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This study aims to use gray models to predict abnormal stock returns.

Data are collected from listed companies in the Tehran Stock Exchange during 2005-2015. The analyses portray three models, namely, the gray model, the nonlinear gray Bernoulli model and the Nash nonlinear gray Bernoulli model.

Results show that the Nash nonlinear gray Bernoulli model can predict abnormal stock returns that are defined by conditions other than gray models which predict increases, and then after checking regression models, the Bernoulli regression model is defined, which gives higher accuracy and fewer errors than the other two models.

The stock market is one of the most important markets, which is influenced by several factors. Thus, accurate and reliable techniques are necessary to help investors and consumers find detailed and exact ways to predict the stock market.

The purpose of this paper is to test whether financial analysts’ rationality in making stocks’ earnings forecasts is homogenous or not across different information regimes in stocks’ past returns.

By treating stocks’ past returns as the information variable in this study, the authors employ a threshold regression model to capture and test threshold effects of stocks’ past returns on financial analysts’ rationality in making earnings forecasts in different information regimes.

The results show that three significant structural breaks and four respective information regimes are identified in stocks’ past returns in the threshold regression model. Across the four different information regimes, financial analysts react to stocks’ past returns quite differently when making one-quarter ahead earnings forecasts. Furthermore, the authors find that financial analysts are only rational in a certain information regime of stocks’ past returns depending on a certain return-window such as one-quarter, two-quarter or four-quarter time period.

This study is different from those in the existing literature by arguing that there could exist heterogeneity in financial analysts’ rationality in making earnings forecasts when using stocks’ past returns information. The finding that financial analysts react to stocks’ past returns differently in the different information regimes of past returns adds value to the research on financial analysts’ rationality.

This study aims to explore which of four chosen factors (i.e. privacy concerns, FoMO, self-disclosure and time cost) induce a feeling of strain among Facebook users in terms of social media fatigue (SMF), and if this occurs, whether it further influences such outcomes as discontinuance of usage (DoU) and interaction engagement decrement (IED).

Through an online structured questionnaire, empirical data were gathered to verify the research model, based on the stressor-strain-outcome (SSO) framework. The SEM technique was employed for assessing the hypothesized relationships.

The findings show that privacy concerns and time cost are strong antecedents of SMF and contribute significantly to its occurrence; while FoMO and self-disclosure do not exhibit any significant influence. Moreover, SMF positively and significantly affects DoU and IED.

This study enhances the existing body of knowledge on SMF and it can help: (1) individuals to be aware of risks and adjust their activities in balance with their well-being, and (2) social media (SM) managers to develop unique strategies to address the specific needs of SM users.

This research contributes to the limited literature on SMF by (1) introducing the concept of IED – as a consequence of SMF, and (2) creating measurement scales for IED.

Over the past two decades, the topics of Environmental, Social and Corporate Governance (ESG) and Corporate Social Responsibility (CSR) have attracted an increasing amount of interest, reflecting a growing sensitivity of investors and corporations towards environmental, social and governance issues.

This survey offers an overview of the academic literature on ESG/CSR through the lens of investors, institutions and firms. We first discuss the definitions of ESG and CSR and their relationship to each other.

We next describe how ESG is measured and note problems with the measurement of and quality of ESG data and discrepancies between different measures of ESG. We then turn our attention to investors, examining what types of investors invest in ESG and the role of institutional investors in ESG. From the firm's perspective, we discuss why firms themselves conduct ESG. We also summarize the literature on the impact of ESG on firms: how ESG affects firms' financing, disclosure and reporting activities and firm performance. Finally, we describe other consequences of the focus of ESG and CSR on firms and investors.

This survey offers an overview of the academic literature on ESG/CSR through the lens of investors, institutions and firms.

This paper presents the particle swarm optimization (PSO) algorithm in conjuction with the fuzzy logic method in order to achieve an optimized tuning of a proportional integral derivative controller (PID) in the DTC control loops of dual star induction motor (DSIM). The fuzzy controller is insensitive to parametric variations, however, with the PSO-based optimization approach we obtain a judicious choice of the gains to make the system more robust. According to Matlab simulation, the results demonstrate that the hybrid DTC of DSIM improves the speed loop response, ensures the system stability, reduces the steady state error and enhances the rising time. Moreover, with this controller, the disturbances do not affect the motor performances.

The purpose of this paper is to generalize the traditional risk evaluation methods and to specify a multi-level risk evaluation framework, in order to prepare customized risk evaluation and to enable effectively integrating the elements of risk evaluation.

A real case study of an electric motor manufacturing company is presented to illustrate the advantages of this new framework compared to the traditional and fuzzy failure mode and effect analysis (FMEA) approaches.

The essence of the proposed total risk evaluation framework (TREF) is its flexible approach that enables the effective integration of firms’ individual requirements by developing tailor-made organizational risk evaluation.

Increasing product/service complexity has led to increasingly complex yet unique organizational operations; as a result, their risk evaluation is a very challenging task. Distinct structures, characteristics and processes within and between organizations require a flexible yet robust approach of evaluating risks efficiently. Most recent risk evaluation approaches are considered to be inadequate due to the lack of flexibility and an inappropriate structure for addressing the unique organizational demands and contextual factors. To address this challenge effectively, taking a crucial step toward customization of risk evaluation.