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The 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.

The 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.

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.

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 study aims to compare the fundamental indexation (FI) portfolio vis-à-vis the cap-weighted index (CWI). It also explored the return-generating attributes of the FI portfolios.

This study extracted relevant data from the Centre for Monitoring Indian Economy’s Prowess database from March 1996 to March 2017 from a sample of National Stock Exchange (NSE) 500 companies. The FI portfolios were constructed with First_50 and Next_50 stocks using the latest and five years of trailing average aggregations. Further, the regression technique was used to identify the return-generating attributes of FI portfolios.

It was found that the FI portfolios based on First_50 and Next_50 stocks outperformed the CWI (i.e. NSE_First_50 and NSE_Next_50) in the Indian capital market, and between the two, the FI portfolios based on Next_50 stocks were superior to the FI portfolios based on First_50 stocks. The cross-sectional superiority of FI portfolios is obvious if they are sorted according to four fundamentals, namely, total income, sales, operating cash flows and profit before depreciation interest tax and amortisation. The return-generating process of FI portfolios is well-explained by market premium followed by value premium and investment premium.

This study may enable portfolio managers and investors to measure FI portfolios’ superiority in the Indian capital market and identify the return-generating attributes of FI portfolios so that the loadings can be switched amongst different priced factors for higher yield. Further, this study extends the FI literature, providing evidence from one of the world’s fastest-growing economies.

To the best of the knowledge, this is amongst the first few studies to explore the performance of FI portfolios vis-à-vis CWIs in India, and to use Fama and French (2015) asset pricing models to understand the return-generating attributes of FI portfolios. It is also novel in the sense that it considers the FI portfolios for a longer duration, predating 1997 and coinciding with the inception of CWIs, namely, NSE_First_50 (inception: 1995) and NSE_Next_50 (inception: 1996), reducing the apprehensions of data-snooping biases.

Recently, due to its passive property, the smart beta has become one of the most interest topics in searching the alpha. In this paper, we attempt to show whether the smart beta strategy generate abnormal excess return, in tradition, which are known as the exclusive property of active fund. Further, we attempt to verify the key drivers of the alpha in the smart beta portfolios. For this purpose, we categorize various smart beta strategies by their scheme for asset picking and risk reduction. Then, based on our categorization, we evaluate and analyze the performance of smart beta strategy in perspective. Our empirical analyses show following results: applying alternative risk reduction scheme to traditional market index portfolio would results in enhanced efficiency; however, without combining any asset picking scheme, the performance of the smart beta portfolio seems explained by the Fama-French 3 factor. Our results lead us to conjecture that it is not the portfolio weighting scheme alone but in association with asset selection scheme that generate significant alpha in the smart beta strategy. In actual practice, our results imply that any passive fund may succeed in seeking the alpha without active strategy, thereby avoiding the risk of market timing and saving the management cost.

The capital asset pricing model has failed to explain the effect of systematic risk (referred to as beta) on actual stock market returns. Accordingly, this study analyzes daily returns by splitting it into overnight and daytime returns. The study analysis empirically confirms a positive relationship between overnight returns and beta and a negative relation between daytime returns and beta. Furthermore, this paper aims to determine that empirical results are mostly the same with three different beta calculations, namely, daily, overnight and daytime returns. The study concludes that beta on overnight returns has the strongest explanatory power and is statistically significant.

This study aims to investigate the influence of smart technologies on SMEs sustainability and to measure the mediation effect of SMEs’ sustainability strategy in the relationship between smart technologies and SMEs’ sustainable performance in the Kingdom of Bahrain. The sustainability concept for the purpose of this study includes environmental sustainability, social sustainability and profitability factors.

The study applied the quantitative analysis method. The sample size was 403 small- and medium-sized enterprises (SMEs) from Bahrain.

The study concludes that smart technology has a major effect on profitability performance, among other sustainable performance factors. In addition, there is no mediation effect of “SMEs’ sustainability strategy”. The study has recommended improving SMEs’ participation in sustainable development principles by considering supportive global initiatives to “Net Zero Roadmap 2050”, increasing the demand for using technologies and including academic “sustainability” concepts in academic programs.

This study contributes significantly to Bahrain’s economic growth by studying the proactive and innovative methods for increasing SMEs’ efficiency. Furthermore, it adds value to Bahrain’s national economy by investigating the role of SMEs and its strategic practices by implementing smart businesses towards developing business empowerment in Bahrain’s economic vision for 2030 and meeting SDGs regionally and globally.

The portfolio of low-risk stocks outperforms the portfolio of high-risk stocks and market portfolios on a risk-adjusted basis. This phenomenon is called the low-risk effect. There are several economic and behavioral explanations for the existence and persistence of such an effect. However, it is still unclear whether specific sector orientation drives the low-risk effect. The study seeks to answer the following important questions in Indian equity markets: (a) Whether sector bets or stock bets mainly drive the low-risk effect? (b) Is it a mere proxy for the well-known value effect? (c) Does the low-risk effect prevail in long-only portfolios?

The study is based on all the listed stocks on the National Stock Exchange (NSE) of India from December 1994 to September 2018. It classifies them into 11 Global Industry Classification Standard (GICS) sectors to construct stock-level and sector-level BAB (Betting Against Beta) and long-only low-risk portfolios. It follows the study of Asness et al. (2014) to construct various BAB portfolios. It applies Fama–French (FF) three-factor and Fama–French–Carhart (FFC) four-factor asset pricing models in addition to Capital Asset Pricing Model (CAPM) to examine the strength of BAB, sector-level BAB, stock-level BAB and long-only low-beta portfolios.

Both sector- and stock-level bets contribute to the return of the low-risk investing strategy, but the stock-level effect is dominant. Only betting on safe sectors or industries will not earn economically significant alpha. The low-risk effect is unique and not a value effect in disguise. Both long-short and long-only portfolios within sectors and industry groups deliver positive excess returns. Consumer staples, financial, materials and healthcare sectors mainly contribute to the returns of the low-risk effect in India. This study offers empirical evidence against the Samuelson (1998) micro-efficient market given the strong performance of the stock-level low-risk effect.

The superior performance of the low-risk investment strategies at both stock and sector levels offers investors an opportunity to strategically invest in stocks from the right sectors and earn high risk-adjusted returns with lower drawdowns over an entire market cycle. Besides, it paves the way for stock exchanges and index manufacturers to launch sector-specific low-volatility indices for relevant sectors. Passive funds can launch index funds and exchange-traded funds by tracking these indices. Active fund managers can espouse sector-specific low-risk investment strategies based on the results of this and similar other studies.

The study is the first of its kind. It offers insights into the portfolio characteristics and performance of the long-short and the long-only variant of low-risk portfolios within sectors and industry groups. It decomposes the low-risk effect into sector-level and stock-level effects.

This paper focuses on the strategic application based on the empirical results of risk-return relationship against the classical concept. Empirical analysis from domestic data, we verify that the traditional concept-‘high risk, high return’ relationship are maintained, however, we confirm the falling pattern in the highest total volatility group. Even though we implies double sorting method to control the well known systematic factor such as BM and size, we still confirm such abnormal risk-return relationship. Furthermore, we perform sub-period analysis before and after the liberalization of Korean capital market and we find such abnormal risk-return relationship is appeared after the liberalization. Based on our empirical results, we establish and verify the new benchmark that evenly allocate highest volatility portfolio to sub-volatility portfolio. Under the new benchmark, we confirm the expansion of the efficient frontier and the improvement of Sharpe ratio. We believe that our results provide an applicability research of smart beta strategy and new benchmark based on such strategy. We expect our research to be used as preliminary study to overcome the era of “new normal” and to reform the investment strategies correspond to segmentation of benchmark.