Cross-correlation and the predictability of financial return series

Duan, Wenqi; Stanley, H. Eugene

doi:10.1016/j.physa.2010.09.013

Cited by 28 publications

(13 citation statements)

References 27 publications

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“…Nos últimos anos, as redes neurais artificiais desenvolveram modelos de pesquisa bemsucedidos para prever, detectar e resumir a estrutura das variáveis de mercados financeiros, sem confiar demais em pressupostos específicos e distribuições de erro (Duan & Stanley, 2011).…”

Section: Introductionunclassified

Nível de Governança Corporativa prediz o desempenho financeiro da empresa? Evidências do mercado brasileiro por meio de redes neurais artificiais

Maestri

Tavares

Penedo

et al. 2019

Rev. Cat. Cien. Cont.

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Este estudo teve como objetivo desenvolver modelos de redes neurais artificiais para prever o desempenho financeiro das empresas pertencentes e não pertencentes aos níveis de governança da B3 (antiga BM&FBovespa). Os modelos foram desenvolvidos utilizando dados das empresas do mercado de ações brasileiro no período de 2005 a 2017. Estipulou-se como hipótese que os modelos que corresponderiam às empresas pertencentes aos segmentos de governança obteriam maior sucesso na previsão do desempenho financeiro das empresas em comparação aos que representariam as que não estavam nos níveis de governança. Os resultados confirmaram a hipótese. Além disso, eles indicaram que o desempenho financeiro das empresas não pertencentes aos níveis de governança corporativa é mais sensível a oscilações ocorridas no ambiente externo, o que dificulta a previsão desse indicador. Este trabalho foi o primeiro no Brasil a construir modelos por meio de técnicas de inteligência artificial – mais especificamente a rede neural backpropagation – para prever o desempenho financeiro da empresa relacionando-o a aspectos da governança corporativa.

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Section: Introductionunclassified

Nível de Governança Corporativa prediz o desempenho financeiro da empresa? Evidências do mercado brasileiro por meio de redes neurais artificiais

Maestri

Tavares

Penedo

et al. 2019

Rev. Cat. Cien. Cont.

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“…Complex network [4][5][6] has been a popular research topic in recent years and has been widely applied in finance. Constructing an economic and financial complex network based on the financial time series (generally stock return series) can systematically and intuitively express mutual dependence among different financial institutions [7][8][9][10]. Unconditional direct relationships between all financial institutions and markets can be disclosed by the complex network, especially the relationships among financial institutions that have not suffered similar losses in the crisis.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Contagion of Systemic Risks on Global Main Equity Markets Based on Granger Causality Networks

Zheng

Song

2018

Discrete Dynamics in Nature and Society

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A total of 156 Granger causal networks of stock markets are constructed by using the Granger causality test and time series sliding window based on stock index data of 34 major stock markets in the world from 2004 to 2017. The topological structures and evolution characteristics of the Granger causal networks are analyzed from the perspective of complex network theory. Empirical results demonstrate that the network topology has a significant difference during the global financial crisis and other periods. The causal relationships among different global stock markets exhibit a jump growth when each major crisis occurs. The contagion path is also short. A causal relationship between any two stock markets can usually be established with one stock market on average, not by using more than five stock markets. For risk contagion, the American stock markets exerted the largest influence in 12 years, followed by the European stock markets. Stock markets with high intermediate contagion ability play an important role in systemic risk contagion. Despite the crucial markets in Europe and America (e.g., USA, Brazil, and Mexico), stock markets with weak network correlation and strong media ability (e.g., the markets of Japan, Korea, Australia, and New Zealand) play a critical role in risk contagion.

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“…[2][3][4]-have attempted to forecast the financial market using support vector machines (SVM) with standard kernels, the area still remains a challenge for practitioners. Therefore, there is a natural interest in applying kernels for financial forecasting by incorporating temporal information between misaligned time series or varying frequencies in the data patterns.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Dynamic Kernels for Financial Time Series Forecasting

Peña

Arratia

Belanche

2016

Artificial Neural Networks and Machine Learning – ICANN 2016

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Abstract. We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data compression process redefines the original financial time series into temporal data blocks, analyzing the temporal information of multiple time intervals. The analysis is done through multivariate dynamic kernels within support vector regression. We also propose two kernels for financial time series that are computationally efficient without a sacrifice on accuracy. The efficacy of the methodology is demonstrated by empirical experiments on forecasting the challenging S&P500 market.

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Cross-correlation and the predictability of financial return series

Cited by 28 publications

References 27 publications

Nível de Governança Corporativa prediz o desempenho financeiro da empresa? Evidências do mercado brasileiro por meio de redes neurais artificiais

Nível de Governança Corporativa prediz o desempenho financeiro da empresa? Evidências do mercado brasileiro por meio de redes neurais artificiais

Dynamic Contagion of Systemic Risks on Global Main Equity Markets Based on Granger Causality Networks

Multivariate Dynamic Kernels for Financial Time Series Forecasting

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