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It has been demonstrated in the US market that expected market excess returns can be predicted using the average higher-order moments of all firms. This study aims to empirically test this theory in emerging markets.

Two measures of average higher moments have been used (equal-weighted and value-weighted) along with the market moments to predict subsequent aggregate excess returns using the linear as well as the quantile regression model.

The authors report that both equal-weighted skewness and kurtosis significantly predict subsequent market returns in two countries, while value-weighted average skewness and kurtosis are significant in predicting returns in four out of nine sample markets. The results for quantile regression show that the relationship between the risk variable and aggregate returns varies along the spectrum of conditional quantiles.

This is the first study that investigates the impact of third and fourth higher-order average realized moments on the predictability of subsequent aggregate excess returns in the MSCI Asian emerging stock markets. This study is also the first to analyze the sensitivity of future market returns over various quantiles.

The purpose of this paper is to discuss how numerous tests that are available in statistical literature to assess normality of a given set of observations perform in normal and near-normal situations. Not all these tests are suitable for all situations but each test has an exclusive area of application.

These tests are assessed for their power at varying degrees of skewness, kurtosis and sample size on the basis of simulated experiments.

It is observed that almost all these tests are indifferent for smaller values of skewness and kurtosis. Further, the power of accepting normality reduces with increasing sample size.

The article gives guidelines to researchers to apply normality assessing tests in different situations.

Studies the necessity of controlling the variation of the skewness of the process distribution in order to reduce the product scrap. Proposes a γ control chart for detecting the skewness shift, also implements a simulation procedure to decide the control limits of the γ chart.

The purpose of this paper is to examine the significance of skewness in maximizing the investor utility using the daily data for eight sectors listed on the National Stock Exchange of India.

The analysis is carried out in three different steps. In the first part, the author analyzes the monthly stock returns and the important financial ratios – price-to-book (PB) ratio, price-earnings (PE) ratio and dividend yield (DY). Second, the author tests the sector-wise return predictability using Westerlund and Narayan (2012) flexible generalized least squares estimator. Third, the author compares the mean–variance–skewness (MVS) utility function with the mean–variance (MV) utility function.

The author forecasts the sectoral stock returns using three financial ratios – PB ratio, PE ratio and DY – as predictors. The results indicate that sectoral stock returns are significantly predicted by these financial ratios. The author then formulates trading strategies by including skewness in the utility function and finds that the investor utility is high when the utility function includes skewness as opposed to when the skewness is excluded.

The author extends the MV utility function to the MVS utility function and shows that the Indian stock market is more profitable when the investor uses a MVS utility function which highlights the main contribution to the literature.

This paper investigates changes in risk-neutral distribution derived from Taiwan stockindex options under different market conditions. The purpose of this paper is to explore whether individual investor sentiment significantly influences the Taiwan option prices.

The authors adopt the optimization method to estimate the risk-neutral distribution from the Taiwan stock index options and use the t-test to examine the difference in risk-neutral skewness, kurtosis, and confidence interval between the pre-crisis and crisis periods. This paper tests the impact of individual investor sentiment on risk-neutral skewness and confidence interval in two sub-periods.

The authors find that errors in individual investors’ expectations significantly influence the Taiwan stock index option prices.

The data concerning the sentiment of speculative institutional investors are incomplete for the Taiwan option market. Therefore, this paper focusses on the analysis of individual investor sentiment. Further research can study the impact of institutional investor sentiment in emerging markets.

The previous literature has suggested that option prices reflect information before the information is revealed in stock prices. Therefore, an important implication is to analyze the information quality revealed in option prices by studying whether the changes in option prices are due to investor sentiment or non-sentiment-related components.

Most of the studies in the literature have focussed on the US option market, and their applicability may vary across different microstructures. This paper shows that the influence of individual investor sentiment in an emerging market is different from that in the US market.

This paper aims to investigate the correlation between stock returns of the parent and newly created entity and the degree of return skewness in parents in the three different corporate restructurings.

Using a sample of spin-offs, equity carve-outs and tracking stocks, ordinary least squares regression is used to test the relationship between stock return correlation as well as stock return skewness and the type of corporate restructurings.

Tracking stock offering has the largest correlation in stock returns, whereas spin-off has the least correlation in stock returns. Also, the result from the skewness test is not consistent with the hypothesis that the stock returns skewness is positively related to the degree of ownership and control.

This is one of the few papers looking at the three corporate restructurings and their return skewness.

The scalability of similarity joins is threatened by the unexpected data characteristic of data skewness. This is a pervasive problem in scientific data. Due to skewness, the uneven distribution of attributes occurs, and it can cause a severe load imbalance problem. When database join operations are applied to these datasets, skewness occurs exponentially. All the algorithms developed to date for the implementation of database joins are highly skew sensitive. This paper presents a new approach for handling data-skewness in a character- based string similarity join using the MapReduce framework. In the literature, no such work exists to handle data skewness in character-based string similarity join, although work for set based string similarity joins exists. Proposed work has been divided into three stages, and every stage is further divided into mapper and reducer phases, which are dedicated to a specific task. The first stage is dedicated to finding the length of strings from a dataset. For valid candidate pair generation, MR-Pass Join framework has been suggested in the second stage. MRFA concepts are incorporated for string similarity join, which is named as “MRFA-SSJ” (MapReduce Frequency Adaptive – String Similarity Join) in the third stage which is further divided into four MapReduce phases. Hence, MRFA-SSJ has been proposed to handle skewness in the string similarity join. The experiments have been implemented on three different datasets namely: DBLP, Query log and a real dataset of IP addresses & Cookies by deploying Hadoop framework. The proposed algorithm has been compared with three known algorithms and it has been noticed that all these algorithms fail when data is highly skewed, whereas our proposed method handles highly skewed data without any problem. A set-up of the 15-node cluster has been used in this experiment, and we are following the Zipf distribution law for the analysis of skewness factor. Also, a comparison among existing and proposed techniques has been shown. Existing techniques survived till Zipf factor 0.5 whereas the proposed algorithm survives up to Zipf factor 1. Hence the proposed algorithm is skew insensitive and ensures scalability with a reasonable query processing time for string similarity database join. It also ensures the even distribution of attributes.

This paper analyzes the roles of idiosyncratic risk and firm-level conditional skewness in determining cross-sectional returns. It is shown that the traditional EGARCH estimates of conditional idiosyncratic volatility may bring significant finite sample estimation bias in the presence of non-Gaussianity. We propose a new estimator that has more robust sampling performance than the EGARCH MLE in the presence of heavy-tail or skewed innovations. Our cross-sectional portfolio analysis demonstrates that the idiosyncratic volatility puzzle documented by Ang, Hodrick, Xiang, and Zhang (2006) exists intertemporally. We conduct further analysis to solve the puzzle. We show that two factors idiosyncratic variance and individual conditional skewness play important roles in determining cross-sectional returns. A new concept, the “expected windfall,” is introduced as an alternate measure of conditional return skewness. After controlling for these two additional factors, we solve the major piece of this puzzle: Our cross-sectional regression tests identify a positive relationship between conditional idiosyncratic volatility and expected returns for over 99% of the total market capitalization of the NYSE, NASDAQ, and AMEX stock exchanges.

This chapter derives sentiment indicators (implied volatility and implied skewness) from the option pricing models of Corrado and Su (1996), Bakshi, Kapadia, and Madan (2003), and Zhang, Zhen, Sun, and Zhao (2017), and then integrates these sentiment indicators with artificial intelligence deep neural network (AIDNN) for developing the behavioral finance AIDNN (BFAIDNN) algorithms. We apply the BFAIDNN algorithms to daily derivatives data of Taiwan Futures and Options markets from 2015 to 2017. Our results demonstrate that the trading strategies established by the BFAIDNN algorithms can generate positive rewards.

The purpose of this paper is to investigate the local feature of driven granular gases in event-driven molecular dynamic simulation, in order to achieve spatial profiles of local velocity distribution and granular temperature, and the local state with various coefficients of restitution.

Event-driven molecular dynamic simulation is performed to study a vibro-fluidized granular gas system. Triangular-wave vibration is adopted in the simulation. The authors focus on the steady state of a driven granular gas.

The simulation finds the local velocity distribution is asymmetric along vibration direction in this driven granular gas system, which agrees with the experimental results obtained in micro-gravity. A nonlinear spatial profile of the skewness of local velocity distribution in vibration direction is found in the simulation. Furthermore, it is found that the value of skewness increases with the system dissipation. It is also found that the two temperature components T+ and T− differ from each other. This shows breakdown of energy equipartition. The ratio between them drops exponentially along y direction in various coefficients of restitution. All results confirm that the bulk boundary effect relates to the dissipation properties of granular gases.

This is the first MD simulation that investigates the bulk boundary effect to the local velocity distribution. The spatial profiles of the skewness of local velocity distribution are also investigated when changing the coefficient of restitution to study the influence of the system dissipative nature.