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The study investigates the impact of higher moments on cross-sectional returns in the Indian equity market.

Using the daily data of 3,085 Bombay Stock Exchange-listed stocks spanning over 20 years from January 2000 to December 2019, the study evaluates the relationship between higher moments (skewness and kurtosis) and stock returns at individual stock and portfolio levels. The variations in the returns of the equal-weighted and the value-weighted portfolios are analysed, where the portfolios are constructed by sorting the stocks on skewness and kurtosis. The returns are adjusted for five common factors – market excess-returns, size, value, momentum and illiquidity, to controls other cross-sectional effects. Besides, the study employs Fama-MacBeth cross-sectional regression and time-series tests of higher moments as robustness measures.

The study presents higher moments anomaly in the Indian equity market. Contrary to what is expected based on a risk-averse rational agent model, a robust positive relationship is observed between the skewness and stock returns. The relationship between the kurtosis and stock returns is negative, albeit statistically weak. These results are robust for the Fama-MacBeth cross-sectional regression and time-series tests.

It is among the earlier attempts to investigate the pricing impact of higher moments at different levels of asset prices in an emerging market. Besides the standard portfolio methodology for explaining cross-sectional variations, the study also employs the time-series tests for higher moment factors, hence provides more robust results. Results have wider implications for asset pricing in emerging markets and highlight many issues for further research.

The purpose of this paper is to examine the relationships between the higher moments of returns (realized skewness and kurtosis) and subsequent returns at the industry level, with a focus on both empirical predictability and practical application via trading strategies.

Daily returns for 48 US industries over the period 1970–2019 from Kenneth French’s data library are used to calculate the higher moments and to construct short- and medium-term single-sort trading strategies. The analysis adjusts returns for common risk factors (market, size, value, investment, profitability and illiquidity) to confirm whether conventional asset pricing models can capture these relationships.

Past skewness positively relates to subsequent industry returns and this relationship is unexplained by common risk factors. There is also a time-varying effect in which the predictive role of skewness is much stronger over business cycle expansions than recessions, a result consistent with varying investor optimism. However, there is no significant relationship between kurtosis and subsequent industry returns. The analysis confirms robustness using both value- and equal-weighted returns.

The calculation of realized moments conventionally uses high-frequency intra-day data, regrettably unavailable for industries. In addition, the chosen portfolio-sorting method may omit some information, as it compares only average group returns. Nonetheless, the close relationship between skewness and future returns at the industry level suggests variations in returns unexplained by common risk factors. This enriches knowledge of market anomalies and questions yet again weak-form market efficiency and the validity of conventional asset pricing models. One suggestion is that it is possible to significantly improve the existing multi-factor asset pricing models by including industry skewness as a risk factor.

Given the relationship between skewness and future returns at the industry level, investors may predict subsequent industry returns to select better-performing funds. They may even construct trading strategies based on return distributions that would generate abnormal returns. Further, as the evaluation of individual stocks also contains industry information, and stocks in industries with better performance earn higher returns, risks related to industry return distributions can also shed light on individual stock picking.

While there is abundant evidence of the relationships between higher moments and future returns at the firm level, there is little at the industry level. Further, by testing whether there is time variation in the relationship between industry higher moments and future returns, the paper yields novel evidence concerning the asymmetric effect of stock return predictability over business cycles. Finally, the analysis supplements firm-level results focusing only on the decomposed components of higher moments.

The purpose of this paper is to detect the occurrence of anomaly and fault in a spacecraft, investigate various tendencies of telemetry parameters and evaluate the operation state of the spacecraft to monitor the health of the spacecraft.

This paper proposes a data-driven method (empirical mode decomposition-sample entropy-principal component analysis [EMD-SE-PCA]) for monitoring the health of the spacecraft, where EMD is used to decompose telemetry data and obtain the trend items, SE is utilised to calculate the sample entropies of trend items and extract the characteristic data and squared prediction error and statistic contribution rate are analysed using PCA to monitor the health of the spacecraft.

Experimental results indicate that the EMD-SE-PCA method could detect characteristic parameters that appear abnormally before the anomaly or fault occurring, could provide an abnormal early warning time before anomaly or fault appearing and summarise the contribution of each parameter more accurately than other fault detection methods.

The proposed EMD-SE-PCA method has high level of accuracy and efficiency. It can be used in monitoring the health of a spacecraft, detecting the anomaly and fault, avoiding them timely and efficiently. Also, the EMD-SE-PCA method could be further applied for monitoring the health of other equipment (e.g. attitude control and orbit control system) in spacecraft and satellites.

The paper provides a data-driven method EMD-SE-PCA to be applied in the field of practical health monitoring, which could discover the occurrence of anomaly or fault timely and efficiently and is very useful for spacecraft health diagnosis.

The purpose of this paper is to introduce an augmented high-order capital asset pricing model (AH-CAPM) as a new risk-based model to price stocks.

The AH-CAPM is defined as a linear model with high-order marginal moments and co-moments from the joint distributions of the sorted stock portfolio returns and the market return.

The performance of the AH-CAPM is tested in the Chinese and US stock markets. Empirical results show that the high-order marginal moments and co-moments from the joint distributions in AH-CAPM contain the risk and return information implied by the Fama–French factors, indicating it as a better risk measurement. Moreover, the AH-CAPM performs better than the Fama–French three-factor model and the Carhart four-factor model in both the Chinese and US stock markets.

Overall, this study introduces a new asset pricing model with better measurements to incorporate risk information in the stock market.

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.

This paper aims to focus on the tracking of a moving magnetic target by using total field magnetometers and to present a tracking method based on the gradient of a magnetic anomaly. In the tracking, it is assumed that the motion of the target is equivalent to a first-order Markov process. And the unit direction vector of the magnetic moment from the gradient of the magnetic anomaly can be obtained. According to the unit direction vector, the inverse problem is turned into an optimization problem to estimate the parameters of the target. The particle swarm optimization algorithm is used to solve this optimization problem. The proposed method is validated by the numerical simulation and real data. The parameters of the target can be calculated rapidly using the proposed method. And the results show that the estimated parameters of the mobile target using the proposed method are very close to the true values.

The authors focus on the tracking of a moving magnetic target by using total field magnetometers and present a tracking method based on the gradient of a magnetic anomaly.

The paper provides an effective method for tracking the magnetic target based on an array with total field sensors.

Comparing with a vector magnetic sensor, the measurement of the scalar magnetic sensor is almost not influenced by its orientation. In this paper, a moving magnetic target was tracked by using total field magnetometers and a tracking method presented based on the gradient of a magnetic anomaly.

This paper presents a new algorithm for detecting and characterizing potholes and bumps directly from noisy signals acquired using an Accelerometer. A wavelet transformation based filter was used to decompose the signals into multiple scales. These coefficients were correlated across adjacent scales and filtered using a spatial filter. Road anomalies were then detected based on a fixed threshold system, while characterization was achieved using unique features extracted from the filtered wavelet coefficients. Our analyses show that the proposed algorithm detects and characterizes road anomalies with high levels of accuracy, precision and low false alarm rates.

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.

The purpose of this paper is to empirically investigate the Saturday effect in three emerging stock markets (Bahrain, Kuwait, and Saudi Arabia) by taking into consideration the thin trading that is normal in such capital markets.

The paper applies the stochastic dominance (SD) approach, which is not distribution‐dependent and can shed light on the utility and wealth implications of portfolio preferences by exploiting information in higher order moments, to investigate empirically the existence of the Saturday effect in the three Gulf stock markets.

The findings indicate that the Saturday effect does not manifest itself in the three Gulf stock markets and that the SD results show that the Saturday effect in these markets is not present when raw data are corrected for thin and infrequent trading.

This paper is believed to be the first to use SD approach to examine the Saturday effect.

This paper aims to give a brief review on behavioral economics and behavioral finance and discusses some of the previous research on agents' utility functions, applicable risk measures, diversification strategies and portfolio optimization.

The authors also cover related disciplines such as trading rules, contagion and various econometric aspects.

While scholars could first develop theoretical models in behavioral economics and behavioral finance, they subsequently may develop corresponding statistical and econometric models, this finally includes simulation studies to examine whether the estimators or statistics have good power and size. This all helps us to better understand financial and economic decision-making from a descriptive standpoint.

The research paper is original.