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
Notice bibliographique
Résumé
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. \n \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. \n \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. \n \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. \n \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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».