Do global financial crises validate assertions of fractal market hypothesis?

Dar, Arif Billah; Bhanja, Niyati; Tiwari, Aviral Kumar

doi:10.1007/s10368-015-0332-0

Cited by 12 publications

(7 citation statements)

References 6 publications

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“…Since investors with different investment horizons interact with each other, they should behave similarly if we scale the time horizon appropriately. As a result, asset prices should exhibit “self-affinity” or “self-similarity” in the same market state [35] . Empirically, many time series exhibit multifractality, that is, they behave similarly in normal times or during market turmoil, respectively, but not so across each regime [22] , [36] .…”

Section: Asymmetric Mf-dfa Approachmentioning

confidence: 99%

Asymmetric efficiency of cryptocurrencies during COVID19

Naeem

Bouri

Peng

et al. 2021

Physica A: Statistical Mechanics and its Applications

134

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Section: Asymmetric Mf-dfa Approachmentioning

confidence: 99%

Asymmetric efficiency of cryptocurrencies during COVID19

Naeem

Bouri

Peng

et al. 2021

Physica A: Statistical Mechanics and its Applications

134

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“…Several contributions have dealt with the FMH, with the aim to transpose it in a rigorous mathematical model or, more often, to test its descriptive potential, or also to study the relation between market illiquidity and turbulent periods, mostly with regard to the 2007 to 2009 global financial crisis . Generally, the findings attained using different methodologies (DFA, Wavelet power spectra, variance scaling, R/S analysis) confirm the capability of FMH to describe the market behavior more effectively than the mainstream EMH.…”

Section: Introductionmentioning

confidence: 96%

A distribution‐based method to gauge market liquidity through scale invariance between investment horizons

Bianchi

Pianese

Frezza

2020

Appl Stoch Models Bus & Ind

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A nonparametric method is developed to detect self-similarity among the rescaled distributions of the log-price variations over a number of time scales. The procedure allows to test the statistical significance of the scaling exponent that possibly characterizes each pair of time scales and to analyze the link between self-similarity and liquidity, the core assumption of the fractal market hypothesis. The method can support financial operators in the selection of the investment horizons as well as regulators in the adoption of guidelines to improve the stability of markets. The analysis performed on the S&P500 reveals a very complex, time-changing scaling structure, which confirms the link between market liquidity and self-similarity.

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“…The majority of studies have used AI techniques to support those arguments, and the fact that certain players can consistently outperform the market demonstrates that the EMH might not be entirely accurate in practice (Asadi et al 2012). As a viable alternative to the EMH, the fractal market hypothesis (FMH) has also been established (Dar et al 2017) by Peters (1994). According to the FMH, markets are stabilized by matching the demand and supply of investors' investment horizons, whereas the EMH supposes that markets are in equilibrium (Dar et al 2017;Karp and Van Vuuren 2019).…”

Section: Introductionmentioning

confidence: 99%

“…As a viable alternative to the EMH, the fractal market hypothesis (FMH) has also been established (Dar et al 2017) by Peters (1994). According to the FMH, markets are stabilized by matching the demand and supply of investors' investment horizons, whereas the EMH supposes that markets are in equilibrium (Dar et al 2017;Karp and Van Vuuren 2019).…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda

Chopra

Sharma

2021

JRFM

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The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal and external environmental variables. Artificial intelligence (AI) techniques can detect such non-linearity, resulting in much-improved forecast results. This paper reviews 148 studies utilizing neural and hybrid-neuro techniques to predict stock markets, categorized based on 43 auto-coded themes obtained using NVivo 12 software. We group the surveyed articles based on two major categories, namely, study characteristics and model characteristics, where ‘study characteristics’ are further categorized as the stock market covered, input data, and nature of the study; and ‘model characteristics’ are classified as data pre-processing, artificial intelligence technique, training algorithm, and performance measure. Our findings highlight that AI techniques can be used successfully to study and analyze stock market activity. We conclude by establishing a research agenda for potential financial market analysts, artificial intelligence, and soft computing scholarship.

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Do global financial crises validate assertions of fractal market hypothesis?

Cited by 12 publications

References 6 publications

Asymmetric efficiency of cryptocurrencies during COVID19

Asymmetric efficiency of cryptocurrencies during COVID19

A distribution‐based method to gauge market liquidity through scale invariance between investment horizons

Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda

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