THE SPILLOVER, RISK AND LEVERAGE EFFECTS OF SMART BETA MANAGEMENT EXCHANGE-TRADED FUND (ETF)

This research uses two different GARCH models to measure spillover, risk, and leverage effects of active, passive, and smart beta management Exchange-traded Funds (ETFs). The increase in popularity of ETFs and new categories within them, specifically the growth of smart beta management, means asset managers and investors have new metrics to account for when determining portfolio exposure following the Adaptive Investment Approach (AIA). The results show significant relationships among all groups regarding the spillover. A trend of positive multi-lateral spillover of returns among the three management types including passive, active and small beta is observed with smart beta showing the highest percentage of a bi-lateral positive effect. The strongest spillover of volatility effects is among the actively managed ETFs. The testing of risk results is insignificant, but the leverage effect results are consistent with the past studies showing the significant negative bi-lateral effect.

The adaptive market hypothesis and high frequency trading

This paper uses NASDAQ order book data for the S&P 500 exchange traded fund (SPY) to examine the relationship between one-minute, informational market efficiency and high frequency trading (HFT). We find that the level of efficiency varies widely over time and appears to cluster. Periods of high efficiency are followed by periods of low efficiency and vice versa. Further, we find that HFT activity is higher during periods of low efficiency. This supports the argument that HFTs seek profits and risk reduction by actively processing information, through limit order additions and cancellations, during periods of lower efficiency and revert to more passive market-making and rebate-generation during periods of higher efficiency. These findings support the argument that the adaptive market hypothesis (AMH) is an appropriate description of how prices evolve to incorporate information.

THE INFLUENCES OF STOCK MARKET ON EXCHANGE-TRADED FUND UNDER THE THRESHOLD EFFECT OF MONETARY POLICY IN CHINA

This paper estimates the smooth transition autoregressive model with exogenous variables to evaluate the effects of stock market returns on the exchange-traded funds’ (ETFs) returns in China with reserve requirement ratio (RRR) from monetary policy as a transition variable. The sample used in this study lasts from March 4, 2005 to June 30, 2017. The empirical result points out that there is the effect of RRR value on the relationship between stock market returns and ETF return. Moreover, these effects are variable depending on the conversion and its changes over time in different variations of threshold intervals. Lastly, the larger the change of China’s stock market variables’ lag period, the smaller the impacts on Chinese ETF return.

Does an Exchange-Traded Fund Converge to its Benchmark in the Long Run? Evidence from Ishares MSCI in Asia-Pacific Countries

This study examines the linkages between exchange-traded funds (ETFs) and their benchmark indices from 2013 to 2019 using iShares MSCI of ten Asia-Pacific countries. Our results show, first, that there is a long-run causality running from the benchmark index to ETFs. These findings imply that ETFs may replicate the performance of the benchmark index over the long run. Second, there is a unidirectional causal relationship from ETFs to the benchmark index in the short run, which indicates that benchmark index prices respond to the short-run changes in the ETF prices when new information is available. Third, there is a significant tracking error between ETFs and the benchmark index. This finding justifies the existence of stock selection and market timing abilities among the ETF managers. Lastly, fund managers add value to the ETFs and generate better than the market returns. This paper provides new evidence to support this new stylized fact of ETFs.

Last hour momentum in the Chinese stock market

PurposeTo capture the last hour momentum over the intraday session, the authors develop a trading strategy for the exchange-traded fund (ETF) that is effective because of the T+0 trading rule. This strategy generates annualized excess return of 9.673%.Design/methodology/approachIn this study, the authors identify a last hour momentum pattern in which the sixth (seventh) half-hour return predicts the next half-hour return by employing high frequency 2012–2017 data from the China Securities Index (CSI) 300 and its ETF.FindingsOverall, both the predictability and the trading strategy are statistically and economically significant. In addition, the strategy performs more strongly on high volatility days, high trading volume days, high order-imbalance days and days without economic news releases than on other days.Originality/valueNoise trading, late-information trading, infrequent rebalancing and disposition effects from retail investors may account for this phenomenon.

THE ROLE OF EXCHANGE TRADED FUND TO DRIVE THE GROWTH OF THE MUTUAL FUND INDUSTRY IN THE CAPITAL MARKET ACCORDING TO A REVIEW OF ISLAMIC LAW

As a product that has just been listed on the stock exchange, this Exchange Traded Fund (ETF) has attracted the attention of various lines of capital market investors. ETF is known as one of the mutual fund product innovations as a follow-up to POJK No. 49/POJK.04/2015. This study is a descriptive study of this product with all the features and advantages and uniqueness it has as a form of diversification of existing mutual fund products. This study also seeks to explore the strengths and weaknesses of ETFs both internally and externally. Interestingly, this descriptive study presents data on investor enthusiasm for ETF products. One of the main attractions of this product is that apart from being traded on the stock exchange, the price is also very cheap. Not only explaining in terms of products and their uniqueness, but this study also discusses the review of Islamic law on ETF transactions on the stock exchange. When compared with the practice of buying and selling, there are many similarities. And there are several other interesting factors to observe regarding the considerations of investors in choosing mutual fund instruments, especially ETFs as an alternative to new investments in the capital market.

Investor Taxes and Option Prices

We examine whether tax-sensitive investors play a significant role in options markets by examining whether option prices reflect investor taxes. Existing empirical option pricing literature ignores taxes. We exploit a unique setting where “index” options on the S&P 500 Index (SPX) and nearly identical “non-index” options on the exchange traded fund (ETF) tracking the S&P 500 Index (SPY) face different tax treatments. We find that higher investor taxes reduce option prices, indicating tax capitalization in options. We find consistent results when analyzing options around the investor tax changes enacted by the American Taxpayer Relief Act (ATRA) of 2012, and for options on stock indices other than the S&P 500 (e.g., Russell 2000). Altogether, our findings provide new evidence of an additional item – investor taxes – influencing option prices, suggesting that tax-sensitive investors play a non-trivial role in options markets and that taxes warrant consideration in broader options research.

Smart Beta Allocation and Macroeconomic Variables: The Impact of COVID-19

Smart beta strategies across economic regimes seek to address inefficiencies created by market-based indices, thereby enhancing portfolio returns above traditional benchmarks. Our goal is to develop a strategy for re-hedging smart beta portfolios that shows the connection between multi-factor strategies and macroeconomic variables. This is done, first, by analyzing finite correlations between the portfolio weights and macroeconomic variables and, more remarkably, by defining an investment tilting variable. The latter is analyzed with a discriminant analysis approach with a twofold application. The first is the selection of the crucial re-hedging thresholds which generate a strong connection between factors and macroeconomic variables. The second is forecasting portfolio dynamics (gain and loss). The capability of forecasting is even more evident in the COVID-19 period. Analysis is carried out on the iShares US exchange traded fund (ETF) market using monthly data in the period December 2013–May 2020, thereby highlighting the impact of COVID-19.