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Evidence of Idiosyncratic Seasonality in Etfs Performance n. 603 Apr 2018 ISSN: 0870-8541 Evidence of Idiosyncratic Seasonality in ETFs Performance Carlos Francisco Alves 1;2 Duarte André de Castro Reis 1 1 FEP-UP, School of Economics and Management, University of Porto 2 CEF.UP, Research Center in Economics and Finance, University of Porto Evidence of Idiosyncratic Seasonality in ETFs Performance Carlos Francisco Alves Faculty of Economics, CEF.UP, University of Porto, 4200-464 Porto, Portugal. Email: [email protected] Duarte André de Castro Reis Faculty of Economics, University of Porto, 4200-464 Porto, Portugal. Version: January 2018 _____________________________________________________________________________ Abstract Studies of the seasonality of ETFs are relatively scarce compared with other financial assets. Moreover, most of the existing literature on ETFs did not assess the seasonality patterns of risk-adjusted returns and tracking error. This article seeks to suppress some of these gaps. The results provide evidence of a first-half of the year effect (higher returns), an outperformance of the second quarter and an underperformance of the fourth quarter compared with the remaining quarters, and higher (lower) returns in the first (third) month of the quarter vs the other months of the quarter. Furthermore, April exhibits a superior and December an inferior performance compared with the remaining months. Besides, higher (lower) returns on Wednesdays (Fridays) were observed compared with the other weekdays. Regarding the tracking error, some seasonal patterns are also reported. For example, the replication was more accurate in April than it was in remaining months and in the first month of each quarter. Finally, the effects detected in ETFs returns were not reflected in indices returns, with the exception of the April effect, indicating that the main seasonality patterns detected are caused by idiosyncratic ETFs factors and not to the constituents of the underlying indices. Keywords: ETFs seasonality; indices seasonality; raw returns; risk-adjusted returns; tracking error; US equity. ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ The Research Center in Economics and Finance (CEF.UP) is supported by the Foundation for Science and Technology (FCT) through the Operational Program for Science, Technology and Innovation (POCTI) of the Community Support Framework (QCAIII), financed by the European Regional Development Fund (ERDF) and Portuguese funds. This project was specifically supported by FCT and by the Programa Operacional Temático Factores de Competitividade (COMPETE), supported by the European program FEDER. 1. Introduction There is an ample literature that seeks to detect and explain the seasonal patterns of returns in stock markets (e.g., Keim, 1983; Jaffe and Westerfield, 1985; Reiganum, 1983; Damodaran, 1989; Kim and Park, 1994; Booth et al., 2001), as well as research that proceeds to a similar scrutiny of bonds (e.g., Jordan and Jordan, 1991; Dbouk et al., 2013), mutual funds (e.g., Gallagher and Pinnuck, 2006; Alves, 2014), and pension funds (e.g., Andreu et al., 2013). The effects documented in this literature include, among others, the January effect (e.g., Rozeff and Kinney, 1976; Gultekin and Gultekin, 1983; Nippani and Arize, 2008; Sikes, 2014), the pre-holiday effect (e.g., Cadsby and Ratner, 1992; Teng and Liu, 2016), turn of the month effect (e.g., Ariel, 1987; Ogden, 1990; Maher and Parikh, 2013), and the weekday effect (e.g., Cross, 1973; Chen and Singal, 2003; Lin and Chen, 2008). More recently, some studies have reported the disappearance and even the reversal of some of these phenomena in some periods of history. This literature is important because it can allow the identification of arbitrage opportunities (and their disappearance), as well as symptoms of anomalies in asset valuation models that may derive from their inability to incorporate properly all the risk- generating factors, or from behavioral factors that are not considered determinant of the equilibrium returns. It is, therefore, important to detect these return patterns, and understand the reasons for their existence. In the case of passive management funds, which simply try to replicate a benchmark index, knowing this reality is even more important, as eventual seasonality may be derived not from underlying asset market, but from idiosyncratic fund market factors. Therefore, it is crucial to know whether there are seasonal patterns in the returns of this type of funds, and, if seasonal patterns are detected, understand whether their origin is due to the assets in which the funds invested, the behavior of their managers, or other factors specific to the industry. However, the published studies about seasonal patterns of the returns of passive funds are, in contrast to other financial instruments, scarce. Even the few that are known do not analyze or compare the returns among certain calendar periods. This article seeks to contribute to the suppression of these gaps by looking for seasonal patterns in the returns 2 of Exchange-Traded Funds (ETFs), as well as by seeking to understand whether these patterns are caused by the underlying asset market or to idiosyncratic factors. In particular, this work seeks to answer the following questions. 1) Is there evidence of seasonality in the returns and tracking error of ETFs? 2) Do the seasonal patterns in the return of ETFs differs from the underlying indices, indicating that this seasonality is due to idiosyncratic factors in the ETFs industry, or are they identical to the underlying indices, indicating that this seasonality is induced by the constituents of the underlying index? This study uses a sample of 148 equity ETFs with a geographical focus in the US and traded in the NYSE Arca. Raw returns and risk-adjusted returns were calculated using the market model and the Carhart model (1997). The calculations of the returns and tracking error were performed based on the prices and NAV of the ETFs, and the seasonality of the benchmark indices was investigated. In terms of econometric models to test the seasonality, the models of Marquering et al.(2006) and Alves (2014) were used. The data were obtained mainly from Thomson Reuters Eikon databases (Datastream), but also from other databases (in particular additional information on benchmark indices). Regarding the results obtained, there is evidence of higher returns in April (risk-adjusted and not-adjusted) and the first half of the year (risk-adjusted). December month is the month with the lowest risk-adjusted returns. Moreover, the ETFs have better performance (risk-adjusted returns) in the second trimester and worse performance in the fourth quarter. The first (third) month of each trimester exhibits higher (lower) risk-adjusted returns than the remaining months of that quarter. Friday (Wednesday) presents lower (higher) performance compared with other days regarding the risk-adjusted returns. Finally, the seasonal patterns detected in ETFs are not reflected on the indices replicated, indicating that the seasonality patterns are due to idiosyncratic ETFs factors and not to the underlying asset market. The remainder of the article is organized as follows. Section 2 reviews the previous literature on the seasonality of Stocks, Bonds, Funds, and ETFs, Section 3 describes data and the methodology used in this study, and Section 4 presents the empirical results. A conclusion is presented in Section 5. 3 2. Literature Review Numerous studies have been published that identified behavioral patterns in the average returns of several financial assets, mainly stocks. Researchers show evidence of abnormal and persistent returns patterns according to calendar periods. 2.1 Seasonality in Stock Markets (i) January Effect The January effect is characterized by higher average returns in January compared with the remaining months. This phenomenon was documented by Rozeff and Kinney (1976) in a study that focused on several stock indices listed on New York Stock Exchange (NYSE) from 1904 to 1974. The results of that study were, however, highly sensitive to the index used (Ritter, 1988). However, further works confirmed the January effect and revealed that this is a phenomenon primarily affecting the small capitalization companies. Keim (1983) indicated that the average daily returns in January of companies listed in the NYSE and American Stock Exchange (AMEX) for 1963 to 1976 are higher than those of the remaining months, and that the first week and the first trading day are the periods that contribute most to the January effect. Additionally, the same study showed the existence of a negative relation between abnormal returns and size, which was more pronounced in January than in the other months. In another article, Reinganum (1983) used data from the NYSE and AMEX from 1963 to 1980 and also found evidence of high returns in January relative to the remaining months of the year for companies with a small size, mostly in the first trading days. Similarly, Fama (1991) obtained results consistent with the January effect for the US market from 1941 to 1991. The January effect has also been documented in international markets. Gultekin and Gultekin (1983), in an analysis of 17 of the most industrialized countries, found the presence of the January effect for most of those countries from 1959 to 1979. The same effect was documented for Canada between 1950 and 1980 by Berges et al. (1984) and Tinic et al. (1987). Moreover, Corhay et al. (1987) documented the January effect for France and Belgium from 1968 to 1983. In the British market, Reinganum and 4 Shapiro (1987) found evidence of the same effect between 1955 and 1980. In terms of explanations for the phenomenon, Reinganum (1983) suggests tax-loss-selling as a cause of the January effect. This hypothesis states that, at the end of the year, investors sell the stocks that have experienced a decline in price over the year, thus recording losses to reduce the amount of tax to pay. These transactions cause the decline of stock prices at the end of the year. In January, these stocks are repurchased at a lower price, which explains the abnormal returns at the beginning of the year.
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