Market efficiency in the art markets using a combination of long memory, fractal dimension, and approximate entropy measures

To give a more definite criterion using electroencephalograph (EEG) approach on brain death determination is vital for both reducing the risks and preventing medical misdiagnosis. This paper presents several novel adaptive computable entropy methods based on approximate entropy (ApEn) and sample entropy (SampEn) to monitor the varying symptoms of patients and to determine the brain death. The proposed method is a dynamic extension of the standard ApEn and SampEn by introducing a shifted time window. The main advantages of the developed dynamic approximate entropy (DApEn) and dynamic sample entropy (DSampEn) are for real-time computation and practical use. Results from the analysis of 35 patients (63 recordings) show that the proposed methods can illustrate effectiveness and well performance in evaluating the brain consciousness states.

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A Random walk down the Dhaka Stock Exchange lane

10.54728/2112091639050575 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Md. Kamrul Bari ◽

Dr. Melita Mehjabeen ◽

Dr. A. K. Enamul Haque

Keyword(s):

Random Walk ◽

Stock Market ◽

Market Efficiency ◽

Long Memory ◽

Stock Exchange ◽

Reliable Estimate ◽

Memory Process ◽

Long Memory Process ◽

Study Results ◽

Adf Test

Market efficiency has always been a matter of keen interest to the researchers of finance. Since the advancement of this concept, researchers are consistently investigating the market efficiency of different financial markets. Bangladesh, being one of the emerging economies, has also attracted the attention of many researchers. The researchers have investigated the realities regarding the market efficiency of both the stock exchanges of the country. Most of their investigations reveal that the Dhaka Stock Exchange (DSE) and the Chittagong Stock Exchange (CSE) are inefficient. This research, however, did not stop at revisiting market efficiency alone. Whether the return series follows a long-memory process, has also been tested. Besides, non-parametric tests have also been conducted to confirm the results of the parametric tests and vice versa. It generated a more reliable estimate of market efficiency for the period under study. Results of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model confirm that the return series does not follow a long memory process, and any shock in the system will eventually vanish. The findings of other tests (the run test, the Augmented Dickey-Fuller (ADF) test, the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, and the Kolmogorov-Smirnov (K-S) test) suggest that the return series of the DSE are time-series stationary, non-normal, and do not follow a random walk. Given these results, we must echo the prior researchers to conclude that the stock market of Bangladesh is not efficient for the period of 2015 to 2020. These findings add new knowledge to the existing knowledge pool about market efficiency and long memory of the stock market of Bangladesh.

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Entropy and Complexity Analyses in Alzheimer’s Disease: An MEG Study

The Open Biomedical Engineering Journal ◽

10.2174/1874120701004010223 ◽

2010 ◽

Vol 4 (1) ◽

pp. 223-235 ◽

Cited By ~ 39

Author(s):

Carlos Gómez ◽

Roberto Hornero

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Fractal Dimension ◽

Background Activity ◽

Senile Plaques ◽

Approximate Entropy ◽

Brain Regions ◽

Brain Disorder ◽

Complexity Measures ◽

Control Subjects

Alzheimer’s disease (AD) is one of the most frequent disorders among elderly population and it is considered the main cause of dementia in western countries. This irreversible brain disorder is characterized by neural loss and the appearance of neurofibrillary tangles and senile plaques. The aim of the present study was the analysis of the magnetoencephalogram (MEG) background activity from AD patients and elderly control subjects. MEG recordings from 36 AD patients and 26 controls were analyzed by means of six entropy and complexity measures: Shannon spectral entropy (SSE), approximate entropy (ApEn), sample entropy (SampEn), Higuchi’s fractal dimension (HFD), Maragos and Sun’s fractal dimension (MSFD), and Lempel-Ziv complexity (LZC). SSE is an irregularity estimator in terms of the flatness of the spectrum, whereas ApEn and SampEn are embbeding entropies that quantify the signal regularity. The complexity measures HFD and MSFD were applied to MEG signals to estimate their fractal dimension. Finally, LZC measures the number of different substrings and the rate of their recurrence along the original time series. Our results show that MEG recordings are less complex and more regular in AD patients than in control subjects. Significant differences between both groups were found in several brain regions using all these methods, with the exception of MSFD (p-value < 0.05, Welch’s t-test with Bonferroni’s correction). Using receiver operating characteristic curves with a leave-one-out cross-validation procedure, the highest accuracy was achieved with SSE: 77.42%. We conclude that entropy and complexity analyses from MEG background activity could be useful to help in AD diagnosis.

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A review of entropy measures for uncertainty quantification of stochastic processes

Advances in Mechanical Engineering ◽

10.1177/1687814019857350 ◽

2019 ◽

Vol 11 (6) ◽

pp. 168781401985735 ◽

Cited By ~ 3

Author(s):

Alireza Namdari ◽

Zhaojun (Steven) Li

Keyword(s):

Stochastic Process ◽

Stochastic Processes ◽

Uncertainty Quantification ◽

Lithium Ion ◽

Second Law Of Thermodynamics ◽

Approximate Entropy ◽

Sample Entropy ◽

Permutation Entropy ◽

Battery Capacity ◽

Entropy Measures

Entropy is originally introduced to explain the inclination of intensity of heat, pressure, and density to gradually disappear over time. Based on the concept of entropy, the Second Law of Thermodynamics, which states that the entropy of an isolated system is likely to increase until it attains its equilibrium state, is developed. More recently, the implication of entropy has been extended beyond the field of thermodynamics, and entropy has been applied in many subjects with probabilistic nature. The concept of entropy is applicable and useful in characterizing the behavior of stochastic processes since it represents the uncertainty, ambiguity, and disorder of the processes without being restricted to the forms of the theoretical probability distributions. In order to measure and quantify the entropy, the existing probability of every event in the stochastic process must be determined. Different entropy measures have been studied and presented including Shannon entropy, Renyi entropy, Tsallis entropy, Sample entropy, Permutation entropy, Approximate entropy, and Transfer entropy. This review surveys the general formulations of the uncertainty quantification based on entropy as well as their various applications. The results of the existing studies show that entropy measures are powerful predictors for stochastic processes with uncertainties. In addition, we examine the stochastic process of lithium-ion battery capacity data and attempt to determine the relation between the changes in battery capacity over different cycles and two entropy measures: Sample entropy and Approximate entropy.

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The dose–response effects of terbutaline on the variability, approximate entropy and fractal dimension of heart rate and blood pressure

British Journal of Clinical Pharmacology ◽

10.1046/j.1365-2125.1998.00674.x ◽

1998 ◽

Vol 45 (3) ◽

pp. 277-285 ◽

Cited By ~ 28

Author(s):

Tuomas T. Jartti ◽

Tom A. Kuusela ◽

Timo J. Kaila ◽

Kari U. O. Tahvanainen ◽

Ilkka A. T. Välimäki

Keyword(s):

Blood Pressure ◽

Heart Rate ◽

Fractal Dimension ◽

Dose Response ◽

Approximate Entropy

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Testing Weak-Form Market Efficiency On The TSX

Journal of Applied Business Research (JABR) ◽

10.19030/jabr.v30i3.8550 ◽

2014 ◽

Vol 30 (3) ◽

pp. 647 ◽

Cited By ~ 3

Author(s):

Ilona Shiller ◽

Ishmael Radikoko

Keyword(s):

Market Efficiency ◽

Unit Root ◽

Long Memory ◽

Statistical Tests ◽

Weak Form ◽

Equity Market ◽

Residual Series ◽

The Common ◽

Two Indices ◽

Equity Index

<p>This study tests the validity of the weak-form EMH on the Canadian TSX equity market using seven TSX daily index returns. Quantitatively, a variety of statistical tests is used to test for the randomness of return series. Results of the common statistical (i.e., the autocorrelation, the BG, the runs) tests all suggest that returns are serially correlated, except returns on the TSX 60 capped index. After rejecting the RWM of TSX indices using univariate unit root (i.e., ADF, PP, KPSS), we proceed to test for the possibility of nonlinear dynamic patterns present in return series. BDS results reject an IID underlying residual series after fitting AR(2) to TSX daily index returns, indicating that a deterministic chaotic process describes the data well. This finding of a temporal dependency is supported also by results of the R/S analysis, which indicates that all TSX index returns possess long-memory properties of an anti-persistent trend-reversing behaviour with two indices showing stronger degree of anti-correlation and five indices showing weaker degree of anti-correlation. Overall, results uniformly reject the RWM governing TSX equity index returns, implying that the Canadian equity market is weak-form inefficient.</p>

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LONG MEMORY AND STOCK MARKET EFFICIENCY: CASE OF SAUDI ARABIA

International Journal of Economics and Financial Issues ◽

10.32479/ijefi.9568 ◽

2020 ◽

Vol 10 (3) ◽

pp. 29-34

Author(s):

Rim Ammar Lamouchi

Keyword(s):

Saudi Arabia ◽

Stock Market ◽

Market Efficiency ◽

Long Memory ◽

Stock Market Efficiency

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An Evaluation of Entropy Measures for Microphone Identification

Entropy ◽

10.3390/e22111235 ◽

2020 ◽

Vol 22 (11) ◽

pp. 1235

Author(s):

Gianmarco Baldini ◽

Irene Amerini

Keyword(s):

A Priori ◽

Approximate Entropy ◽

Identification Accuracy ◽

Permutation Entropy ◽

Features Selection ◽

Statistical Features ◽

Audio Signals ◽

Trade Off ◽

Entropy Measures ◽

Physical Features

Research findings have shown that microphones can be uniquely identified by audio recordings since physical features of the microphone components leave repeatable and distinguishable traces on the audio stream. This property can be exploited in security applications to perform the identification of a mobile phone through the built-in microphone. The problem is to determine an accurate but also efficient representation of the physical characteristics, which is not known a priori. Usually there is a trade-off between the identification accuracy and the time requested to perform the classification. Various approaches have been used in literature to deal with it, ranging from the application of handcrafted statistical features to the recent application of deep learning techniques. This paper evaluates the application of different entropy measures (Shannon Entropy, Permutation Entropy, Dispersion Entropy, Approximate Entropy, Sample Entropy, and Fuzzy Entropy) and their suitability for microphone classification. The analysis is validated against an experimental dataset of built-in microphones of 34 mobile phones, stimulated by three different audio signals. The findings show that selected entropy measures can provide a very high identification accuracy in comparison to other statistical features and that they can be robust against the presence of noise. This paper performs an extensive analysis based on filter features selection methods to identify the most discriminating entropy measures and the related hyper-parameters (e.g., embedding dimension). Results on the trade-off between accuracy and classification time are also presented.

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