Fractality and Multifractality in a Stock Market’s Nonstationary Financial Time Series

Jung, Nam Soon; Le, Quang A.; Mafwele, Biseko Juma; Lee, Hyun Min; Chae, Seo Yoon; Lee, Jae Woo

doi:10.3938/jkps.77.186

Cited by 2 publications

(2 citation statements)

References 147 publications

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“…Although the R/S method can describe the fractal characteristics of each stock market, the premise of the application of the R/S method is that the time series it targets must be a stationary time series. In fact, a large number of studies have proved that financial time series are often nonstationary [ 21 , 22 ]. In addition, the Hurst index calculated by R/S method is prone to the interference of short-term memory, so the calculation results are often higher than the actual situation.…”

Section: Literature Reviewmentioning

confidence: 99%

Asymmetric Fractal Characteristics and Market Efficiency Analysis of Style Stock Indices

Cai

Peng

et al. 2022

Entropy

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As a typical complex system, the stock market has attracted the attention of scholars and investors to comprehensively understand its fractal characteristics and analyze its market efficiency. Firstly, this paper proposes an asymmetric, detrended fluctuation analysis based on overlapping sliding windows (OSW-A-MFDFA). It reduces the generation of fluctuation errors, and the calculation results are more robust and reliable. The advantage of the OSW-A-MFDFA is that it not only can reveal the multifractal characteristics of time series clearly, but also can further accurately analyze the asymmetry of fractal characteristics under different trends. Secondly, this paper focuses on the variation in the width difference and height difference of the multifractal spectrum under different trends. Finally, based on multifractality, this paper proposes a comprehensive indicator MED that can be used to measure market efficiency, which is characterized by traversing all fluctuation orders. The application revealed many interesting findings in style stock indices. Style stock indices have asymmetric multifractal characteristics, and there are significant differences in the fractal spectrum of different style assets. Moreover, the market efficiency of style stock indices is time-varying, which can be reasonably explained from the perspective of the adaptive market hypothesis.

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Section: Literature Reviewmentioning

confidence: 99%

Asymmetric Fractal Characteristics and Market Efficiency Analysis of Style Stock Indices

Cai

Peng

et al. 2022

Entropy

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“…We might infer from the principles that underpin technical analysis that history is a repeating cycle. Numerous time series of the financial market have been discovered to include stylized facts of the financial market, and there are many stylized facts of financial market indexes to be found [ 1 , 2 ] as well. As a result, in this paper, we will discuss ways for forecasting market prices.…”

Section: Introductionmentioning

confidence: 99%

A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model

Zhang

Zhou

et al. 2022

Computational Intelligence and Neuroscience

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As a result of the fast growth of financial technology and artificial intelligence around the world, quantitative algorithms are now being employed in many classic futures and stock trading, as well as hot digital currency trades, among other applications today. Using the historical price series of Bitcoin and gold from 9/11/2016 to 9/10/2021, we investigate an LSTM-P neural network model for predicting the values of Bitcoin and gold in this research. We first employ a noise reduction approach based on the wavelet transform to smooth the fluctuations of the price data, which has been shown to increase the accuracy of subsequent predictions. Second, we apply a wavelet transform to diminish the influence of high-frequency noise components on prices. Third, in the price prediction model, we develop an optimized LSTM prediction model (LSPM-P) and train it using historical price data for gold and Bitcoin to make accurate predictions. As a consequence of our model, we have a high degree of accuracy when projecting future pricing. In addition, our LSTM-P model outperforms both the conventional LSTM models and other time series forecasting models in terms of accuracy and precision.

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Fractality and Multifractality in a Stock Market’s Nonstationary Financial Time Series

Cited by 2 publications

References 147 publications

Asymmetric Fractal Characteristics and Market Efficiency Analysis of Style Stock Indices

Asymmetric Fractal Characteristics and Market Efficiency Analysis of Style Stock Indices

A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model

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