Detecting chaos and predicting in Dow Jones Index

Alves, P.R.L.; Duarte, L.G.S.; Mota, L.A.C.P. da

doi:10.1016/j.chaos.2018.03.034

Cited by 15 publications

(11 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Some studies have investigated the nonlinear properties of financial markets [ 30 , 31 , 32 ]. Hsieh [ 30 ] discussed some of the methodological issues in detecting chaotic and nonlinear behavior.…”

Section: Descriptive Statistics and Nonlinear Behavior Analysismentioning

confidence: 99%

The Stock Market Model with Delayed Information Impact from a Socioeconomic View

Wang

Shi

Shang

et al. 2021

Entropy

View full text Add to dashboard Cite

Finding the critical factor and possible “Newton’s laws” in financial markets has been an important issue. However, with the development of information and communication technologies, financial models are becoming more realistic but complex, contradicting the objective law “Greatest truths are the simplest.” Therefore, this paper presents an evolutionary model independent of micro features and attempts to discover the most critical factor. In the model, information is the only critical factor, and stock price is the emergence of collective behavior. The statistical properties of the model are significantly similar to the real market. It also explains the correlations of stocks within an industry, which provides a new idea for studying critical factors and core structures in the financial markets.

show abstract

Section: Descriptive Statistics and Nonlinear Behavior Analysismentioning

confidence: 99%

The Stock Market Model with Delayed Information Impact from a Socioeconomic View

Wang

Shi

Shang

et al. 2021

Entropy

View full text Add to dashboard Cite

show abstract

“…Franch (2018) proved the chaotic behavior for German Mark, Japanese Yen, Swiss Franc and Pound. Alves et al (2018) focused on Dow Jones Index to determine the chaotic dynamics. It was determined that the chaotic dynamics are exist, but prediction performance deteriorates at sharp movements, Tiwari and Gupta (2018) focused, in G7 stock markets, to search the presence of chaos with using Lyapunov exponent.…”

Section: Literature Reviewmentioning

confidence: 99%

Chaotic Behavior in Exchange Rate

Bildirici¹,

Sonüstün²

2018

IJFR

View full text Add to dashboard Cite

In this paper, we aim to analyze the chaotic structure of the daily Euro/USD parity. The examined data covers the period of 01/01/2004 and 03/04/2018. In this context firstly, to determine chaotic behavior of daily Euro/USD rates, the BDS test was used. And following, the chaotic behavior was examined by Largest Lyapunov Exponents (LLE) and Henon Map methods. The results proved the existence of chaotic structure in the data.

show abstract

“…A teoria do caos descreve uma variedade de sistemas não-lineares e por isso já foi abordada nas mais diversasáreas como a meteorologia ( [Lorenz 1995]), a química e filosofia ( [Prigogine and Stengers 1984]), o jornalismo ( [Gleick 1987]), física ( [Strogatz 2012]), a matemática ( [Mandelbrot and Hudson 2004]), a construção de reservatórios ( [Hurst 1956]), a previsão do tempo ( [Lorenz 1963]), os ciclos pluviais e a bolsa de valores ( [Mandelbrot 1968], [Mandelbrot 1997], [Alves et al 2018], [Alves 2019]), a topologia ( [Tempelman and Khasawneh 2020]), e a coleta e processamento de dados ( [Trinh and Miki 1999], [Feng et al 2009], [Rocha and Teles 2013], [Liu and Zhang 2016], [Alves et al 2017], [Tian 2020]).…”

Section: Introductionunclassified

Alocação de Taxa de Transmissão Utilizando Predição do Tráfego de Rede Baseada no Expoente de Lyapunov

Rosa¹,

Rocha²

2020

Anais Do Workshop Em Desempenho De Sistemas Computacionais E De Comunicação (WPerformance 2020)

View full text Add to dashboard Cite

Esse artigo avalia o desempenho de algoritmos selecionados, desenvolvidos com o propósito de quantificar parâmetros referentes à teoria do caos, primeiramente descrevendo um procedimento padrão de análise e medida de dados experimentais que apresentam características não-lineares, dentro do espectro da Teoria do Caos e seus procedimentos mais notáveis, para introduzir um método de predição de tráfego e propor uma alocação dinâmica de taxa de transmissão para servidores de rede baseada no expoente de Lyapunov e no parâmetro de Hurst. Reconstruir o atrator, determinar dimensão de incorporação e dimensão de correlação, estimar o expoente de Lyapunov e o parâmetro de Hurst, prever o tráfego, e alocar a taxa de transmissão correspondente para os servidores de rede são os passos seguidos nessa análise.

show abstract

Detecting chaos and predicting in Dow Jones Index

Cited by 15 publications

References 19 publications

The Stock Market Model with Delayed Information Impact from a Socioeconomic View

The Stock Market Model with Delayed Information Impact from a Socioeconomic View

Chaotic Behavior in Exchange Rate

Alocação de Taxa de Transmissão Utilizando Predição do Tráfego de Rede Baseada no Expoente de Lyapunov

Contact Info

Product

Resources

About