record
Abstract
In the first chapter, we study how innovation networks among firms cause interdependencies in their stock returns. Using patent grants and citation data from the United States Patent and Trademark Office, we find that patents generate positive abnormal returns not only for the innovating firm but also for firms that have previously cited them in their own patents. The magnitude of this financial spillover is directly proportional to the quality of the granted patents and the intensity of a firm’s reliance on its upstream firms in the innovation network. The spillover effect is diminished when firms compete in the product market, but it is larger when the firms are also interconnected within the supply chain. Additionally, we find that the financial spillovers of innovation are restricted to firms that are directly connected in the innovation network. We quantify the spillovers and find that innovations generate large positive financial externalities on other firms. The second chapter investigates the strategic behavior of chief executive officers (CEOs) in disclosing discretionary corporate news close to their contract renewal dates. Analyzing 296 fixed-term employment contracts of Standard & Poor’s (S&P) 1500 firms covering the period from 2000 to 2009 and using contract length as an instrumental variable (IV), we show that, compared to the baseline, the number of discretionary news items is higher by 1.4 in the quarter preceding the renewal date and by 1.44 in the quarter following it. The sentiment of news items is higher by 0.45 in the quarter before the renewal date and lower by 0.35 in the subsequent quarter. This provides evidence of strategic disclosure of good news before, and clustering of bad news after, the contract renewal dates. This behavior is stronger among CEOs with a history of poor performance and weaker among those who also hold the position of chairman. In the third chapter, I study the effect of mutual fund managers’ learning and experience on their portfolio decisions and performance, with a particular focus on stock-specific experience. Using the total number of quarters a specific stock has been held in a manager’s portfolio as a proxy for the manager’s stock-specific experience, the findings show that managers consistently generate superior abnormal returns when dealing with stocks on which they have more experience. The results show that, on average, each additional quarter of experience a manager has with a particular stock in their portfolio predicts a 2.7 basis point higher abnormal return for the stock in the following quarter. Additionally, this experience leads to a 2.06% ex-post increase in the proportionate value that the stock adds to the portfolio in the following quarter. The results not only highlight the effect of stock-specific experience on performance but also underscore the significant impact of industry-specific experience. Specifically, when fund managers with more experience in a given industry add a stock from the same industry that has not previously been in their portfolio, they achieve better performance with that stock in the next quarter compared to their peers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".