Wavelet Leader and Multifractal Detrended Fluctuation Analysis of Market Efficiency: Evidence from the WAEMU Market Index

Diallo, Oumou Kalsoum; Mendy, Pierre

doi:10.22440/wjae.5.1.1

Cited by 3 publications

(4 citation statements)

References 54 publications

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“…C1 values above 0.5 indicate positive correlation (signal has memory), values below 0.5 indicate negative correlation, and a value of 0.5 indicates lack of correlation (random signal). Meanwhile, C2, the second log-cumulant, reflects multifractality (and the width of the singularity spectrum, like M ) ( Wendt and Abry, 2007 ; Wendt et al, 2009 ; Zilber, 2014 ; Diallo and Mendy, 2019 ). Given the concavity of the scaling function, C2 is always negative, and when C2 equals 0, it is said to indicate monofractality.…”

Section: Methodsmentioning

confidence: 99%

Altered Brain Criticality in Schizophrenia: New Insights From Magnetoencephalography

Alamian

Lajnef

Pascarella

et al. 2022

Front. Neural Circuits

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Schizophrenia has a complex etiology and symptomatology that is difficult to untangle. After decades of research, important advancements toward a central biomarker are still lacking. One of the missing pieces is a better understanding of how non-linear neural dynamics are altered in this patient population. In this study, the resting-state neuromagnetic signals of schizophrenia patients and healthy controls were analyzed in the framework of criticality. When biological systems like the brain are in a state of criticality, they are thought to be functioning at maximum efficiency (e.g., optimal communication and storage of information) and with maximum adaptability to incoming information. Here, we assessed the self-similarity and multifractality of resting-state brain signals recorded with magnetoencephalography in patients with schizophrenia patients and in matched controls. Schizophrenia patients had similar, although attenuated, patterns of self-similarity and multifractality values. Statistical tests showed that patients had higher values of self-similarity than controls in fronto-temporal regions, indicative of more regularity and memory in the signal. In contrast, patients had less multifractality than controls in the parietal and occipital regions, indicative of less diverse singularities and reduced variability in the signal. In addition, supervised machine-learning, based on logistic regression, successfully discriminated the two groups using measures of self-similarity and multifractality as features. Our results provide new insights into the baseline cognitive functioning of schizophrenia patients by identifying key alterations of criticality properties in their resting-state brain data.

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Section: Methodsmentioning

confidence: 99%

Altered Brain Criticality in Schizophrenia: New Insights From Magnetoencephalography

Alamian

Lajnef

Pascarella

et al. 2022

Front. Neural Circuits

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show abstract

“…(Knight et al, 2017) propose a new method for estimating the Hurst exponent that naturally accounts for the irregularity of the data sampling. (Khamis et al, 2018), (Jiang et al, 2019) and (Diallo and Mendy, 2019) use multivariate analysis to estimate the generalized Hurst coefficient ( ) (Ciuciu et al, 2008) and concluded that the approach based on wavelet leader (WL) coefficients gives a better estimate of ( ).…”

Section: Methodsmentioning

confidence: 99%

“…This paper analyzes the weak-form informational efficiency of the West African Economic and Monetary Union (WAEMU) stock exchange. 1 Following a first paper (Diallo and Mendy, 2019) analyz-ing the Bourse Régionale des Valeurs Mobilières (BRVM) through the BRVM10 index, 2 we were able to demonstrate that the BRVM does not verify the efficient market hypothesis. Thus, this paper conducts a more detailed, sector-by-sector analysis using the seven sectoral indices.…”

Section: Introductionmentioning

confidence: 93%

A method to test weak-form market efficiency from sectoral indices of the WAEMU stock exchange: A wavelet analysis

Diallo

Mendy

Burlea‐Schiopoiu

2021

Heliyon

Self Cite

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This study assesses the efficiency of the West African Economic and Monetary Union (WAEMU) regional stock exchange using daily data on its seven (7) sectoral indices from December 31, 2013, to January 4, 2019. To this end, we analyze the market structure and calculate the generalized Hurst index by using the discrete wavelet transformation (DWT) and wavelet leader transformation (WLT) approaches. Our conclusions can be summarized as follows: first, this study highlights the multifractal nature of the WAEMU stock market. Second, the Hurst generalized index reveals a persistent or nonpersistent process depending on the sector, according to the q chosen or the method used (DWT or WLT). The dynamics of the indices reveal the characteristics of short memory or, in some cases, long memory, and the efficient market hypothesis is rejected.

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“…Its values approximate those of the H; values above 0.5 indicate positive correlation (signal has memory), values below 0.5 indicate negative correlation, and a value of 0.5 indicates lack of correlation (random signal). Meanwhile, C2, the second log-cumulant, reflects multifractality (and the width of the singularity spectrum, like M) (Wendt, 2007;Wendt et al, 2009;Zilber, 2014;Diallo and Mendy, 2019). Given the concavity of the scaling function, C2 is always negative, and when C2 equals 0, it is said to indicate monofractality.…”

Section: Measuring Self-similarity and Multifractalitymentioning

confidence: 99%

Altered brain criticality in Schizophrenia: New insights from MEG

Alamian

Lajnef

Pascarella

et al. 2021

Preprint

View full text Add to dashboard Cite

Schizophrenia has a complex etiology and symptomatology that is difficult to untangle. After decades of research, important advancements towards a central biomarker are still lacking. One of the missing pieces is a better understanding of how non-linear neural dynamics are altered in this patient population. In this study, the resting-state neuromagnetic signals of schizophrenia patients and healthy controls were analyzed in the framework of criticality. When biological systems like the brain are in a state of criticality, they are thought to be functioning at maximum efficiency (e.g., optimal communication and storage of information) and with maximum adaptability to incoming information. Here, we assessed the self-similarity and multifractality of resting-state brain signals recorded with magnetoencephalography in patients with schizophrenia patients and in matched controls. Our analysis showed a clear ascending, rostral to caudal gradient of self-similarity values in healthy controls, and an opposite gradient for multifractality (descending values, rostral to caudal). Schizophrenia patients had similar, although attenuated, gradients of self-similarity and multifractality values. Statistical tests showed that patients had higher values of self-similarity than controls in fronto-temporal regions, indicative of more regularity and memory in the signal. In contrast, patients had less multifractality than controls in the parietal and occipital regions, indicative of less diverse singularities and reduced variability in the signal. In addition, supervised machine-learning, based on logistic regression, successfully discriminated the two groups using measures of self-similarity and multifractality as features. Our results provide new insights into the baseline cognitive functioning of schizophrenia patients by identifying key alterations of criticality properties in their resting-state brain data.

show abstract

Wavelet Leader and Multifractal Detrended Fluctuation Analysis of Market Efficiency: Evidence from the WAEMU Market Index

Cited by 3 publications

References 54 publications

Altered Brain Criticality in Schizophrenia: New Insights From Magnetoencephalography

Altered Brain Criticality in Schizophrenia: New Insights From Magnetoencephalography

A method to test weak-form market efficiency from sectoral indices of the WAEMU stock exchange: A wavelet analysis

Altered brain criticality in Schizophrenia: New insights from MEG

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