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This study aims to provide a comprehensive overview of asset pricing research and identifies the general research trends in the area. The study also aims to provide future direction to the researchers in the area of asset pricing.

The study uses bibliometric analysis techniques to achieve the stated purpose. The study covers 3,007 articles published in the top 50 finance and economics journals, accessed from the Scopus database for a period of 47 years (1973–2020). After initial searching for “asset pricing” as the main keyword in “title, abstract, keywords”, the database yields 6,583 articles. This number further reduces to 3,007 articles when the search is restricted to research and review articles published in the top 50 peer-reviewed journals.

The tabular and pictorial representation obtained from the analysis exhibit that asset pricing is an extensively researched area; however, a sudden rise in the number of publications (242) observed for 2019 demonstrates a growing interest amongst researchers. Further, affiliation statistics indicate that the volume of research is mainly concentrated in the USA and other developed nations; hence it opens vistas for the exploration of risk-return dynamics in the context of emerging markets.

The work presents an exhaustive and comprehensive review along with potential research implications. The present study reconciles various contradictory views of the prior studies under asset pricing such as risk-return trade-off, low-risk anomaly and provides the researchers with potential research gaps.

Recent studies have shown that low-volatility shares outperform high-volatility shares. Given the conventional finance theory that risk drives return, this study aims to investigate and attempt to explain the presence of the low-risk anomaly (LRA) in South Africa.

Using share prices from 1990 to 2016, various buy-and-hold strategies are constructed to determine the return to an investor attempting to capitalise on such an anomaly. These strategies involve combinations relating to a price filter, the calculation of risk and volatility, value-weighting or equal-weighting of portfolios and the window period to construct said portfolios.

It was found that the LRA exists on the Johannesburg Stock Exchange (JSE_=) when using univariate sorts, without controlling for the size or value effect. When using multivariate portfolio sorts (size and volatility or value and volatility), it was found that the LRA does not exist on the JSE under the majority of risk proxies, but particularly prevalent when downside risk is used. This loosely points towards a potential “inverse momentum” effect where low-return portfolios outperform their counterparts.

In general, it is established that the risk–return relationship is non-linear and deterministic under traditional proxies, but improves to being somewhat, but not completely, linear under a Kalman filter. The Kalman filter, which can be considered a proxy for learning, does not remove the anomaly in its entirety, indicating that behavioural approaches are needed to explain such phenomena.

This study aims to investigate the risk-return trade-off in the Indian equity market at both the aggregate equity market level and in the cross-sections of stock return using alternative risk measures.

The study uses weekly and monthly data of 3,085 Bombay Stock Exchange-listed stocks spanning over 20 years from January 2000 to December 2019. The study evaluates the risk-return trade-off at the aggregate equity market level using the value-weighted and the equal-weighted broader portfolios. Eight different risk proxies belonging to the conventional, downside and extreme risk categories are considered to analyse the cross-sectional risk-return relationship.

The results show a positive equity premium on the value-weighted portfolio; however, the equal-weighted portfolio of these stocks shows an average return lower than the return on the 91-day Treasury Bills. The inverted size premium mainly causes this anomaly in the Indian equity market as the small stocks have lower returns than big stocks. The study presents a strong negative risk-return relationship across different risk proxies. However, under the subsample of more liquid stocks, the low-risk anomaly regarding other risk proxies becomes moderate except the beta-anomaly. This anomalous relationship seems to be caused by small and less liquid stocks having low institutional ownership and higher short-selling constraints.

The findings have important implications for investors, managers and practitioners. Investors can incorporate the effects of different highlighted anomalies in their investment strategies to fetch higher returns. Managers can also use these findings in their capital budgeting decisions, resource allocations and other diverse range of direct and indirect decisions, particularly in emerging markets such as India. The findings provide insights to practitioners while valuing the firms.

The study is among the earlier attempts to examine the risk-return trade-off in an emerging equity market at both the aggregate equity market level and in the cross-sections of stock returns using alternative measures of risk and expected returns.

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.

High levels of turnover in financial markets are consistent with the notion that trading, like gambling, yields direct utility to some agents. The purpose of this paper is to show that the presence of these agents attenuates covariance risk pricing and volatility, and implies a negative relation between volume and future returns. Since psychological literature indicates that the desirability of a gamble arises from the ex ante volatility of the outcome, the authors propose that agents derive greater utility from trading more volatile stocks. These stocks earn lower average returns in equilibrium, although the risk premium on the market portfolio is positive. The authors then consider a dynamic setting where agents’ utility from trading increases when they make positive profits in earlier rounds (e.g. due to an endowment effect). This leads to “bubbles,” i.e. disproportionate jumps in asset returns as a function of past prices, higher volume in up markets relative to down markets, as well as a leverage effect, wherein down markets are followed by higher volatility than up markets.

Analytical.

The presence of gamblers attenuates covariance risk pricing and volatility, and implies a negative relation between volume and future returns. If gamblers prefer more volatile stocks, these stocks earn lower average returns in equilibrium. If agents’ utility from trading increases when they make positive profits in earlier rounds (e.g. to an endowment effect), this leads to higher volume and lower volatility in up markets relative to down markets.

No paper has previously modeled agents who derive direct utility from trading.

Does Fortune magazine’s list of the 100 Fastest-Growing Companies have information content; that is, is the list a source for market-beating performance? The paper aims to discuss this issue.

Using data for 26 annual periods, 1991–2016, the paper examines the top 5, 10, 25, 50 and all 100 stocks on a return-risk basis, including an application of Modern Portfolio Theory. To generate portfolio performance metrics, the study uses conventional mean-variance analysis, which includes the estimation of returns and risks, where risk will be measured by standard deviation and β. To arrive at the performance metrics and to determine whether information content is embedded in the list, the study reviews a series of tests. Because Fortune ranks the companies from 1 to 100, the data can be used to test if information content is displayed in sub-groups, such as in the first five to ten companies, even if it does not exist in the 100-stock portfolios.

The study finds that the returns are not high enough nor are the risks low enough statistically to conclude the existence of significant information content.

As part of the authors’ efforts to move to the population of 2,600 firms as closely as possible, the authors use “delisting” returns from CRSP on 120 firms to account for missing observations, with a final sample size of 2,594 firms.

The evidence indicates that investors drawn to Fortune’s “100 Fastest-Growing Companies” should view them skeptically as a source for an effective stock selection strategy.

On the basis of the results of this study, readers will conclude that subscribers drawn to Fortune’s “100 Fastest-Growing Companies” should view them skeptically for investment recommendations. From a portfolio perspective, the study is unable to uncover information content that could lead to a market-beating performance, suggesting that the published criteria Fortune uses to select the Fastest-Growing Companies is embedded in the prices of the stocks even before Fortune publishes its list. The study notes that the selection criteria used by Fortune do involve some judgments on the part of the editorial staff (e.g. whether an announced restatement of previously reported financial data appears to have a significant impact), which means that someone who wished to anticipate the publication of the next list of the “Fastest-Growing Companies” would not only have to gather information but would also have to correctly anticipate these judgment calls.

This paper's aim is to examine the risk‐adjusted market performance of an overall franchise and three sub‐sector franchise common stock portfolios from 1990 through 2008.

Four sets of franchise sector portfolios are constructed, their returns are calculated, and their performances relative to three market benchmarks are evaluated using the Sharpe ratio and Jensen's α.

The all franchise portfolio significantly outperformed the three market benchmarks. Among the sector portfolios, the services and restaurant portfolios also outperformed the market benchmarks, but not the lodging portfolio. Results support the theoretical hypothesis that franchising may provide superior advantages to investors and point to a possible “franchising anomaly”. Investors consider franchise firms to be less risky than the average publicly traded firms and therefore require a lower rate of return.

The results of the study suggest that in the past, franchise managers may have paid a much higher cost of capital than warranted by their firms' risk characteristics. Study results also have positive implications for franchise firms' access to capital and for evaluating franchise managers' compensation arrangements. Investors should consider allocating a portion of their investible funds to franchise stocks. Many lodging firms may not have taken full advantage of the benefits of franchising to reduce their financial risks. Restaurant firms may further improve their financial performance by selling their riskier units.

This is the first comprehensive study of the risk‐adjusted market performance of franchise firms over an extended period of time covering a variety of economic conditions that also analyzes the risk‐adjusted performance of the main business subcategories in franchising.

Motivated by the recent theoretical rehabilitation of mean-variance analysis, the authors revisit the question of whether minimum variance (MinVar) or maximum Sharpe ratio (MaxSR) investment weights are preferable in practical portfolio formation.

The authors answer this question with a focus on mainstream investors which can be modeled by a preference for simple portfolio optimization techniques, a tendency to cling to past asset characteristics and a strong interest in index products. Specifically, in a rolling-window approach, the study compares the out-of-sample performance of MinVar and MaxSR portfolios in two asset universes covering multiple asset classes (via investable indices and their subindices) and for two popular input estimation methods (full covariance and single-index model).

The authors find that, regardless of the setting, there is no statistically significant difference between MinVar and MaxSR portfolio performance. Thus, the choice of approach does not matter for mainstream investors. In addition, the analysis reveals that, contrary to previous research, using a single-index model does not necessarily improve out-of-sample Sharpe ratios.

The study is the first to provide an in-depth comparison of MinVar and MaxSR returns which considers (1) multiple asset classes, (2) a single-index model and (3) state-of-the-art bootstrap performance tests.

In 2018, the International Financial Reporting Standard 9-Financial Instruments became mandatory, effectively changing the underlying accounting principles of financial instruments. This paper systematically reviews the academic literature on the implementation effects of IFRS 9, providing a coherent picture of the state of the empirical literature on IFRS 9.

The study thrives on a systematic review approach by analyzing existing academic studies along the following three broad categories: adoption and implementation, impact on financial reporting, and risk management and provisioning. The study concludes by providing research prospects to fill the identified gaps.

We document data-related issues, forecasting uncertainties and the interaction of IFRS 9 with other regulatory standards as implementation challenges encountered. Also, we observe cross-country heterogeneity in reporting quality. Furthermore, contrary to pre-implementation expectations, we find improvement in risk management. This suggests that despite the complexities of the new regulatory standard on financial instruments, it appears to be more successful in achieving the intended objective of enhancing better market discipline and transparency rather than being a regulatory overreach.

As the literature on IFRS 9 is burgeoning, we provide state-of-the-art guidance and direction for researchers with a keen interest in the economic significance and implications of IFRS 9 adoption. The study identifies gaps in the literature that require further research, specifically, IFRS 9 adoption and firm’s hedging activities, IFRS 9 implications on non-financial firms. Lastly, existing studies are mostly focused on Europe and underscore the need for more research in under-researched jurisdictions, particularly in Asia and Africa. Also, to standard setters, policymakers and practitioners, we provide some insight to aid the formulation and application of standards.