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Return-Enhancing Strategies with International ETFs: Exploiting the Turn-of-the-Month Effect

2015· article· en· W2339720206 sur OpenAlex

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Notice bibliographique

RevueFinancial Services Review · 2015
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueFinancial Markets and Investment Strategies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDiversification (marketing strategy)BusinessStock (firearms)Index fundMarket liquidityFinancial economicsEconomicsMonetary economicsInstitutional investorFinanceOpen-end fundCorporate governanceGeographyMarketing
DOInon disponible

Résumé

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1. IntroductionExchange-traded funds (ETFs) become a valuable tool for individual investors and financial advisors in the pursuit of higher returns and more effective diversification. In this article, we study whether investors can take advantage of the turn-of-the-month (TOM) effect in international ETFs. The TOM effect refers to the phenomenon that stock returns are higher surrounding the turn of the month. Early studies in Ziemba (1991) and Cadsby and Ratner (1992) document that the TOM effect exists in several international stock returns. Recently, McConnell and Xu (2008) reconfirm the existence of the TOM effect in 34 out of 38 international market indexes.Hensel and Ziemba (1996) and Kunkel and Compton (1998) show that the trading strategy of investing in low risk fixed-income account during non-TOM period and switching to investing in stocks during TOM period produces a higher return than the traditional strategy of buying-and-holding stocks. However, Chen and Chua (2011) show that the trading strategy of holding a Standard & Poor's Depositary Receipt (SPDR), the corresponding ETF for the Standard & Poor's (S&P) 500 index, during TOM period and switching to holding T-bills during non-TOM period does not produce a higher return than the strategy of investing in SPDR throughout the month.Given that ETFs have high liquidity and extremely low trading cost, can individual investors exploit the TOM effect in iShares? We examine the following markets: Australia, Brazil, Canada, France, Germany, Japan, Hong Kong, Sweden, and United Kingdom. All of them allow foreign investors without much restriction. We first show that the TOM effect exists in index returns in all markets except for the Japanese market, whereas the TOM effect exists in all nine ETF returns. When we control for other known confounding factors such as the January effect and the Weekend effect, the TOM effect still exists in all nine ETF returns except for the Japanese market and in six index returns. In addition, we find that the risk level is lower during TOM period than during non-TOM period for both index and ETF returns in all nine markets.Following Chen and Chua (2011), we compare the performance of the following three strategies for individual investors. The first strategy is for investors who buy and hold an index fund mimicking a foreign stock market index throughout the month. The second strategy is for investors who buy and hold the corresponding ETF throughout the month. The third strategy is for investors who invest in the ETF during TOM period and switch to holding T-bills during non-TOM period. We show that this ETF-T-bills switching strategy produces the highest return and has the lowest risk compared to the other two strategies. Statistically, the mean monthly return from the switching strategy is significantly positive, whereas those from the other two strategies are not significant. Economically, this switching strategy produces a terminal value that is at least 50% higher than the other two strategies. Therefore, our results show that investors can exploit the TOM effect in international ETFs.The following of the article is organized as follows. Section 2 reviews previous literatures on TOM calendar anomaly and ETFs. Section 3 describes data and methodology used in this study. Empirical results are presented in Section 4. Section 5 summarizes and concludes.2. Related studies2.1. Calendar anomalyAriel (1987) shows that the cumulative returns during a window of (^9, ^9) around the first day of the month are non-negligible even after controlling for the impact of the January effect. Examining the monthly Dow Jones Industrial Average (DJIA) returns over the period of 1987 through 1986, Lakonishok and Smidt (1988) find an average of 0.473% return cumulated during the four-day period at the turn of month, which is higher than the average cumulative return of 0.349% in the whole month. …

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Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,834
Score d'incertitude au seuil0,573

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,022
Tête enseignante GPT0,231
Écart entre enseignants0,209 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle