Application of Chaos Theory in the Business World - Sadat Madarshahi, Saeid;Kumar, Uma
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Accuracy and efficiency of economical furcating models are strategic and crucial of business world. Many economists believe that linear models are not efficient enough. So many researches focus on understanding different economical time series structure and dynamical models that can fit them. I try to define the chaos theory, short review on business data investigation and finally I will investigate the Tehran stock exchange Index for chaotic behavior. I will use Correlation Dimension, Hurst, Largest Lyapunov Exponent and BDS tests for my investigation. If the hypotheses do not reject it, it…mehr

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Produktbeschreibung
Accuracy and efficiency of economical furcating models are strategic and crucial of business world. Many economists believe that linear models are not efficient enough. So many researches focus on understanding different economical time series structure and dynamical models that can fit them. I try to define the chaos theory, short review on business data investigation and finally I will investigate the Tehran stock exchange Index for chaotic behavior. I will use Correlation Dimension, Hurst, Largest Lyapunov Exponent and BDS tests for my investigation. If the hypotheses do not reject it, it shows that it is possible to develop a dynamical model for short term forecasting of the market. Even information memory calculated by Hurst and LLE tests can clear the forecasting limitation. Tests results show enough evidence to accept the Hypotheses of chaotic behavior but due to weakness of chaotic tests for economical data, we still need to wait for new tests for a greater confidence.
Autorenporträt
Saied finished his civil engineering bachelor in Iran and studied MBA in Carleton University, Ottawa. The current book is his thesis in 2006 and he tried to implement the chaotic models to the Stock index data stream. This book is appropriate for anyone who seeks an introduction to Chaos Theory, Lyapunov Exponent, CD, Economical time series.