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This research paper examines the corporate dividend payout behaviors of non-financial firms from Istanbul Stock Exchange (Borsa Istanbul). Survey method is conducted to investigate managerial views on corporate dividend policy. The study investigates whether the evidence in Turkish stock market on dividend policy is similar to the European and the U.S. firms’ results which are reported earlier by other studies, and moreover in what extent Lintner’s (1956) findings on dividends is supported by today’s listed firms in an emerging market. The financial managers from 38 firms out of 216 non-financial companies responded the survey. The results show that there is a significant positive relationship between cash dividends and earnings. Earnings are viewed as the most important factor in dividend decision like in European and the U.S. firms. Sustainable change in earnings, stability and level of future earnings, and the desire to distribute a proportion of earnings to shareholders are the common determinants of dividend policy. The majority of the respondents reports that they target dividends. Dividend yield is the most common measure for dividend targeting. Share repurchases are not viewed as alternative to dividend payouts unlike the U.S. firms. The study finds supporting evidences for bird-in-the-hand and signaling hypotheses, and Lintner’s model.

In a data set, an outlier refers to a data point that is considerably different from the others. Detecting outliers provides useful application-specific insights and leads to choosing right prediction models. Outlier detection (also known as anomaly detection or novelty detection) has been studied in statistics and machine learning for a long time. It is an essential preprocessing step of data mining process. In this study, outlier detection step in the data mining process is applied for identifying the top 20 outlier firms. Three outlier detection algorithms are utilized using fundamental analysis variables of firms listed in Borsa Istanbul for the 2011-2014 period. The results of each algorithm are presented and compared. Findings show that 15 different firms are identified by three different outlier detection methods. KCHOL and SAHOL have the greatest number of appearances with 12 observations among these firms. By investigating the results, it is concluded that each of three algorithms makes different outlier firm lists due to differences in their approaches for outlier detection.

In modern age, positive psychological capital is as important as financial capital for organizational development and sustainability. Efficacy, hope, optimism, and resilience are the dimensions of positive psychological capital. Emotional labor is defined by Hochschild as control of self emotions at work by workers who have a high degree of contact with coworkers within the organization or with external clients, so as to create an expression, voice or body gesture which is acceptible to the clients. Emotional labor is analysized under three components: surface acting, deep acting and naturally felt emotions. This study is aimed to investigate the impact of positive psychological capital on emotional labor. So a research is conducted with the aim of exploring the relationship between the sub-dimensions of psychological capital and emotional labour.