Insider trading, asymmetric information, and market liquidity : three essays on market microstructure
Bibliographic record
Abstract
This thesis comprises three essays on market microstructure, focusing on the issues of insider trading, asymmetric information and market liquidity. The first essay examines the effects of the mandatory disclosure regulations on the trading behavior of informed traders. Specifically, we compare the (perfect Bayesian) equilibrium when disclosure is mandatory to the equilibrium when insiders do not have to disclose their trades. We show that under mandatory disclosure the market becomes more efficient and more liquid, making the uninformed traders unambiguously better off. We also show that in order to conceal part of his information, under mandatory disclosure the insider may trade against his information, and, at the same time, add a random---"noise"---component to his trade order. As a result, insiders may end up buying (selling) when his information indicates the asset is overvalued (undervalued). This provides a rationale for contrarian trading. The second essay examines trading behavior, price behavior and the informational efficiency and the informativeness of the price process in the equilibrium of a strategic trading game when some investors receive information before others. We show that the early informed investor may trade against his information to maintain his information superiority over the market. Under some conditions, subsequent price changes are positively correlated. We also find that the price process is less efficient and less informative than would be the case where there is no late-informed trader. The third essay analyzes the infra-day behavior of market liquidity of the Toronto Stock Exchange which uses a computerized limit-order trading system. Along with previous studies, we show that the U-shaped infra-day pattern of spread does not depend on the market architecture. In addition, we confirm that bid-ask spread and market depth are two dimensions of market liquidity. Liquidity providers use both dimensions to deal with adverse selection problems. We also examine how price volatility and trading volume affect market liquidity. Price volatility is inversely related to market liquidity but trading volume is directly related to liquidity. High trading volume implies high liquidity trades and as a result, liquidity providers decrease (increase) ask (bid) price and/or increase depth at each quote.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".