Pricing behavior of exchange traded funds

Abstract

Stock price performance, Exchange traded fund, Liquidity, G10, G11, G14,

Industry momentum: an exchange‐traded funds approach

“…According to Madura and Ngo (2008), using ETFs to assess the performance of industry momentum has a number of distinct benefits. ETFs can represent specific sectors or indices, which appeals to investors and leads directly to implementing industry momentum strategies; ETF prices are tied to net asset value (NAV) (unlike closed‐end funds); ETFs are structured as trusts to minimise tax distributions; ETFs provide greater tax benefits as they generate fewer capital gains as a result of lower turnover (which only reflects changes in underlying indexes); expenses are low due to passive investment approach (as opposed to the active management style of mutual funds); ETFs can be traded any time the exchange is open (unlike mutual funds); ETFs can be used for shorting and ETFs are widely traded, resulting in relatively low transaction costs and lower management fees, particularly for index ETFs.…”

Factores relacionados a las liquidaciones de ETFs durante 2008

“…The motivation to begin with a model that included the market capitalization, the ETF liquidity and the ETF monthly return came from the article by Madura and Ngo (2008). They identified the characteristics that might affect the future performance of ETFs.…”

“…This study presents one hypothesis for the sign (positive or negative) and significance of each coefficient of explanatory variables included in the model, which accounts for seven hypotheses. Market capitalization was one of the factors that Madura and Ngo (2008) find to be effective as indicators of future performance. They find that market capitalization is inversely related to the performance of ETFs.…”

“…In addition to the factors mentioned in the research conducted by Madura and Ngo (2008) -the market capitalization, the liquidity and the return -the present study included other factors in the model to explain the probability of closure. Lin and Mackintosh (2010) measured the tracking error using the standard deviation of the daily return differences between the index and the ETF.…”

“…The results of the estimation process identified the significant factors explaining the response variable STATUS, which assumes the value of 0 for active ETFs and the value of 1 for liquidated ETFs. The researcher began the model estimation process with the three factors (Model 1) identified by Madura and Ngo (2008), which include the following factors: market capitalization, liquidity, and ETF return. Then the researcher continued the estimation process incrementing the number of explanatory variables used in the model (the researcher added the other factors one by one).…”