Can Smart Beta Funds Outperform Human-managed Funds Across Market Phases? Evidence from India

Using a comprehensive sample of 164 domestic equity Smart Beta (SB) ETFs during 2003-2014 period, I analyze whether these funds beat their benchmarks by tilting their portfolios to well-known factors such as size, value, momentum, quality, beta and volatility. I then test if Smart Beta funds harvest factor premiums more efficiently than their traditional cap-weighted benchmarks by periodic trading against price movements. While 60% of SB fund categories have beaten their raw passive benchmarks, I find no conclusive empirical evidence to support the hypothesis that SB ETFs outperform their risk-adjusted benchmarks over the studied period. Performance of SB funds is also insignificant when compared with the risk-adjusted blended benchmark that uses existing cap-weighted funds to provide low-cost passive exposure to market, size and value factors. SB ETFs exhibit potentially unintended factor tilts which may work to offset the return advantage from intended factor tilts. After decomposing total allocation component of SB funds into static and dynamic effects, I find that the benefit from dynamic factor allocation is neutral at best. This is consistent with the hypothesis that static factor exposure rather systematic rule-based rebalancing is the main driver of SB ETFs performance.

ABSTRACTThis research paper explores the theme of Smart Beta and the current state of inefficiency of active fund fee structures in South African domestic equity unit trusts. The emerging understanding is that many of the latent sources of value-add (or alpha) of active fund managers are currently accessible in cheaper form via Smart Beta products. Smart Beta products use mechanical and automated rules to establish exposures to tradeable instruments that emulate many of the understood and replicable themes in current active asset management. We sample 91 well-known general equity funds along with nine local Smart Beta funds and demonstrate how disruptive Smart Beta products could be to the fee structures of many of these active funds. We do this by mapping the reproducible elements of the active return of these funds to fungible Smart Beta factors. We conclude with five broad predictions around the active fund management industry in South Africa. Additionally, we focus on how active managers might prudently align themselves with an understanding of what aspects of their value-add are not replicable in order to persist and thrive.

It was early 2015 and executives in iShares' Factor Strategies Group were considering the launch of a new class of exchange-traded funds (ETFs) called smart beta funds. Specifically, the group was considering smart beta multifactor ETFs that would provide investors with simultaneous exposure to four fundamental factors that had shown themselves historically to be significant in driving stock returns: the stock market value of a firm, the relative value of a firm's financial position, the quality of a firm's financial position, and the momentum of a firm's stock price. The executives at iShares were unsure whether there would be demand in the marketplace for such multifactor ETFs, since their value added from an investor's portfolio perspective was unknown. Students will act as researchers for iShares' Factor Strategies Group and conduct detailed analysis of Fama and French's five-factor model and the momentum effect, smart beta ETFs including multifactor ETFs, and factor investing with smart beta ETFs to help iShares make its decision.

This research will discuss the origins of the smart beta and its subsequent trends by explaining its primary characteristics and theoretical background. To address the primary research question, we will conduct a comparative analysis of Smart beta ETFs, actively managed ETFs, and passive ETFs in order to quantify the outperformance of Smart beta strategies. Our research group will be completed by many ETF instruments, with eight distinct ETF funds representing each investment strategy being compared to their respective selected benchmark. The Sharpe, Sortino, and Jensen ratios are used to calculate the overall risk-adjusted returns. We came to two distinct conclusions: US large-capitalization equity Smart Beta funds do not outperform active or passive strategies in terms of risk adjusted returns. Despite this, and contrary to academic research, emerging markets equity smart beta funds had significantly larger excess returns than other types of investment funds.

In 21st century people want each and every gadget and tool to be smart So do they need some smart investment strategies. This article is all about smart beta investment strategies which is combination of both active and passive management strategies. It also Focuses on factors, types growth, pros and cons of Smart Beta Funds. Main aim here is to clarify the concept of Smart beta and how it has been adapted in India.

It looks like exchange-traded funds (ETFs) are eating mutual funds’ lunch. Although we think this is more an index versus active story, it has not stopped mutual funds from looking to convert their active strategies into ETFs. The active ETF space is already alive and well: Active strategies already exist in the form of transparent active and smart beta funds. Although assets in these funds remain small, they are on a growth path that is consistent with that of index funds. The arbitrage mechanism helps active and smart beta ETFs track their net asset values very well, which is good for investors. Nontransparent active ETFs would help larger, active managers hide their trades from predatory traders and might also lower transaction and holding costs to investors. To date, none has been approved because of concerns over their tradeability. We think that they would likely have wider spreads than current ETFs but would trade much better than closed-end funds (which also trade on exchange intraday).

The Exchange Traded Fund (ETF) industry has gained much attractivity among investors in the last decade but remains still a shady market that has some technicities. This paperwork aims to address the origin of this concept and its recent tendencies by describing the main features and theoretical framework that surround it. To address the main research question, we will proceed by establishing a comparative study between Smart ßeta ETF with actively managed ETF instruments and passive ETF instruments to capture if there is an outperformance made by the Smart ßeta strategies. The primary approach for this research is to compute the overall daily returns over a 1-year timeframe1. Several ETF instruments will complete our study group with fifty-one different ETF funds. The overall risk-adjusted returns are computed according to the three main ratios: Sharp, Sortino and Jensen ratio. The first part will serve as a literature review of the critical theoretical concepts to enable the reader to achieve a good base understanding of the topic. The second part will be based on an empirical study to present our findings on this subject. In the end, we will offer a general conclusion that rewinds all the significant contributions on this topic with some recommendations. After studying the performance of fifty-one ETFs, we concluded, as in previous studies, that Smart Beta funds do not offer a risk-adjusted performance superior to active and passive strategies. NB: The data could be altered by the global COVID-19 pandemic and may be biased in term of representativity of the overall state of the market., inducing higher volatility than could impact the neutrality of this analysis.

PurposeThe purpose of this paper is to examine the relative effects of rational and quasi-rational sentiments of individual and institutional investors on a set of smart beta fund returns. The magnitudes of the impacts of institutional investor sentiments are greater than those of individual investor sentiments. In addition, both rational and quasi-rational sentiments of individual and institutional investors have significant impacts on smart beta fund returns. The magnitudes of the impacts of quasi-rational sentiments are greater than those of the rational sentiments for both types of investors (quasi-rational sentiments of institutional investors have the maximum impact). These results are consistent with the arguments that professional investors consider the sentiments of individual investors as contrarian leading indicators which are mainly driven by noise while conform the sentiments of institutional investors which are driven by more rational factors. A majority of smart beta funds in the sample outperform the S&P500 returns in the short term but fail to consistently beat the market. The authors find evidence that smart beta funds with consistently high returns are relatively less (more) driven by individual (institutional) investor sentiments. Overall, the authors argue that smart beta funds appear to follow quasi-rational sentiments of both individual and institutional investors that are not rooted in economic fundamentals.Design/methodology/approachThe results of the impulse functions generated from a multivariate model suggest that the smart beta fund returns are negatively (positively) impacted by individual (institutional) investor sentiments.FindingsThe magnitudes of the impacts of institutional investor sentiments are greater than those of individual investor sentiments. In addition, both rational and quasi-rational sentiments of individual and institutional investors have significant impacts on smart beta fund returns. The magnitudes of the impacts of quasi-rational sentiments are greater than those of the rational sentiments for both types of investors (quasi-rational sentiments of institutional investors have the maximum impact).Originality/valueThese results are consistent with the arguments that professional investors consider the sentiments of individual investors as contrarian leading indicators which are mainly driven by noise while conform the sentiments of institutional investors which are driven by more rational factors. A majority of smart beta funds in the sample outperform the S&P500 returns in the short term but fail to consistently beat the market. The authors find evidence that smart beta funds with consistently high returns are relatively less (more) driven by individual (institutional) investor sentiments. Overall, the authors argue that smart beta funds appear to follow quasi-rational sentiments of both individual and institutional investors that are not rooted in economic fundamentals.

The subject of the study is global changes and the crisis in financial markets, which obviously have a great influence on the formation of changes in investor preferences, as well as related approaches to compiling an investment portfolio of financial assets. Currently, a modern investment portfolio is a collection of different financial assets, selected because of the different reactions of their prices to volatility in market trends, mainly during periods of financial crises. The main issues of forming and managing an investment portfolio using Smart Beta funds were also presented, allowing to reduce risks and uncertainties, ensuring the effective formation and management of a portfolio of financial assets, and its diversification.

In mathematical finance, backtest overfitting connotes the usage of his- torical market data to develop an investment strategy, where too many variations of the strategy are tried, relative to the amount of data avail- able. Backtest overfitting is now thought to be a primary reason why investment models and strategies that look good on paper often disap- point in practice. In this study, we focus on overfitting in the context of designing an investment portfolio or stock fund. We demonstrate a computer program that, given any desired performance profile, designs a portfolio consisting of common securities, such as the constituents of the S&P 500 index, that achieves the desired profile based on in-sample back- test data. Unfortunately, the program also shows that these portfolios typically perform erratically on more recent, out-of-sample data, which is symptomatic of statistical overfitting. Less erratic results can be obtained by restricting the portfolio to positive-weight components, but then the results are quite unlike the target profile on both in-sample and out-of- sample data. One implication of these results is that so-called smart beta funds, which are designed in-sample to deliver a desirable performance profile, are likely to disappoint out-of-sample.

13 - The Low Beta Anomaly and Interest Rates