The Informational Efficiency of the Romanian Stock Market: Evidence from Fractal Analysis

Pleşoianu, Anita; Todea, Alexandru; Razvan, Capusan

doi:10.1016/s2212-5671(12)00128-1

Cited by 13 publications

(11 citation statements)

References 14 publications

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“…The results prove that the intraday index returns do not follow a random process and are influenced by both large and small fluctuations in some periods. The presence of multifractality in European stock markets is reported by several studies (Jagric et al 2005;Domino 2011;Pleşoianu et al 2012;Caraiani 2012;Ferreira 2018). The empirical findings suggest that European stock markets do not support the weak form of efficiency.…”

Section: Discussionmentioning

confidence: 81%

Evidence of Intraday Multifractality in European Stock Markets during the Recent Coronavirus (COVID-19) Outbreak

Aslam

Mohti

Ferreira

2020

IJFS

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This study assesses how the coronavirus pandemic (COVID-19) affects the intraday multifractal properties of eight European stock markets by using five-minute index data ranging from 1 January 2020 to 23 March 2020. The Hurst exponents are calculated by applying multifractal detrended fluctuation analysis (MFDFA). Overall, the results confirm the existence of multifractality in European stock markets during the COVID-19 outbreak. Furthermore, based on multifractal properties, efficiency varies among these markets. The Spanish stock market remains most efficient while the least efficient is that of Austria. Belgium, Italy and Germany remain somewhere in the middle. This far-reaching outbreak demands a comprehensive response from policy makers to improve market efficiency during such epidemics.

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Section: Discussionmentioning

confidence: 81%

Evidence of Intraday Multifractality in European Stock Markets during the Recent Coronavirus (COVID-19) Outbreak

Aslam

Mohti

Ferreira

2020

IJFS

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“…On the one hand, MF-DFA has been applied to agricultural or commodities markets such as oil or gold [16][17][18] and suggests that prices do not follow RWH. On the other hand, it has been adopted in financial markets, mostly capital markets [19][20][21][22][23][24][25][26][27][28][29][30][31][32], and suggests the existence of fractal properties. Due to the discovery of multifractal properties of the financial markets in the last decade, interest in financial analysis of stock markets using MF-DFA has increased.…”

Section: Introductionmentioning

confidence: 99%

“…However, most studies using multifractality rather focus on developed stock markets. The use of the technique for emerging CEE markets is uncommon [28][29][30][31][32], ignoring the potential to highlight multifractal properties in these markets.…”

Section: Introductionmentioning

confidence: 99%

Multifractal Detrended Fluctuation Analysis (MF-DFA) of Stock Market Indexes. Empirical Evidence from Seven Central and Eastern European Markets

et al. 2020

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In this paper, we present a comparative investigation of the multifractal properties of seven Central and Eastern European (CEE) stock markets using recent financial data up to August 2018 by employing seasonal and trend decompositions before applying multifractal detrended fluctuation analysis. We find that stock indices returns exhibit long-range correlations, supporting the idea that the stock markets in question are not efficient markets and have not reached a mature stage of market development. The results of the paper are of interest to investors looking for opportunities in these stock exchanges and also to policy makers in their endeavour of realizing institutional reforms in order to increase stock market efficiency and to support the sustainable growth of the financial markets.

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“…The h(q) value is the Hurst exponent, often used to measure the dependence levels of financial assets (see, for example, in [38][39][40]). If h(q) = 0.5, the variable behaves like a random walk, while h(q) > 0.5 and h(q) < 0.5 represent, respectively, persistent and anti-persistent patterns.…”

Section: Multifractal Detrended Fluctuation Analysis (Mfdfa)mentioning

confidence: 99%

Investigating Long-Range Dependence of Emerging Asian Stock Markets Using Multifractal Detrended Fluctuation Analysis

2020

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The use of multifractal approaches has been growing because of the capacity of these tools to analyze complex properties and possible nonlinear structures such as those in financial time series. This paper analyzes the presence of long-range dependence and multifractal parameters in the stock indices of nine MSCI emerging Asian economies. Multifractal Detrended Fluctuation Analysis (MFDFA) is used, with prior application of the Seasonal and Trend Decomposition using the Loess (STL) method for more reliable results, as STL separates different components of the time series and removes seasonal oscillations. We find a varying degree of multifractality in all the markets considered, implying that they exhibit long-range correlations, which could be related to verification of the fractal market hypothesis. The evidence of multifractality reveals symmetry in the variation trends of the multifractal spectrum parameters of financial time series, which could be useful to develop portfolio management. Based on the degree of multifractality, the Chinese and South Korean markets exhibit the least long-range dependence, followed by Pakistan, Indonesia, and Thailand. On the contrary, the Indian and Malaysian stock markets are found to have the highest level of dependence. This evidence could be related to possible market inefficiencies, implying the possibility of institutional investors using active trading strategies in order to make their portfolios more profitable.

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The Informational Efficiency of the Romanian Stock Market: Evidence from Fractal Analysis

Cited by 13 publications

References 14 publications

Evidence of Intraday Multifractality in European Stock Markets during the Recent Coronavirus (COVID-19) Outbreak

Evidence of Intraday Multifractality in European Stock Markets during the Recent Coronavirus (COVID-19) Outbreak

Multifractal Detrended Fluctuation Analysis (MF-DFA) of Stock Market Indexes. Empirical Evidence from Seven Central and Eastern European Markets

Investigating Long-Range Dependence of Emerging Asian Stock Markets Using Multifractal Detrended Fluctuation Analysis

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