Return-Enhancing Strategies with International ETFs: Exploiting the Turn-of-the-Month Effect
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Abstract
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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Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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