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This chapter used empirical data from five developed markets and five emerging markets to perform an examination of anomalies using common financial economic approaches along with more innovative econometric models. Of the methodologies used to test for anomalies, the data-driven panel and quantile regressions were empirically found to be better suited over the traditionally common approaches to describe the non-linear, switching behavior of the anomalies. In the developed markets, the statistically significant small firms (size) had the highest average returns. In the developing markets, the lower price-to-earnings (P/E) ratios (value) had the highest average returns. In addition, the research found (1) a small country effect, (2) sales had a negative relationship with returns, and (3) a lower (higher) book-to-market (B/M) was associated with higher returns in the developed (developing) markets, indicating investors received a higher premium for growth (value) equities. The semi-strong form of the efficient market hypothesis was also found to be violated. The anomalies’ behavior varied between sorted portfolios, industries, and developed to emerging markets; though it was found to be consistent through time (not disrupted by bear or bull markets).

The paper aims to characterize anomaly detection in hyperspectral imagery.

This paper develops an adaptive causal anomaly detector (ACAD) to investigate several issues encountered in hyperspectral image analysis which have not been addressed in the past. It also designs extensive synthetic image‐based computer simulations and real image experiments to substantiate the work proposed in this paper.

This paper developed an ACAD and custom‐designed computer simulations and real image experiments to successfully address several issues in characterizing anomalies for detection, which are – first, how large size for a target to be considered as an anomaly? Second, how an anomaly responds to its proximity? Third, how sensitive for an anomaly to noise? Finally, how different anomalies to be detected? Additionally, it also demonstrated that the proposed ACAD can be implemented in real time processing and implementation.

This paper is the first work on investigation of several issues related to anomaly detection in hyperspectral imagery via extensive synthetic image‐based computer simulations and real image experiments. In addition, it also develops a new developed an ACAD to address these issues and substantiate its performance.

This work contributes to the general problem of justifying the validity of the heuristic that underpins medium imaging using topological derivatives (TDs), which involves the sign and the spatial decay away from the true anomaly of the TD functional. The author considers here the identification of finite-sized (i.e. not necessarily small) anomalies embedded in bounded media and affecting the leading-order term of the acoustic field equation.

TD-based imaging functionals are reformulated for analysis using a suitable factorization of the acoustic fields, which is facilitated by a volume integral formulation. The three kinds of TDs (single-measurement, full-measurement and eigenfunction-based) studied in this work are given expressions whose structure allows to establish results on their sign and decay properties. The latter are obtained using analytical methods involving classical identities on Bessel functions and Legendre polynomials, as well as asymptotic approximations predicated on spatial scaling assumptions.

The sign component of the TD imaging heuristic is found to be valid for multistatic experiments and if the sought anomaly satisfies a bound (on a certain operator norm) involving its geometry, its contrast and the operating frequency. Moreover, upon processing the excitation and data by applying suitably-defined bounded linear operatirs to them, the magnitude component of the TD imaging heuristic is proved under scaling assumptions where the anomaly is small relative to the probing region, the latter being itself small relative to the propagation domain. The author additionally validates both components of the TD imaging heuristic when the probing excitation is taken as an eigenfunction of the source-to-measurement operator, with a focusing effect analogous to that achieved in time-reversal based methods taking place. These findings extend those of earlier studies to the case of finite-sized anomalies embedded in bounded media.

The originality of the paper lies in the theoretical justifications of the TD-based imaging heuristic for finite-sized anomalies embedded in bounded media.

This paper aims to examine the Chinese investment anomaly and dissect it from a perspective of rational expectation framework.

Characteristic-based sorting and Fama–MacBeth two-stage cross-sectional regression are adopted to test the relationship between corporate investment and expected returns in both portfolio and individual stock levels. Under the framework of pricing kernels, an investment-based common risk factor is constructed to test the role of risk played in the negative investment-return relationship. Moreover, a Markov regime switching model is adopted to investigate the time-varying risk premium across market regimes.

Empirical results provide ample evidence showing that there is a negative relationship between investment and expected returns in the Chinese stock market. The new investment-based risk factor is found to capture the return differences across characteristic-based portfolios. In addition, risk premium of the new risk factor is not only statistically positive throughout the sample period, but also has an asymmetry that is higher during market downturn but lower under bull market.

This paper merely tests the hypotheses derived from rational school.

Investment strategies based on characteristic-sorted portfolios should be adjusted to different market regimes.

First, this paper provides comprehensive empirical results by adopting different methodologies for investigating the investment anomaly in China. Second, an investment-based factor is constructed specifically for the Chinese stock market for the first time. Finally, this is the first paper to investigate the asymmetric risk premium across the Chinese bear and bull regimes by using a multivariate Markov regime switching model.

This study aims to examine investors’ herd behaviour for various calendar events and size-based stock portfolios in Pakistan. The authors consider three calendar effects, crisis (COVID-19 and financial crisis 2018–19), announcement of political news and popular calendar anomalies (month-of-the-year and day-of-the-week), and investigate the impact of stock size on calendar effect in terms of investors’ herd behaviour.

The study uses non-linear specification to capture herd behaviour using firm-level daily data for 496 stocks listed on Pakistan Stock Exchange over the period 2001–2020.

The results indicate herd formation during periods of COVID-19, financial crisis, political news announcements and January (month-of-the-year). The authors also observe significant herding for the biggest and smallest size stocks over complete period. However, the authors find more pronounced herding in big stocks during January as compared to the more noticeable herding in small stocks over complete period. The findings suggest that herding in small stocks is not the main cause of January herding and hint on the prevalence of significant institutional herding during January.

The stock prices destabilize because of the mimicking behaviour during crisis periods, days of political announcements and month of January. Implementation of insider trading laws and transparent information environment can help in reducing these effects and increasing market efficiency.

The authors consider the recent COVID period in our analysis. In addition, we provide new evidence on the possible impact of stock size on calendar effect in terms of herd behaviour, which, to the best of the authors’ knowledge, has not yet been documented in literature.

This study uses stochastic dominance (SD) theory to examine whether the traditional festival, such as the Spring Festival (often in February), affects the patterns of monthly anomaly for the Taiwan Stock Exchange (TWSE). The paper aims to discuss these issues.

The authors employ a new bootstrap-based test due to Linton, Maasoumi and Whang (hereafter LMW). The LMW test is well suited for financial time series data, such as monthly returns of various portfolios in this study, because it allows for general dependence among the prospects (distributions) and does not require the observations to be identically and independently distributed.

The particular findings of this study are that the February effect and the February-size effect indeed exist in the TWSE. Furthermore, allowing part of investors' assets is invested in the risky asset and the remaining part in a risk-free asset, first finding for monthly anomaly in the extant literature, is useful in distinguishing the performance among various size-month portfolios.

Instead of tax-loss and window dressing hypothesis, the Spring Festival money movement hypothesis can be used to well explain the findings.

The current study aims at examining the impact of upward and downward earnings management on the cross-sections of stock return. The study also examines the moderating role of cross-sectional effects on the association between earnings management and stock returns.

The study employed univariate and bivariate-sorted portfolio-level analysis to investigate the issue. Fama–Macbeth cross-sectional regression is used to analyze the moderating role of different cross-sectional effects. The study used a sample of 3085 Bombay Stock Exchange (BSE) listed stocks spanning over 20 years from January 2000 to December 2019.

The findings suggest that investors have different perceptions toward different forms of earnings management. In other words, results exhibit that investors perceive downward earnings management as an element of risk; hence, they discount the returns at a higher rate. On the contrary, results show that upward earnings management is positively perceived by the investors; hence, they hold the stocks even at a lower rate of return. This relation is found to be consistent even after controlling the impact of marker effect, size effect, value effect and momentum effect.

This study is among pioneering studies that consider the direction of earnings management while examining its impact on the stock return. This study is also among the earlier attempts to examine the moderating role of four different cross-sectional effects by taking a uniform sample of stocks over the same period.

This paper aims to investigate a relatively new anomaly of investment growth and revisits well-known anomalies of size and value. It aims to answer two main research questions. First, can covariance risks (i.e. factor loadings) be excluded from being determining variables that drive return premiums and explain stock returns? Second, from a behavioral finance standpoint, the authors examine whether using firm characteristics is a more practical and accessible approach and also meets the necessary and sufficient conditions to analyze stock returns.

The authors create the investment-growth-based factor (LMH) which is defined as the return difference between low and high investment growth portfolios. The authors then incorporate the LMH factor along with other characteristic-based factors and their loadings into characteristic-balanced portfolio and three-factor model tests.

The authors find that covariance risks on investment growth, size and value are not necessary as determining variables. Instead, they find that behavioral-related firm characteristics of investment growth, size and value are necessary and sufficient as determinants of return premiums and stock returns.

The results have practical and useful implications for investors in their stock portfolio analysis and selection because firm characteristics are relatively more available than covariance risks that need estimation and typically contain measurement errors.

The paper has practical value to investors in their stock portfolio analysis and selection. Methodologically, in contrast to prior studies that do not directly use the investment growth to control for portfolio characteristics, the use of the newly created LMH factor and its loadings allows us to directly and properly test if the investment growth anomaly is related to the investment growth characteristic that is hypothesized to drive return premiums and determine stock returns from behavioral finance perspectives.

The purpose of this study is to investigate explanations for the behaviour of the size premium using measures of large and small stock holdings of mutual funds.

Returns‐based style analysis is used to measure asset class exposure by regressing equity fund returns on asset class returns over the period 1965 to 2003. The coefficients estimate portfolio asset allocation indicating a fund's investment styles. The estimates from 36‐month rolling regressions of US equity fund returns on various asset classes are aggregated and used as measures of investors' exposure to small stocks. The patterns are analyzed in the context of the behaviour of the abnormal returns to small stocks.

The results indicate the importance of the 1974‐1975 bear market to the historical size premium and support an overreaction and reversal argument. Exposure to small stocks drops dramatically between 1975 and 1977, suggesting a sell‐off of small stocks. Fund exposure subsequently increases rapidly to its highest levels between 1982 and the market crash of 1987. These patterns are consistent with pricing pressure that would lead to the initial undervaluation and subsequent overvaluation driving returns to small stocks over this period.

The study introduces the application of the returns‐based style analysis methodology to studying an asset‐pricing phenomenon and demonstrates important insights that can be obtained from the use of this methodology in new contexts and at an aggregate level.

This paper participates in the debate on market efficiency and correct approach for asset pricing through a comprehensive review of literature in favor, as well as against the long held belief of market efficiency. The purpose of this paper is to understand emerging trends in behavioral finance and establish its future potential as a mainstream alternative theory of asset pricing.

The review and discussion of literature is mainly divided into three different sections that are –theories supporting efficient market hypothesis (EMH); studies providing evidences from the stock market on the failure of EMH and studies on behavioral finance, discussing separately investors’ behavioral biases keeping in mind their effect on stock prices; and providing empirical evidences on the effect of investor sentiment on stock prices.

The review of literature from both the point of views has helped in understanding the market efficiency issue and changing dynamics of asset pricing approach. This is achieved by highlighting the gaps in the concept of market efficiency and also suggesting how these gaps can be bridged with a superior approach such as behavioral finance. Through further discussion of emerging trends in behavioral finance, the paper also points out gaps and how these can be abridged, for behavioral finance to be accepted as a mainstream alternative approach to EMH.

This is an extensive and one of a kind study that discusses market efficiency through discussion of EMH and behavioral finance side by side. With the help of such a study, researchers can precisely understand the need and can focus on the future course of action to make behavioral finance a mainstream approach to asset pricing.