Studying the effect of perceptual errors on the decisions made by the investors by effectiveness of information in Tehran Stock Exchange Company

There are many latent factors that are effective on the decisions made by the investors. The factors that the investors are not aware of their effectiveness and make investment decisions. The main purpose of the present research is to study the perceptual factors affecting on the decision making process of the investors and the effect of information on these factors. For this aim, 385 investors of Tehran Stock Exchange Company were selected as a sample through random sampling method and the required data were gathered via the questionnaire. The accuracy of the hypothesizes was tested via a structural equation model. The results obtained from the present study show that the decision making process of the investors is affected by the representative error, overconfidence error and mood state error by 19%. Moreover, overconfidence error is affected by the degree of information by 95% and by the anonymity of the information by 10%. The mood state error is effective on information processing time by 13% and the information processing time is effective on decision making process by 24%.Keywords: Behavioral Finance, Perceptual Error, Decision making, Investmen

Prediction of Load in Reverse Extrusion Process of Hollow Parts using Modern Artificial Intelligence Approaches

Extrusion is one of the important processes to manufacture and produce military and industrial components. Designing its tools is usually associated with trial and error and needs great expertise and adequate experience. Reverse extrusion process is known as one of the common processes for production of hollow parts with closed ends. The significant load required in formation of a workpiece is one of the existing constraints for the reverse extrusion process. This issue becomes rather difficult especially for the parts having thin walls since its analysis using finite element softwares is exposed to some limitations. In this regard, application of artificial intelligence for prediction of load in the reverse extrusion process will not only save time and money, but also improve quality features of the product. Based on the existing data and methods suggested for variations of punching force through the reverse extrusion process, the system is trained and then performance of the system is evaluated using the test data in this paper. Efficiency of the proposed method is also assessed via comparison with the results of others.DOI:http://dx.doi.org/10.11591/ijece.v4i3.535