Evidence of the fractal market hypothesis in European industry sectors with the use of bootstrapped wavelet leaders singularity spectrum analysis

Lahmiri, Salim; Bekiros, Stelios; Bezzina, Frank

doi:10.1016/j.chaos.2022.112813

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…At present, the study by Golyandina "Caterpillar Method -SSA Analysis of Time Series" [10] gives the most comprehensive description of the method. It is worth noting that foreign authors also refer to the study by Golyandina as the original source [11,12]. Other studies devoted to SSA analysis also belong to the Russian authors Leontyeva [13] and Danilov [14].…”

Section: Methodsmentioning

confidence: 99%

“…The coefficients of the Fourier series are calculated using the following formulas: (11) for the first harmonic, (12) for the second harmonic and so on. Usually, the first two or three harmonics are sufficient for forecasting.…”

Section: Methodsmentioning

confidence: 99%

“…Usually, the first two or three harmonics are sufficient for forecasting. The Fourier coefficients obtained by formulas (11) and (12) are substituted into the equation (10), and forecasting is performed using this equation. In this work, for forecasting by the Fourier transform method, use was made of the code in the Python programming language developed by the author.…”

Section: Methodsmentioning

confidence: 99%

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Forecasting financial time series using singular spectrum analysis

Zinenko

2023

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Financial time series are big arrays of information on quotes and trading volumes of shares, currencies and other exchange and over-the-counter instruments. The analysis and forecasting of such series has always been of particular interest for both research analysts and practicing investors. However, financial time series have their own features, which do not allow one to choose the only correct and well-functioning forecasting method. Currently, machine-learning algorithms allow one to analyze large amounts of data and test the resulting models. Modern technologies enable testing and applying complex forecasting methods that require volumetric calculations. They make it possible to develop the mathematical basis of forecasting, to combine different approaches into a single method. An example of such a modern approach is the Singular Spectrum Analysis (SSA), which combines the decomposition of a time series into a sum of time series, principal component analysis and recurrent forecasting. The purpose of this work is to analyze the possibility of applying SSA to financial time series. The SSA method was considered in comparison with other common methods for forecasting financial time series: ARIMA, Fourier transform and recurrent neural network. To implement the methods, a software algorithm in the Python language was developed. The method was also tested on the time series of quotes of Russian and American stocks, currencies and cryptocurrencies.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Forecasting financial time series using singular spectrum analysis

Zinenko

2023

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“…In our study, given its robustness and statistical efficiency in estimating multifractals when the dataset is small [30] , the wavelet leaders method [30] is employed to assess multifractal properties in price returns and trading volume variations of cryptocurrencies. Indeed, in recent works, the method was effective in multifractal characterization of high frequency Bitcoin data [30] , and of family business, sustainability, shariah, green, technology, and equity markets in European zone [31] .…”

Section: Introductionmentioning

confidence: 99%