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Record W7071811225

Three Essays on Financial Markets

2020· dissertation· en· W7071811225 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2020
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsDiversification (marketing strategy)Emerging marketsCorporate governancePortfolioFinancial marketStock marketPosition (finance)Stock (firearms)Downside risk
DOInot available

Abstract

fetched live from OpenAlex

This dissertation consists of three essays that address recent topics in financial markets that concern for scholars, policymakers, and investors. The first essay examines the benefits of international diversification for US investors, while accounting for market development, corporate governance, market cap effects, and structural change across countries over period August 1996 –July 2013. Improved risk adjusted returns are obtained from a diversified portfolio consisting of a mix of developed and emerging countries. Additionally, we find that diversification benefits are not significant for most of the small-cap foreign assets when an investor already holds position in corresponding countries large-cap assets. Diversification benefits based on the governance effectiveness of a country’s companies are not ubiquitous. We find that economically significant improvements in risk-return performance can be attained by adding large caps of developed countries with high and low overall Governance Metrics International (GMI) ratings and large and small caps of emerging countries with low overall GMI ratings to the investment universe containing the assets of common law developed countries. However, diversification benefits are economically significant only for large and small caps of low GMI emerging countries when short selling is not allowed. \n \nThe second essay looks at the market impact of recent regulatory changes in Canada that provide for trading halts on individual stocks that experience large upside or downside movements. The focus is on all stocks traded on the Toronto Stock Exchange since the inception of the single stock circuit breaker rule (SSCB) in February 2012, to replace the short-sale uptick rule. The results support pricing efficiency: material information that caused the circuit breaker is incorporated in stock prices on the day of the halt (neither overreaction nor underreaction), with no decline in market liquidity. Using trade-by-trade data constructed on 5-minute trading intervals, we refine the daily results, and show that shocks in realized volatility are focused in the ten-minute trading interval surrounding the halts. While circuit breakers provide a limited “safety net” for investors when their stocks are subject to severe volatility, they do not provide for a quick turnaround for stocks experiencing severe price decline events. \n \nThe last essay re-examines the historical vs implied volatility spread anomaly, reported by Goyal and Saretto (2009) using a second-order stochastic dominance (SSD) criterion. The approach incorporates transaction frictions, and is robust to model specification problems, return distributions, as well as preferences. It is found that option trading frictions such as cash collateral requirements and option trading costs significantly reduce but do not eliminate returns to a long-short straddle trading strategy pre-2006 period. However, the anomaly disappears after 2006, consistent with market efficiency. The SSD test results confirm the findings.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0040.006
Scholarly communication0.0080.009
Open science0.0010.004
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0240.007

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.

Opus teacher head0.040
GPT teacher head0.243
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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