文档介绍：“Babeş-Bolyai” University
Faculty of Mathematics puter Science
Department puter Science
LÓRÁNT BÓDIS
FINANCIAL TIME SERIES FORECASTING USING
ARTIFICIAL WORKS
Master Thesis
Supervisor:
Prof. Dr. Dan Dumitrescu
2004
Abstract
Financial and capital markets (especially stock markets) are considered high return
investment fields, which in the same time are dominated by uncertainty and volatility. Stock
market prediction tries to reduce this uncertainty and consequently the risk. As stock markets
are influenced by many economical, political and even psychological factors, it is very
difficult to forecast the movement of future values. Since classical statistical methods
(primarily technical and fundamental analysis) are unable to deal with the non-linearity in the
dataset, thus it became necessary the utilization of more advanced forecasting procedures.
Financial prediction is a research active area and works have been proposed as one
of the most promising methods for such predictions.
Artificial works (ANNs) mimics, simulates the learning capability of the
human brain. NNs are able to find accurate solutions in plex, noisy environment or
even to deal efficiently with partial information. In the last decade the ANNs have been
widely used for predicting financial markets, because they are capable to detect and
reproduce linear and nonlinear relationships among a set of variables. Furthermore they have
a potential of learning the underlying mechanics of stock markets, . to capture plex
dynamics and non-linearity of the stock market time series.
In this paper, study we will get acquainted with some financial time series analysis
concepts and theories linked to stock markets, as well as with the works based
systems and hybrid techniques that were used to solve several forecasting problems
concerning the capital, financial and stock markets. Putting the fo