Abstract:We use wavelet multiresolution decomposition and cross-wavelet analysis to reveal certain properties in financial data related to mortgages to households and gross domestic product data in Spain. Wavelet techniques possess many desirable properties, some of which are useful as a vehicle for analysing economic and financial data. In our case, wavelets are useful for drawing conclusions both in the time and frequency domains and for obtaining information on the different phases through which the study variables … Show more
“…This strong volatility resulted from the port development process, owing to the influences of many uncertain factors. The periodicity of the high-energy regions was due to seasonal demand, represented by the high-frequency component ( d 1 and d 2 ) (González-Concepción et al 2012 ). Fig.…”
Section: Data Analytics For Port Throughputmentioning
Forecasting cargo throughput is an essential albeit challenging task in ensuring efficient seaport management. In this study, data analytics is employed to analyze the nonlinear dynamic behaviors, as well as disruptions in port throughputs. Further, nonlinear analytical methods, including the Lyapunov exponent (LE), information entropy, Hurst exponent, and wavelet decomposition, are employed to explore the complex dynamic behavior of port throughput under supply chain disruptions. By employing the discrete wavelet transform (DWT) and the long short-term memory (LSTM) network, we develop a novel hybrid model of port throughput forecasting. DWT is employed to decompose the original data into a finite set of frequency components, so that the various hidden features of cargo throughput can be extracted via different modes, such as the trend, residual, and seasonal components. Thereafter, each component, obtained from the DWT spectra, is predicted via a machine learning model. Additionally, hypothesis testing, model evaluation, and statistical significance tests are employed to comprehensively evaluate the introduced forecasting models. Regarding prediction accuracy and efficiency, our extensive simulation results confirm the superiority of the hybrid strategy over five benchmarked models. Finally, by employing business forecasting software, we show that the robust hybrid strategy achieves accurate predictions of port throughputs against market disruptions. Our findings can help decision-makers understand disruption mechanisms in port systems, thus enabling them to successfully achieve their business goals.
“…This strong volatility resulted from the port development process, owing to the influences of many uncertain factors. The periodicity of the high-energy regions was due to seasonal demand, represented by the high-frequency component ( d 1 and d 2 ) (González-Concepción et al 2012 ). Fig.…”
Section: Data Analytics For Port Throughputmentioning
Forecasting cargo throughput is an essential albeit challenging task in ensuring efficient seaport management. In this study, data analytics is employed to analyze the nonlinear dynamic behaviors, as well as disruptions in port throughputs. Further, nonlinear analytical methods, including the Lyapunov exponent (LE), information entropy, Hurst exponent, and wavelet decomposition, are employed to explore the complex dynamic behavior of port throughput under supply chain disruptions. By employing the discrete wavelet transform (DWT) and the long short-term memory (LSTM) network, we develop a novel hybrid model of port throughput forecasting. DWT is employed to decompose the original data into a finite set of frequency components, so that the various hidden features of cargo throughput can be extracted via different modes, such as the trend, residual, and seasonal components. Thereafter, each component, obtained from the DWT spectra, is predicted via a machine learning model. Additionally, hypothesis testing, model evaluation, and statistical significance tests are employed to comprehensively evaluate the introduced forecasting models. Regarding prediction accuracy and efficiency, our extensive simulation results confirm the superiority of the hybrid strategy over five benchmarked models. Finally, by employing business forecasting software, we show that the robust hybrid strategy achieves accurate predictions of port throughputs against market disruptions. Our findings can help decision-makers understand disruption mechanisms in port systems, thus enabling them to successfully achieve their business goals.
“…𝜔 0 is the mother wavelet's central frequency (the frequency at the centre of a Gaussian envelope) and To implement the CWT for this study, the Morlet wavelet is adopted because of its ability to be decomposed into both real and imaginary parts which allows for broader analysis of the dynamic movements between two signals or time series (Ferrer et al, 2018). Equally, an appropriate value of 𝜔 0 = 6 is employed as it provides a good balance between frequency and time localization, while simplifying interpretation given that the wavelet scale and frequency are inversely related (see Grinsted et al, 2004;Aguiar-Conraria et al, 2008;González-Concepción, Gil-Fariña & Pestano-Gabino, 2012;Aloui & Hkiri, 2014 for more detail). Subsequently, the Morlet wavelet used within this study is a truncated version expressed below.…”
“…Other researchers focused more on the operational differences between Islamic banking and conventional banks, as a cause of higher credit risk. They concluded that Islamic banks faced higher risks than conventional banks due to conflicts between their applied methods of handling credit risk and rules of Sharia (Elgari 2003;Ferhi 2018) There is a limited possibility for managing credit risk in Islamic banks, due to how Islamic rules relate to financial transactions. For instance, the use of haram goods, i.e.…”
Islamic Finance receives more attention due to the growing need for financial services in countries with a Muslim population. However, the rules of Islam and its applications in daily life cause conflicts in today's conventional financial system. Since interest gains are prohibited in Islam according to the Quran, Islamic banks develop and use interest-free methods, unlike the conventional banking system. Islamic Finance introduced profit-sharing ratios to replace interest rates and to increase the participation of religious investors in the financial system. In this research, we compare interest rates with profit-sharing ratios in the Turkish banking market. We use wavelet and historical correlation analysis as a new methodology in evaluating the association between these two factors. Although it is presumed that Islamic banks operate as interest-free banks, our analysis confirms former studies and finds that profit-sharing ratios are highly correlated and coherent with interest rates in Turkey. We also find small differences among Islamic banks on how quickly profit-sharing ratios follow the market interest rate changes.
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