Multifractal Early Warning Signals about Sudden Changes in the Stock Exchange States

Critical phenomena in stock exchange are regularly occurring and difficult to predict events, often leading to disastrous consequences. The presented paper is devoted to the search and research of early warning signals of critical transitions in stock exchange based on the results of a multifractal...

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Main Authors: Andrey Dmitriev, Andrey Lebedev, Vasily Kornilov, Victor Dmitriev
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2022/8177307
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author Andrey Dmitriev
Andrey Lebedev
Vasily Kornilov
Victor Dmitriev
author_facet Andrey Dmitriev
Andrey Lebedev
Vasily Kornilov
Victor Dmitriev
author_sort Andrey Dmitriev
collection DOAJ
description Critical phenomena in stock exchange are regularly occurring and difficult to predict events, often leading to disastrous consequences. The presented paper is devoted to the search and research of early warning signals of critical transitions in stock exchange based on the results of a multifractal analysis of a series of transactions in shares of public companies. We have proposed and justified the use of certain features of behavior of multifractal spectrum shape parameters such as signals. As model time series, on which methods of multifractal analysis were tested, we used a series of the number of unstable sites of the sandpile automaton on the random Erdős–Rényi graph, self-organizing into critical and bistable states. It was found that the early warning signals for both cellular automata and stock exchanges are an increase in the magnitude of the maximum position, a decrease in the width, and a decrease, followed by a sharp increase, in the value of the spectrum asymmetry parameter.
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spelling doaj-art-48ff9509e08845cc829dd5f00bfa2c602025-08-20T02:05:55ZengWileyComplexity1099-05262022-01-01202210.1155/2022/8177307Multifractal Early Warning Signals about Sudden Changes in the Stock Exchange StatesAndrey Dmitriev0Andrey Lebedev1Vasily Kornilov2Victor Dmitriev3National Research University Higher School of EconomicsNational Research University Higher School of EconomicsNational Research University Higher School of EconomicsNational Research University Higher School of EconomicsCritical phenomena in stock exchange are regularly occurring and difficult to predict events, often leading to disastrous consequences. The presented paper is devoted to the search and research of early warning signals of critical transitions in stock exchange based on the results of a multifractal analysis of a series of transactions in shares of public companies. We have proposed and justified the use of certain features of behavior of multifractal spectrum shape parameters such as signals. As model time series, on which methods of multifractal analysis were tested, we used a series of the number of unstable sites of the sandpile automaton on the random Erdős–Rényi graph, self-organizing into critical and bistable states. It was found that the early warning signals for both cellular automata and stock exchanges are an increase in the magnitude of the maximum position, a decrease in the width, and a decrease, followed by a sharp increase, in the value of the spectrum asymmetry parameter.http://dx.doi.org/10.1155/2022/8177307
spellingShingle Andrey Dmitriev
Andrey Lebedev
Vasily Kornilov
Victor Dmitriev
Multifractal Early Warning Signals about Sudden Changes in the Stock Exchange States
Complexity
title Multifractal Early Warning Signals about Sudden Changes in the Stock Exchange States
title_full Multifractal Early Warning Signals about Sudden Changes in the Stock Exchange States
title_fullStr Multifractal Early Warning Signals about Sudden Changes in the Stock Exchange States
title_full_unstemmed Multifractal Early Warning Signals about Sudden Changes in the Stock Exchange States
title_short Multifractal Early Warning Signals about Sudden Changes in the Stock Exchange States
title_sort multifractal early warning signals about sudden changes in the stock exchange states
url http://dx.doi.org/10.1155/2022/8177307
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