Affect-based stock investment decision: The role of affective self-affinity

This paper studies the role of affective self-affinity for a company in the stock investment decision by investigating the factors triggering it. Based on the social identity theory and the affect literature we hypothesize that three types of identifications, namely group related, company-people related and idea/ideal related, trigger affective self-affinity for a company which results in extra affect-based motivation to invest in the company's stock. The two ideas included in the idea/ideal related affective self-affinity are socially responsible investing and nationality related ideas. Based on the survey data of 133 active individual investors, we find that the more the investors perceive the company supports/represents a specific group or idea or employ a specific person, with which the investors identify themselves, the higher is the investors’ affective self-affinity for the company. This results in higher extra affective motivation to invest in the company's stock over and beyond financial indicators. Thus, investors’ identification with groups, people, or ideas such as socially responsible investing and nationality results in higher affect-based investment motivation through affective self-affinity aroused in the investors. Moreover, positive attitude towards the company is another factor that explains the affect-based extra investment motivation. © 2017 Elsevier Inc

Abstract Geographic Information Systems Technologies in Crime Analysis and Crime Mapping

Social and geographic factors, such as location of schools or neighborhoods with different socioeconomic status within an area, can influence the patterns and rate of crime incidents in that area. Crime analysis and crime maps, achieved by GIS, have a major role in reducing crime and improving the effective police activities. Cankaya district includes significant concentrations of evening, late-night leisure and shopping opportunities in Ankara. Using information such as where and when crime occurs, this paper emphasizes what the inputs and outputs of crime analysis could be. In the first stage, the relationship between socioeconomic characteristics and crime rate for two police precincts of Cankaya was investigated. Then, spatial and non-spatial GIS analyses using Spatial Analyst, Buffer Analysis, and Hotspot Analysis were performed. The findings of this paper give the potential incident sites. 1

Two-dimensional hydrodynamic modelling for urban flood risk assessment using unmanned aerial vehicle imagery: A case study of Kirsehir, Turkey

WOS: 000485980500006This study is an assessment of flash flood risk in the downstream part of an intermittent stream which lost its natural bed due to intense human interventions, with the example of Hastane Brook in the province of Kirsehir (Turkey). The effects of flooding events with high, medium, and low probability of occurrence are investigated on a street-by-street basis with a two-dimensional (2D) hydrodynamic model constructed in HEC-RAS 5.0 software. Due to the lack of records of past flood events required for model calibration, it is aimed to utilise high-quality data as much as possible in model development. Therefore, to more accurately simulate the movement of water and thereby to advise adequate measures for reducing the most likely flood effects, the required high-resolution terrain and land use data are produced by processing the aerial images acquired by the unmanned aerial vehicle (UAV) flights over the flood risk zone. The estimation of flood hydrographs is only based on the synthetic unit hydrograph methods due to the absence of representative stream gauging stations inside or near the region. The resultant flood hazard maps are cautionary in terms of demonstrating the effects of possible floods that are unexpected to come from such an intermittent stream basin.Kirsehir Ahi Evran University Scientific Research Projects Coordination UnitAhi Evran University [MMF.A3.17.004]The Kirsehir Ahi Evran University Scientific Research Projects Coordination Unit, Grant/Award Number: MMF.A3.17.00