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Record W7023992211

The predictive nature of short interest on market returns and the effect of short selling on volatility, liquidity and price discovery with some international evidence

2020· dissertation· en· W7023992211 on OpenAlexaboutno aff

Bibliographic record

VenueGreenwich Academic Literature Archive (University of Greenwich) · 2020
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsPrice discoveryCapital asset pricing modelVolatility (finance)Market liquidityStock (firearms)Short interest ratioMomentum (technical analysis)Interest rateStock marketRate of return
DOInot available

Abstract

fetched live from OpenAlex

I look to explore the findings of Boehmer et al. (2010) and in particular test across model specification investment horizon and across countries. This in turn means I look at whether short sellers are informed traders and if there is evidence that short sellers engage in market manipulation. I also look at whether and to what extent short sales affect liquidity, price discovery, volatility and cross-section of stock returns. The main novelties of research of this PhD are that I employ a new more efficient asset pricing model to the findings of Boehmer et al. (2010), I explore also a time period after publication to observe market efficiency and that I explore an international market. I am also one of few studies to study volatility, liquidity and price discovery in regards to a short sale ban in the UK. 
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\nFirstly, I look at whether the strategy of Boehmer et al. (2010) is valid when another most recent and efficient model is used to adjust for risk premium. Boehmer et al. (2010) used the Fama and French Three Factor Model with Momentum to adjust for risk premium, in my case I use the Fama and French Five Factor model to adjust for risk premium. The strategy of Boehmer et al. (2010) involves going long the top percentile of stocks ranked by short interest and going short the bottom percentile of stocks ranked by short interest and rebalancing each month based on new short interest data. I find that heavily shorted stocks underperform lightly shorted stocks. The Fama and French Five Factor model holds a high positive alpha for lightly shorted portfolios on top of both higher excess and raw returns for lightly shorted portfolios compared to heavily shorted portfolios. However, the short component of the Boehmer et al. (2010) strategy yields a positive return, therefore I advise to go long the top five percentile of stocks. The Fama and French Five Factor model does indicate there is good news in short interest albeit without a short component.
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\nSecondly, I look at whether the strategy of Boehmer et al. (2010) is still valid after publication in the US stock market or whether an efficient market has caused arbitrage to take place and make this strategy redundant. I again find that heavily shorted stocks underperform lightly shorted stocks, however the strategy of Boehmer et al. (2010) is not completely valid as the short component of the strategy yields a positive monthly return. It is still however valid to go long the top five percentile of stocks and I believe that this underperformance of heavily shorted stocks means arbitrage has not taken place.
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\nThirdly, I also look at whether the strategy of Boehmer et al. (2010) is valid in another OECD country such as Canada. This is used as an indicator of the international OECD market based on short interest data availability. I find again that heavily shorted stocks underperform lightly shorted stocks, in line with findings consistent with previous literature. However, the strategy of Boehmer et al. (2010) is again completely not valid as the bottom five percentile of stocks have a positive monthly return. I advise going long the top five percentile portfolio as the best strategy, in line with my findings regarding arbitrage in the US stock market. There is no valid reason in holding a long/short portfolio if the short side is yielding a positive monthly return. I find the US stock market outperforms the Canadian stock market on average over the February 2010 to July 2017 period. 
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\nFourthly I look at the relationship between liquidity, volatility and price discovery with short selling. I use the UK financial stocks short sale ban of the 2007-2009 financial crisis in order to explore the effects of short selling on liquidity, volatility and price discovery. I employ a control portfolio as well to see the effects of the short sale ban. I employ a GARCH model to explore the effects of short selling on volatility. I find that volatility is not affected during the short sale ban, this in turn questions the significance of short sale bans like many other studies before mine have done. I employ a Bid-Ask Spread Model to explore the effects of short selling on liquidity. I find that the Bid-Ask Spread Model shows good fit regression wise and that liquidity deteriorates during the short sale ban period. Lastly, I employ a Wald-Wolfowitz Runs Test to see fat tails in its distribution, this shows the effects of price discovery on a short sale ban. I find that price discovery deteriorates during the short sale ban period.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.215
Teacher spread0.201 · 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 teacher head, not a consensus.

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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