Granger causality detection in high-dimensional systems using feedforward neural networks

Calvo‐Pardo, Hector; Mancini, Tullio; Olmo, José

doi:10.1016/j.ijforecast.2020.10.004

Cited by 9 publications

(9 citation statements)

References 46 publications

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“…We applied two new methodologies: first, as an AI technique, the FFNN, nonlinear Granger causality test recommended by Montalto et al ( 2015 ) and Calvo-Pardo et al ( 2021 ); and second, as another nonlinear 2 approach, nonlinear LPIRFs. To the best of our knowledge, our study is the first in the overconfidence-bias literature to apply the abovementioned AI application and LPIRFs to test for the presence of overconfidence behavior.…”

Section: Data and Empirical Methodologymentioning

confidence: 99%

“… The FFNN has a basic structure and a wide range of applications, and can estimate any continuous and square-integral functions with arbitrary precision (Yang and Li 2017 ). Unknown dependence structures between elements of high-dimensional, multivariate time series with weak and strong persistence can be accommodated using this method (Calvo-Pardo et al 2021 ). …”

Section: Data and Empirical Methodologymentioning

confidence: 99%

“…Unknown dependence structures between elements of high-dimensional, multivariate time series with weak and strong persistence can be accommodated using this method (Calvo-Pardo et al 2021 ).…”

Section: Data and Empirical Methodologymentioning

confidence: 99%

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Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach

2023

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Overconfidence behavior, one form of positive illusion, has drawn considerable attention throughout history because it is viewed as the main reason for many crises. Investors’ overconfidence, which can be observed as overtrading following positive returns, may lead to inefficiencies in stock markets. To the best of our knowledge, this is the first study to examine the presence of investor overconfidence by employing an artificial intelligence technique and a nonlinear approach to impulse responses to analyze the impact of different return regimes on the overconfidence attitude. We examine whether investors in an emerging stock market (Borsa Istanbul) exhibit overconfidence behavior using a feed-forward, neural network, nonlinear Granger causality test and nonlinear impulse-response functions based on local projections. These are the first applications in the relevant literature due to the novelty of these models in forecasting high-dimensional, multivariate time series. The results obtained from distinguishing between the different market regimes to analyze the responses of trading volume to return shocks contradict those in the literature, which is the key contribution of the study. The empirical findings imply that overconfidence behavior exhibits asymmetries in different return regimes and is persistent during the 20-day forecasting horizon. Overconfidence is more persistent in the low- than in the high-return regime. In the negative interest-rate period, a high-return regime induces overconfidence behavior, whereas in the positive interest-rate period, a low-return regime induces overconfidence behavior. Based on the empirical findings, investors should be aware that portfolio gains may result in losses depending on aggressive and excessive trading strategies, particularly in low-return regimes.

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Section: Data and Empirical Methodologymentioning

confidence: 99%

Section: Data and Empirical Methodologymentioning

confidence: 99%

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Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach

2023

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“…8 For the interested reader Algorithm 1 in Calvo-Pardo et al (2021) proposes a procedure for excluding nodes within a neural network which can increase the predictive accuracy within a high dimensional setting.…”

Section: Optimal Number Of Stocks Based On Eigenvector Centralitymentioning

confidence: 99%

“…According to Mitchener and Richardson (2019), the investigation of the causality of shock propagation, amplification of systemic risk and potentially the magnitude of economic downturns within a network remains an open economic question. Calvo-Pardo et al (2021) propose a novel framework to identify Granger causality using neural network models which can be utilized for explaining the propagation of systemic risk in high-dimensional settings.…”

Section: Introductionmentioning

confidence: 99%

Optimal Portfolio Choice and Stock Centrality for Tail Risk Events

Katsouris¹

2021

Preprint

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We propose a novel risk matrix to characterize the optimal portfolio choice of an investor with tail concerns. The diagonal of the matrix contains the Value-at-Risk of each asset in the portfolio and the off-diagonal the pairwise ∆CoVaR measures reflecting tail connections between assets. First, we derive the conditions under which the associated quadratic risk function has a closed-form solution. Second, we examine the relationship between portfolio risk and eigenvector centrality. Third, we show that portfolio risk is not necessarily increasing with respect to stock centrality. Forth, we demonstrate under certain conditions that asset centrality increases the optimal weight allocation of the asset to the portfolio. Overall, our empirical study indicates that a network topology which exhibits low connectivity is outperformed by high connectivity based on a Sharpe ratio test.

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Covid-19 Detection by Wavelet Entropy and Jaya

Wang

2021

Intelligent Computing Theories and Application

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Granger causality detection in high-dimensional systems using feedforward neural networks

Cited by 9 publications

References 46 publications

Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach

Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach

Optimal Portfolio Choice and Stock Centrality for Tail Risk Events

Covid-19 Detection by Wavelet Entropy and Jaya

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