Bibliographic record
Abstract
Recent technological advancements have challenged financial markets. Academic researchers, regulators and market participants voice concerns that modern markets bear the negative externalities of such advancements. Specifically, they are concerned that today’s markets are becoming more fragile and unfair to less sophisticated traders. This work employs empirical methodology to test whether these concerns are justified. This thesis contains three essays:\nThe first essay studies whether modern markets become less liquid during intraday extreme price movements (EPMs). When a price moves in a certain direction, liquidity providers face two opposing incentives. The first incentive is to stay in the market to accumulate more inventory in anticipation of a price reversal. The second incentive is to withdraw due to capital constraints, inventory and adverse selection risks. Using data from Canadian and U.S. markets, I find that the former incentive is stronger during intraday EPMs. This finding alleviates concerns that prices are subject to periods of extreme volatility due to systematic liquidity withdrawals. Contrary to these concerns, liquidity providers appear sufficiently incentivized to dampen intraday volatility.\nThe second essay examines the activity of a specific type of modern liquidity providers – high frequency traders (HFTs) – around EPMs. I find that, on average, HFTs provide liquidity during EPMs by absorbing imbalances created by non-high frequency traders (nHFTs). Yet HFT liquidity provision is limited to EPMs in single stocks. When several stocks experience simultaneous EPMs, HFT liquidity demand dominates their supply. There is little evidence of HFTs causing EPMs.\nThe third essay studies whether recent technological advancements result in higher costs for less sophisticated traders. In modern markets, trading firms spend generously to gain a speed advantage over their rivals. The marketplace that results from this rivalry is characterized by speed differentials whereby some traders are faster than others. Is such a marketplace optimal? To answer this question, I study a series of exogenous weather-related episodes that temporarily remove the speed advantages of the fastest traders by disrupting their microwave networks. During these episodes, adverse selection declines accompanied by improved liquidity and reduced volatility. Liquidity improvement is larger than the decline in adverse selection consistent with the emergence of latent liquidity and enhanced competition among liquidity suppliers. The results are confirmed in an event-study setting, whereby a new business model adopted by one of the technology providers reduces speed differentials among traders, which results in liquidity improvements.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".