Forecasting stock market prices
Journal
ICT Innovations 2010, Web Proceedings
Date Issued
2010-09
Author(s)
Janeski, Miroslav
Abstract
In recent years, use of data mining and machine learning techniques
in finance for such tasks as pattern recognition, classification, and time series
forecasting have dramatically increased. However, the large numbers of
parameters that must be selected to develop a good forecasting model have
meant that the design process still involves much trial and error. The objective
of this paper is to select the optimal parameters for designing of a neural
network model for forecasting economic time series data. There is proposed a
neural network based forecasting model for forecasting the stock market price
movement. The system is tested with data from one Macedonian Stock, the
NLB Tutunska Banka stock. The system is shown to achieve an overall
prediction rate of over 60%. A number of difficulties encountered when
modeling such forecasting model are discussed.
in finance for such tasks as pattern recognition, classification, and time series
forecasting have dramatically increased. However, the large numbers of
parameters that must be selected to develop a good forecasting model have
meant that the design process still involves much trial and error. The objective
of this paper is to select the optimal parameters for designing of a neural
network model for forecasting economic time series data. There is proposed a
neural network based forecasting model for forecasting the stock market price
movement. The system is tested with data from one Macedonian Stock, the
NLB Tutunska Banka stock. The system is shown to achieve an overall
prediction rate of over 60%. A number of difficulties encountered when
modeling such forecasting model are discussed.
Subjects
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