Bibliographic record
Abstract
This thesis combines three papers studying the effects of innovations in modern digital platforms on investors and securities. In Chapter 1, I build a model where endogenous prices set by entrepreneurs play a central role in determining the success rates of their projects. Individuals with private information about the value of projects choose the appropriate time for investments using equity crowdfunding platforms. When opportunity costs are sufficiently low, investors with sufficiently high valuations promote the project to others by investing early. As opportunity costs increase, entrepreneurs lower prices to make the project more competitive compared to outside options, creating the "pricing effect." I then show that, as a consequence of the pricing effect, increases in opportunity costs improve project success rates during times of high costs. Furthermore, if prices remained unchanged, then more valued projects would be more likely to succeed. However, entrepreneurs increase asset prices as they have more valued projects, discouraging individuals to invest. As a result of this increase in asset prices, projects with lower expected values have higher success rates than those with higher expected values.Chapter 2 is a joint work with Redouane Elkamhi. We extend Kyle (1985) by introducing transparency in trade orders and insiders with single/multiple accounts. We find that splitting orders across time without fully revealing private information requires multiple accounts. Therefore, insiders with a single account trade more aggressively than insiders with multiple accounts. This heterogeneity in trade behaviors leads to non-monotonicity between volume and fundamental value. In Chapter 3, using an event study analysis with 60 cases from the Coinbase exchange over the period 2016–2021, I discover that newly included coins into the Coinbase exchange, on average, experience 16.8% gain in its price in the first day following the inclusion announcement. Price appreciation continues in the following days, reaching a high of 22.52% on the fourth day. Price depreciation occurs at a slower rate, taking another 14 days to lose all of the gains seen in the first four days, which shows that Coinbase effect is not persistent but leads to short-term bubbles for event coins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".