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The purpose of this paper is to postulate that market participants’ views on the nature of discretionary accruals as earnings management or earnings manipulation could relate to a rise or a fall in a firm’s stock prices.

Applying the quantile regression and measuring gains and losses according to the stock returns, this study shows that the relation between earnings manipulation and stock returns is non-uniform and it varies significantly across various quantiles of the latter.

The empirical results imply a positive (negative) |DA|-RETURN relation for stocks experiencing a rise (fall) in stock prices. This finding is consistent with the notion that market participants lean towards (become) trend followers (fundamentalists) when their stocks price rise (fall) and, thus, positively reward (negatively punish) discretionary accruals.

Using the behavioural heterogeneity of market participants as a research framework, this paper contributes to the literature by demonstrating that market participants’ decisions to positively reward (negatively punish) earning management behaviour depend on their perceptions on nature of discretionary accruals (earnings management vs earnings manipulation).

The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory.

This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integrated moving average (ARFIMA)-FIGARCH model and ARFIMA-asymmetric power autoregressive conditional heteroscedasticity (APARCH) models, and unconditional volatility realized volatility using autoregressive integrated moving average (ARIMA) and ARFIMA in-sample forecasting models to estimate the persistence of the long-term memory.

Given the GARCH framework, the ARFIMA-APARCH long-memory model gave the better forecast results signifying the importance of accounting for asymmetric information when modelling volatility in a financial market. Using the unconditional realized volatility results from the Singapore and Indian markets, the ARIMA model outperforms the ARFIMA model in terms of forecast performance and provides reasonable forecasts.

The issue of long memory has important implications for the theory and practice of finance. It is well-known that accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management.

It could be said that using long-memory augmented models would give better results to investors so that they could analyse the market trends in returns and volatility in a more accurate manner and reach at an informed decision. This is useful to minimize the risks.

This research enhances the literature by estimating the influence of intraday variables on daily volatility. This is one of very few studies that uses conditional GARCH framework models and unconditional realized volatility estimates for forecasting long-term memory. The authors find that the methods complement each other.

The purpose of this paper is to develop a new finite difference method for solving the seismic wave propagation in fluid-solid media, which can be described by the acoustic and viscoelastic wave equations for the fluid and solid parts, respectively.

In this paper, the authors introduced a coordinate transformation method for seismic wave simulation method. In the new method, the irregular fluid–solid interface is transformed into a horizontal interface. Then, a multi-block coordinate transformation method is proposed to mesh every layer to curved grids and transforms every interface to horizontal interface. Meanwhile, a variable grid size is used in different regions according to the shape and the velocity within each region. Finally, a Lebedev-standard staggered coupled grid scheme for curved grids is applied in the multi-block coordinate transformation method to reduce the computational cost.

The instability in the auxiliary coordinate system caused by the standard staggered grid scheme is resolved using a curved grid viscoelastic wave field separation strategy. Several numerical examples are solved using this new method. It has been shown that the new method is stable, efficient and highly accurate in solving the seismic wave equation defined on domain with irregular fluid–solid interface.

First, the irregular fluid–solid interface is transformed into a horizontal interface by using the coordinate transformation method. The conversion between pressures and stresses is easy to implement and adaptive to different irregular fluid–solid interface models, because the normal stress and shear stress vanish when the normal angle is 90° in the interface. Moreover, in the new method, the strong false artificial boundary reflection and instability caused by ladder-shaped grid discretion are resolved as well.

This study aims to provide a new method of multiscale directional Lyapunov exponents (MSDLE) calculated based on the state space reconstruction for the nonstationary time series, which can be applied to detect the small target covered by sea clutter.

Reconstructed state space is divided into non-overlapping submatrices whose columns are equal to a predetermined scale. The authors compute eigenvalues and eigenvectors of the covariance matrix of each submatrix and extract the principal components σ_ip and their corresponding eigenvectors. Then, the angles ψ_ip of eigenvectors between two successive submatrices were calculated. The curves of (σ_ip, ψ_ip) reflect the nonlinear dynamics both in kinetic and directional and form a spectrum with multiscale. The fluctuations of (σ_ip, ψ_ip), which are sensitive to the differences of backscatter between sea wave and target, are taken out as the features for the target detection.

The proposed method can reflect the local dynamics of sea clutter and the small target within sea clutter is easily detected. The test on the ice multiparameter imaging X-ban radar data and the comparison to K distribution based method illustrate the effectiveness of the proposed method.

The detection of a small target in sea clutter is a compelling issue, as the conventional statistical models cannot well describe the sea clutter on a larger timescale, and the methods based on statistics usually require the stationary sea clutter. It has been proven that sea clutter is nonlinear, nonstationary or cyclostationary and chaotic. The new method of MSDLE proposed in the paper can effectively and efficiently detect the small target covered by sea clutter, which can be also introduced and applied to military, aerospace and maritime fields.

This research examines the effect of people interaction on value creation of tourist experiences by reconstructing a scale of value perception. It gathers a set of on-site survey data collected from tourists visiting Norwegian Arctic destinations that contain 579 useful questionnaires. A 19-item value measurement is first validated by confirmatory factor analyses (CFA) that results in a 13-item, five-factor parsimonious model. The CFA results also suggest a high-order factor solution; it finds two convergent factors explicated by five value domains. The derived high-order factors are labeled as tangible value and intangible value. Further analyses show significant relationships between experience values and people interaction. That is the intangible domain of value could create significant mediating effect on people interaction. Specifically, novelty and social values tend to moderate tourist experience. The conclusion furnishes implications in theory advancement and service innovation along with suggestions for research study.

Investors who can transfer their savings to investments in a well-regulated market benefit not only themselves but also economic development. Hence, it is crucial for fund owners to evaluate their stock market investment decisions. The goal of the study is to understand which model determines the asset returns most efficiently. In this regard, the validity of single and multi-index asset pricing models (capital asset pricing model-CAPM and Fama–French models) has been examined in the Turkish Stock Exchange for 2009–2020, with the quantile regression (QR) approach.

On 18 portfolios comprised of quoted stocks in the Istanbul Stock Exchange 100 (ISE-100/BIST-100), we test the CAPM, the Fama and French three factor model (FF3) and the Fama and French five factor model (FF5). Empirical analyses have been carried out via QR approach regressing the portfolios' average weekly excess returns on risk premium/market factor (Rm-Rf), firm size, book value/market value (B/M), profitability and investments factors. QR estimation has been employed since QR is more effective and provides a better definition of the distribution’s tails.

Our empirical findings have revealed that the average excess weekly returns can be explained more strongly via CAPM. Moreover, Fama and French models are expected to give more reliable result with more data, whereas the market premium would give robust results for the Turkish Capital Market.

Individuals investing in financial assets must find the price model that best fits the market. The return can be approximated in the most appropriate manner using the right variables.

The study differs from other research by comparing the asset pricing models via examining the assets' weekly returns with QR in the Istanbul Stock Exchange 100 (ISE-100).

This paper undertakes an extensive and systematic review of the literature on earnings management (EM) over the past three decades (1992–2022). Furthermore, the study identifies emerging research themes and proposes future avenues for further investigation in the realm of EM.

For this study, a comprehensive collection of 2,775 articles on EM published between 1992 and 2022 was extracted from the Scopus database. The author employed various tools, including Microsoft Excel, R studio, Gephi and visualization of similarities viewer, to conduct bibliometric, content, thematic and cluster analyses. Additionally, the study examined the literature across three distinct periods: prior to the enactment of the Sarbanes-Oxley Act (1992–2001), subsequent to the implementation of the Sarbanes-Oxley Act (2002–2012), and after the adoption of International Financial Reporting Standards (2013–2022) to draw more inferences and insights on EM research.

The study identifies three major themes, namely the operationalization of EM constructs, the trade-off between EM tools (accrual EM, real EM and classification shifting) and the role of corporate governance in mitigating EM in emerging markets. Existing literature in these areas presents mixed and inconclusive findings, suggesting the need for further theoretical development. Further, the study findings observe a shift in research focus over time: initially, understanding manipulation techniques, then evaluating regulatory measures, and more recently, investigating the impact of global accounting standards. Several emerging research themes (technology advancements, cross-cultural and cross-national studies, sustainability, behavioral aspects and non-financial indicators of EM) have been identified. This study subsequent analysis reveals an evolving EM landscape, with researchers from disciplines like data science, computer science and engineering applying their analytical expertise to detect EM anomalies. Furthermore, this study offers significant insights into sophisticated EM techniques such as neural networks, machine learning techniques and hidden Markov models, among others, as well as relevant theories including dynamic capabilities theory, learning curve theory, psychological contract theory and normative institutional theory. These techniques and theories demonstrate the need for further advancement in the field of EM. Lastly, the findings shed light on prominent EM journals, authors and countries.

This study conducts quantitative bibliometric and thematic analyses of the existing literature on EM while identifying areas that require further development to advance EM research.

The purpose of this paper is to review the insights provided by behavioral finance studies conducted in the last decade (2006-2015) examining behavioral variables in financial decision making.

The literature review assesses 623 qualitative and quantitative studies published in various international refereed journals and identifies possible scope of future work.

The paper identifies stock market anomalies which contradict rational agents of modern portfolio theory at an aggregate level and behavioral mediators, influencing the financial decision making at an investor level. The paper also attempts to classify different dimensions of risk as professed by the investor.

The authors synthesize the contribution made by behavioral finance studies in extending the knowledge of financial market and investor behavior.