An approach for measuring corporation financial stability by Econophysics and Bayesian method

Zhong, Guang-Yan; Li, Jiang-Cheng; Mei, Dong-Cheng; Tang, Niansheng

doi:10.1016/j.physa.2019.121197

Cited by 9 publications

(3 citation statements)

References 59 publications

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“…The stability of systems is a frequent concern for complex natural systems [35] and economic and financial systems [13,34,36,37]. From the statistical-physical point of view, mean first passage (escape) time (MFPT) is one of the commonly used methods to measure the stability of systems, such as noiseenhanced stability [4,38], mean escape time of stochastic volatility [11], etc.…”

Section: Methods -mentioning

confidence: 99%

Roles of GARCH and ARCH effects on the stability in stock market crash

Li¹,

Xing²,

Huang³

et al. 2021

EPL

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We theoretically stochastic simulate and empirically analyze the escape process of stock market price nonequilibrium dynamics under the influence of GARCH and ARCH effects, and explore the impact of ARCH and GARCH effects on stock market stability. Based on the nonlinear GARCH model of econophysics, and combined with GARCH and ARCH effects of volatility, we propose a delay stochastic monostable potential model. We use the mean escape time, or mean hitting time, as an indicator for measuring price stability, as first introduced in Ref. [1]. Based on the comparative analysis of actual Chinese A-share data, the theoretical and empirical findings of this paper are as follows} (1) The theoretical simulation results and actual data are consistent. (2) There exist optimal GARCH and ARCH effects maximally enhancing stock market stability.

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Section: Methods -mentioning

confidence: 99%

Roles of GARCH and ARCH effects on the stability in stock market crash

Li¹,

Xing²,

Huang³

et al. 2021

EPL

Self Cite

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“…However, there are successful instances of applying econophysics approach in fields that include business volatility and stock markets, economic value and growth, economic and financial time series, behavioural finance, corporation financial stability, distribution and interactions of economic entities, market structure and financial risks (Chen and Li, 2012;Chakraborti et al, 2011;Huang, 2015;Guedes et al, 2019;Schinckus and Jovanovic, 2013;Zapart, 2015;McCauley, 2004;Meng et al, 2016;Rickles, 2007;Zhong et al, 2019). Mainly the concepts of physics such as Bernoulli's equation, Newton's law of gravitation, Brownian motion, Schrodinger equation, Bose-Einstein distribution, Gaussian function, Fourier transformation, and Heisenberg's uncertainty principle have been adopted to naturalise the econophysics models (Donmez and Sen, 2018;Meng et al, 2016;Zhang and Huang, 2010;Cotfas, 2013;Pedram, 2012;Mantegna, 2016;Kusmartsev, 2011;Agustini et al, 2018;Hsu, 2010;Wang and Pei, 2015).…”

Section: Introductionmentioning

confidence: 99%

An econophysics approach to forecast bulk shipbuilding orderbook: an application of Newton’s law of gravitation

Sakalayen

Duru

Hirata

2020

MABR

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Purpose Bulk shipping mostly facilitates the smooth flow of raw materials around the globe. Regardless, forecasting a bulk shipbuilding orderbook is a seldom researched domain in the academic arena. This study aims to pioneer an econophysics approach coupled with an autoregressive data analysis technique for bulk shipbuilding order forecasting. Design/methodology/approach By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The model has been evaluated through autoregressive integrated moving average analysis, and the outcome indicates a relatively stable good fit. Findings The outcomes of the econophysics model indicate a relatively stable good fit. Although relevant maritime data and its quality need to be improved, the flexibility in refining the predictive variables ensure the robustness of this econophysics-based forecasting model. Originality/value By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The research result helps shipping investors make decision in a capital-intensive and uncertainty-prone environment.

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“…Their findings illustrated the superior performance of MCMC in forecasting seasonal and intermittent demand with overall lower accuracy metrics. Besides the above-mentioned studies, Bayesian MCMC approach has also become more prevalent in dynamic model estimation (Liesenfeld and Richard, 2004;Li et al, 2017;Billio et al, 2018;Brix et al, 2018;Akbar et al, 2019;Kostrzewski and Kostrzewska, 2019;Yang, 2019;Zhong et al, 2019a;Zhong et al, 2019b;Leng and Li, 2020;Maldonado et al, 2020;Kienzle et al, 2021;, wind forecasting (Pang et al, 2001;Erto et al, 2010;Li and Shi, 2010;Karatepe and Corscadden, 2013;Moghaddass and Sheng, 2019;García et al, 2020;Lepore et al, 2020;Kikumoto et al, 2021;Mbuvha et al, 2021), water quality modelling (Samanta et al, 2007;Liu et al, 2008;Cheng et al, 2014;Hantush and Chaudhary, 2014;Lu et al, 2014;Wellen et al, 2014;Camacho et al, 2015;Zheng and Han, 2015;Chaudhary and Hantush, 2017), and uncertainty analysis (Keats et al, 2007;Lehuger et al, 2009;Wang and Chen, 2013;Wang and Jin, 2013;Liang et al, 2016;Berger et al, 2016;Folgoc et al, 20...…”

Section: Review On Bayesian Probabilistic Forecasting Methodsmentioning

confidence: 99%

Assessment of atmospheric pollutant emissions by ships using the Bayesian Markov chain Monte Carlo probabilistic forecasting algorithm

Liu¹

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CHAPTER 2 LITERATURE REVIEW……………………………………………10 2.1 Introduction………………………………………….…….…………………10 2.2 Literature review approach…………………………………………..……….11 2.2.1 Description………………….……………...…………….………………11 2.2.2 Bibliometric statistics………………………………...……….………….12 2.3 Literature review in ship emission accounting……………………….……....14 2.3.1 Top-down ship emission accounting method………………….………….14 2.3.2 Bottom-up ship emission accounting method…………….………………16 2.3.2.1 Temporal and spatial resolution of ship movements………………....18 2.3.2.2 Data sources for ship specification information……….……..…..…..20 2.3.2.3 Approaches to estimate emission factors…………….……………….22 2.3.3 Research topics in bottom-up emission inventories………….………..….24 2.3.3.1 Ship emission inventories with different boundaries…………………24 2.3.3.2 Ship emission inventories with different emission sources……..….…27 2.4 Other data modelling techniques………………………………….…………..29 III

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An approach for measuring corporation financial stability by Econophysics and Bayesian method

Cited by 9 publications

References 59 publications

Roles of GARCH and ARCH effects on the stability in stock market crash

Roles of GARCH and ARCH effects on the stability in stock market crash

An econophysics approach to forecast bulk shipbuilding orderbook: an application of Newton’s law of gravitation

Assessment of atmospheric pollutant emissions by ships using the Bayesian Markov chain Monte Carlo probabilistic forecasting algorithm

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