Applying Approximate Entropy (ApEn) to Speculative Bubble in the Stock Market

Bhaduri, Saumitra N.

doi:10.1177/0972652714534023

Cited by 10 publications

(7 citation statements)

References 18 publications

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“…However, we do not observe that the regularity techniques studied in this paper can serve to predict an imminent “crash”. Thus, although previous research suggests that these algorithms can serve as leading indicators of the existence of crises or bubbles 4,8,10 , the results of this work cannot validate those conclusions. A situation such as the bubble of the dot com is not reflected in the NASDAQ before the index begins to fall, nor the falls in the Hang Seng in 1997–1998 can be predicted using ApEn or the Pincus Index.…”

Section: Discussioncontrasting

confidence: 88%

Quantifying the randomness of the stock markets

Delgado-Bonal

2019

Sci Rep

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Randomness has been mathematically defined and quantified in time series using algorithms such as Approximate Entropy (ApEn). Even though ApEn is independent of any model and can be used with any time series, as the markets have different statistical values, it cannot be applied directly to make comparisons between series of financial data. In this paper, we develop further the use of Approximate Entropy to quantify the existence of patterns in evolving data series, defining a measure to allow comparisons between time series and epochs using a maximum entropy approach. We apply the methodology to the stock markets as an example of its application, showing that the number of patterns changed for the six analyzed markets depending on the economic situation, in agreement with the Adaptive Markets Hypothesis.

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Section: Discussioncontrasting

confidence: 88%

Quantifying the randomness of the stock markets

Delgado-Bonal

2019

Sci Rep

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“…LPPLS differs from other methods (Phillips 2015 ) because its methodological approach does not require econometric modelling and therefore can be considered in all respects a “multidisciplinary” tool (Benth et al 2013 ; Fantazzini 2019 ). On the same class of methods, but based instead on the entropy concept, are those introduced by Stosic et al ( 2016 ) and Bhaduri ( 2014 ). Empirical results suggest that financial crises are associated with significant increase of exchange rate entropy (Stosic et al 2016 ), reflecting instability in FX market dynamics, and that there are strong “tell-tale” signs characterized by low Approximate Entropy (ApEn) level during many of crash events (Bhaduri 2014 ).…”

Section: The Need Of Multidisciplinary Methodsmentioning

confidence: 99%

“…On the same class of methods, but based instead on the entropy concept, are those introduced by Stosic et al ( 2016 ) and Bhaduri ( 2014 ). Empirical results suggest that financial crises are associated with significant increase of exchange rate entropy (Stosic et al 2016 ), reflecting instability in FX market dynamics, and that there are strong “tell-tale” signs characterized by low Approximate Entropy (ApEn) level during many of crash events (Bhaduri 2014 ). The approaches followed by Stosic et al ( 2016 ) and Bhaduri ( 2014 ) have the advantage that, as is the case for the entropy-based tools, they do not require a parametric model and work directly on the data.…”

Section: The Need Of Multidisciplinary Methodsmentioning

confidence: 99%

A critique of financial neoliberalism: a perspective combining multidisciplinary methods and commodity markets

Vellucci

2021

SN Bus Econ

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The era of financialization bas been marked by a huge increase in the size of the financial sector. The economic justifications for this expansion arise from the prevailing neo-liberal ideology; they are based on arguments like leading role of markets, economic efficiency, reallocation and spreading of risk. Given these reasons, financialization can be viewed as a form of neoliberalism and then we can use the term financial neoliberalism (Palley in Financialization: the economics of finance capital domination. Springer, 2016) to denote it. This commentary paper hopes to complement this view by highlighting the usefulness of recent “multidisciplinary” methods (i.e., methods which do not fall within the usual economic discipline). We focus first on the definition of financialization process, second on its implications on the commodity markets, and third and finally on the importance of the role of multidisciplinary methods, applied to these markets, capable of highlighting the contradictions of the neoliberalism framework, in such a way, we hope, to promote further research.

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“…Its development was motivated by the length constraints of biological data. Since then it has been used in different fields such as psychology [118], psychiatry [119], and finance [120][121][122][123][124]. Pincus co-authored with Kalman [124], considering both empirical data and models, including composite indices, individual stock prices, the random-walk hypothesis, Black-Sholes, and fractional Brownian motion models to demonstrate the benefits of ApEn applied to the classical econometric modeling apparatus.…”

Section: Entropy As a Measure Of Complexitymentioning

confidence: 99%

Econophysics of cryptocurrency crashes: an overview

et al. 2021

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Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.

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Applying Approximate Entropy (ApEn) to Speculative Bubble in the Stock Market

Cited by 10 publications

References 18 publications

Quantifying the randomness of the stock markets

Quantifying the randomness of the stock markets

A critique of financial neoliberalism: a perspective combining multidisciplinary methods and commodity markets

Econophysics of cryptocurrency crashes: an overview

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