Scaling analysis of price by multi-scale Shannon entropy

Osoolian, Mohammad; Fadaeinejad, Mohammad Esmaeeil; Bagheri, Mobina; Ardalankia, Jamshid

doi:10.1142/s0129183123500389

Int. J. Mod. Phys. C

2022

DOI: 10.1142/s0129183123500389

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Scaling analysis of price by multi-scale Shannon entropy

Mohammad Osoolian

Mohammad Esmaeeil Fadaeinejad

Mobina Bagheri

et al.

Abstract: Multi-scale behaviors emerge in financial markets as complex systems. In this study, we intended to employ multi-scale Shannon entropy to trace the information transition of these phenomena, at different levels of Tehran stock market index (TEDPIX). The obtained results show that, in various magnitude scales and time scales, entropy Granger-causes TEDPIX index in terms of linear and nonlinear aspects. The results revealed that Granger causalities exist between entropy and TEDPIX. The causalities were linear in… Show more

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Cited by 2 publications

References 46 publications

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Adaptive Line Enhancer for Passive Sonars Based on Frequency-Domain Sparsity, Shannon Entropy Criterion and Mixed-Weighted Error

Li,

Cheng,

Qiu

2024

Arab J Sci Eng

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Adaptive line enhancer (ALE) is one of the vital signal processing techniques to the detection and recognition of underwater acoustic targets for passive sonars. Conventional ALEs, based on Gaussian noise assumption and least mean square (LMS) algorithm, can achieve good line enhancement property in Gaussian noise background. However, limited by the high steady-state misadjustment of LMS algorithm, the performance of conventional ALEs deteriorates under non-Gaussian noise background and degrades severely in processing signals with comparably lower signal-to-noise ratio (SNR). Therefore, it’s of great necessity to improve the line enhancement performances of ALE techniques to meet the demands of engineering application in passive sonars. In order to optimize the robustness and adaptability of conventional ALEs in dealing with underwater acoustic signals with much lower-SNR and in non-Gaussian noise background, a modified ALE algorithm called frequency-domain ALE based on l1-norm, Shannon entropy criterion and mixed-weighted norm (l1-SE-MWE-FALE) is proposed in this paper. The proposed l1-SE-MWE-FALE algorithm is based on the integration of frequency-domain sparsity, Shannon entropy (SE) criterion along with mixed-weighted error of LMS and least absolute deviation (LAD) to improve the ALE performance in situations above. The simulation results demonstrate that, when the input SNR is as low as – 25 dB, the local SNR (LSNR) gain for line spectrums by l1-SE-MWE-FALE is 9.8 dB, 3.7 dB and 2.3 dB higher than conventional ALE, l1-norm-based frequency-domain ALE (l1-FALE) and l1 norm-Shannon entropy criterion-based frequency-domain ALE (l1-SE-FALE), respectively. Meanwhile, the simulation results also indicate that the parameters of the proposed method can be chosen loosely and hence are insensitive to the choice of their values. Furthermore, the processing results of two different kinds of real ship-radiated noise signals recorded by passive sonars also imply the advantages of the proposed method over the other three ALEs both qualitatively and quantitatively in the respect of line spectrum LSNR gain and parameter insensitivity. The simulation and experiment results both validate the performance insensitivity to parameter adjustment and hence exhibit a good perspective of applications for passive sonars.

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Adaptive Line Enhancer for Passive Sonars Based on Frequency-Domain Sparsity, Shannon Entropy Criterion and Mixed-Weighted Error

Li,

Cheng,

Qiu

2024

Arab J Sci Eng

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An uncertainty measure based on Pearson correlation as well as a multiscale generalized Shannon-based entropy with financial market applications

Koushki

Osoolian

Sharif

2022

International Journal of Nonlinear Sciences and Numerical Simulation

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In this research, we intended to employ the Pearson correlation and a multiscale generalized Shannon-based entropy to trace the transition and type of inherent mutual information as well as correlation structures simultaneously. An optimal value for scale is found to prevent over smoothing, which leads to the removal of useful information. The lowest Singular Value Decomposition Multiscale Generalized Cumulative Residual Entropy (SVDMWGCRE), or SVD Entropy (SVDE), is obtained for periodic–chaotic series, generated by logistic map; hence, the different dynamic, correlation structures, and intrinsic mutual information have been characterized correctly. It is found out that the mutual information between emerging markets entails higher sensitivity, and moreover emerging markets have demonstrated the highest uncertainty among investigated markets. Additionally, the fractional order has synergistic effects on the enhancement of sensitivity with the multiscale feature. According to the logistic map and financial time series results, it can be inferred that the logistic map can be utilized as a financial time series. Further investigations can be performed in other fields through this financial simulation. The temporal evolutions of financial markets are also investigated. Although the results demonstrated higher noisy information for emerging markets, it was illustrated that emerging markets are getting more efficient over time. Additionally, the temporal investigations have demonstrated long-term lag and synchronous phases between developed and emerging markets. We also focused on the COVID-19 pandemic and compared the reactions of developing and emerging markets. It is ascertained that emerging markets have demonstrated higher uncertainty and overreaction to this pandemic.

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Scaling analysis of price by multi-scale Shannon entropy

Cited by 2 publications

References 46 publications

Adaptive Line Enhancer for Passive Sonars Based on Frequency-Domain Sparsity, Shannon Entropy Criterion and Mixed-Weighted Error

Adaptive Line Enhancer for Passive Sonars Based on Frequency-Domain Sparsity, Shannon Entropy Criterion and Mixed-Weighted Error

An uncertainty measure based on Pearson correlation as well as a multiscale generalized Shannon-based entropy with financial market applications

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