درخت حوزه‌های تخصصی

Being aware of the waiting time for selling residential units is one of the important issues in the housing sector for the majority of people, especially investors. There are several factors affecting the waiting time for selling residential units. Determining the influential factors on the time period of selling real estates can lead to an informed decision making by real estate consultants, sellers as well as those seeking to buy real estates. Using a real estate database in Iran, the present paper proposes a two-module procedure. The first module deals with implementation of association rule mining. Using the well-known association rule mining techniques namely FP-Growth, several association rules have been extracted which indicate the effective factors on the waiting time for selling residential units. Generated association rules have been evaluated based on metrics such as support, confidence and lift and finally the best rules are selected. The main objective of the second module is to develop a fuzzy inference system which can determine the factors influencing the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments. In conclusion section, a number of suggestions for future studies are presented. For example, machine learning algorithms such as neural networks, decision trees, etc. can also be used to predict the duration of residential units’ sale. The main objective of the second module is to develop a fuzzy inference system which can learn about the factors that influence the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it takes to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments.

Technical analysis is constituted as an approach in the market analysis which is based on the study of pricing behavior and shares size in the past and price determination and its procedure in the future. Algorithmic transactions are growing rapidly in order to automate business strategies, given the arrival of computer-based technologies and the rapid processing of bulky information. Trading systems combine input information and ultimately identify the time of purchase and sale by forming one signal. In this paper, the training system is a kind of fuzzy inference system that combines fuzzified RSI and SO signals from technical analysis. The system’s trade rules database (selling, buying, and holding) would be calculated based on an optimization process using PSO. This optimization process should be repeated at certain intervals to keep the system up to date. This process is called the corrective property of systems. The findings on the overall index in the period 2001/3/21-2019/3/20 indicate that the system having optimized training on training data has an average daily return of /0027, risk-taking of /0065 and the daily sharp ratio of /42. Concerning the index of return and sharp ratio, the findings reveal that the system outperforms the signals and the market performance.