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Article
Publication date: 25 June 2020

Minghua Wei and Feng Lin

Aiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this paper…

Abstract

Purpose

Aiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this paper proposes an EEG signals classification method based on multi-dimensional fusion features.

Design/methodology/approach

First, the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals. Then, the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks (3DCNNs) model. Finally, the spatial-frequency features are incorporated to the bidirectional gated recurrent units (Bi-GRUs) models to extract the spatial-frequency-sequential multi-dimensional fusion features for recognition of brain's sensorimotor region activated task.

Findings

In the comparative experiments, the data sets of motor imagery (MI)/action observation (AO)/action execution (AE) tasks are selected to test the classification performance and robustness of the proposed algorithm. In addition, the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.

Originality/value

The experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks, so as to achieve more stable classification performance in dealing with AO/MI/AE tasks, and has the best robustness on EEG signals of different subjects.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 29 March 2013

Mikko Ranta

The purpose of this paper is to examine contagion among the major world markets during the last 25 years and propose a new way to analyze contagion with wavelet methods.

Abstract

Purpose

The purpose of this paper is to examine contagion among the major world markets during the last 25 years and propose a new way to analyze contagion with wavelet methods.

Design/methodology/approach

The analysis uses a novel way to study contagion using wavelet methods. The comparison is made between co‐movements at different time scales. Co‐movement methods of the discrete wavelet transform and the continuous wavelet transform are applied.

Findings

Clear signs of contagion among the major markets are found. Short time scale co‐movements increase during the major crisis while long time scale co‐movements remain approximately at the same level. In addition, gradually increasing interdependence between markets is found.

Research limitations/implications

Because of the chosen method, the approach is limited to large data sets.

Practical implications

The research has practical implications to portfolio managers etc. who wish to have better view of the dynamics of the international equity markets.

Originality/value

The research uses novel wavelet methods to analyze world equity markets. These methods allow the markets to be analyzed in the whole state space.

Details

International Journal of Managerial Finance, vol. 9 no. 2
Type: Research Article
ISSN: 1743-9132

Keywords

Open Access
Article
Publication date: 1 April 2022

Stephan Bales and Hans-Peter Burghof

The paper examines the impact of COVID-19 on bank stock returns over various time scales and frequencies for 36 countries. Moreover, the authors look at the governments' responses…

1044

Abstract

Purpose

The paper examines the impact of COVID-19 on bank stock returns over various time scales and frequencies for 36 countries. Moreover, the authors look at the governments' responses to the corona crisis and examine its impact on bank stock returns.

Design/methodology/approach

The paper applies continuous wavelet transformation to obtain robust estimates of the co-movement (coherency) between confirmed cases and bank stock returns over time and at different time scales. Furthermore, the authors apply fixed effects panel regression to examine the response of bank stocks to domestic COVID-19 policies.

Findings

The results indicate that the number of confirmed COVID-19 cases negatively impacts bank stock returns during different waves of the pandemic in the medium-run. However, there is only little dependence in the very short-run. Moreover, bank stock returns positively react to domestic COVID-19 polices. This demonstrates that governmental interventions not only reduce the spread of COVID-19 but are also able to thereby calm financial markets.

Originality/value

The application of wavelet methods to the field of economics and finance is relatively recent and allows the distinction between short-term and long-term effects. Standard econometric methods, in contrast, only operate within the time domain. This paper combines wavelet methods with conventional econometrics to answer the research question.

Details

Fulbright Review of Economics and Policy, vol. 2 no. 1
Type: Research Article
ISSN: 2635-0173

Keywords

Article
Publication date: 9 March 2010

Lei Lin, De‐kai Xu and Hou‐jun Wang

The purpose of this paper is to provide a new method of the fault diagnosis of wireless sensor networks (WSNs) node, which is based on wavelet neural network (WNN).

Abstract

Purpose

The purpose of this paper is to provide a new method of the fault diagnosis of wireless sensor networks (WSNs) node, which is based on wavelet neural network (WNN).

Design/methodology/approach

The approach uses WNN to diagnose the sensor module of the node.

Findings

The method based on WNN sensing parts of the WSN nodes in additional fault location is accurate feasible.

Research limitations/implications

The fault of WSNs node protean, it is necessary to establish even more fault model for the training of WNN.

Practical implications

The simulation results provide useful guidelines for the engineers faced with the detection the fault of the WSN node.

Originality/value

The WNN is well‐known. The innovation here is applying this method in order to diagnose the fault of WSNs node.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 29 no. 2
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 20 July 2022

Seema Saini, Utkarsh Kumar and Wasim Ahmad

To the best of our knowledge, no study has examined credit cycle synchronizations in the context of emerging economies. Studying the credit cycles synchronization across BRICS…

Abstract

Purpose

To the best of our knowledge, no study has examined credit cycle synchronizations in the context of emerging economies. Studying the credit cycles synchronization across BRICS (Brazil, Russia, India, China and South Africa) countries is crucial given the magnitude of trade and financial integration among member counties. The enormity of the trade and financial linkages among BRICS countries and growth spillovers from emerging economies to advanced and low-income countries provide the rationale and motivation to study the synchronization of credit cycles across BRICS.

Design/methodology/approach

The study investigates the credit cycles coherence across BRICS economies from 1996Q2 to 2020Q4. The synchronization analysis is done using the noval wavelet approach. The analysis examines not only the coherence but also the extent of credit cycle synchronization that varies across frequencies and over time among different pairs of nations.

Findings

The authors find heterogeneity in the credit cycles' synchronization among the member nations. China and India are very much in sync with the other BRICS countries. China's high-frequency credit cycle mostly leads the other countries' credit cycles before the global financial crisis and shows a mix of lead/lag relationships post-financial crisis. Interestingly, most of the time, India's low-frequency credit cycles lead the member countries' credit cycles, and Brazil's low frequency credit cycle lag behind the other BRICS countries' credit cycles, except for Russia. The results are crucial from the macroprudential policymaker's perspective.

Research limitations/implications

The empirical design is applicable to a similar set of countries and may not directly fit each emerging economy.

Practical implications

The findings will help understand the marked deepening of trade, technology, investment and financial interdependence across the world. BRICS acronym requires no introduction, but such analysis may help understand the interaction at the monetary policy level.

Originality/value

This is the first study that highlights the need to understand the credit variable interactions for BRICS nations.

Details

International Journal of Emerging Markets, vol. 19 no. 3
Type: Research Article
ISSN: 1746-8809

Keywords

Article
Publication date: 22 November 2021

Byomakesh Debata, Kshitish Ghate and Jayashree Renganathan

This study aims to examine the relationship between pandemic sentiment (PS) and stock market returns in an emerging order-driven stock market like India.

Abstract

Purpose

This study aims to examine the relationship between pandemic sentiment (PS) and stock market returns in an emerging order-driven stock market like India.

Design/methodology/approach

This study uses nonlinear causality and wavelet coherence techniques to analyze the sentiment-returns nexus. The analysis is conducted on the full sample period from January to December 2020 and further extended to two subperiods from January to June and July to December to investigate whether the associations between sentiment and market returns persist even several months after the outbreak.

Findings

This study constructs two novel measures of PS: one using Google Search Volume Intensity and the other using Textual Analysis of newspaper headlines. The empirical findings suggest a high degree of interrelationship between PS and stock returns in all time-frequency domains across the full sample period. This interrelationship is found to be further heightened during the initial months of the crisis but reduces significantly during the later months. This could be because a considerable amount of uncertainty regarding the crisis is already accounted for and priced into the markets in the initial months.

Originality/value

The ongoing coronavirus pandemic has resulted in sharp volatility and frequent crashes in the global equity indices. This study is an endeavor to shed light on the ongoing debate on the COVID-19 pandemic, investors’ sentiment and stock market behavior.

Details

Review of Behavioral Finance, vol. 15 no. 2
Type: Research Article
ISSN: 1940-5979

Keywords

Article
Publication date: 18 September 2019

Mouna Abdelhedi and Mouna Boujelbène-Abbes

The purpose of this paper is to empirically investigate the volatility spillover between the Chinese stock market, investor’s sentiment and oil market, specifically during the…

Abstract

Purpose

The purpose of this paper is to empirically investigate the volatility spillover between the Chinese stock market, investor’s sentiment and oil market, specifically during the 2014‒2016 turmoil period.

Design/methodology/approach

This study used the daily and monthly China market price index, oil-price index and composite index of Chinese investor’s sentiment. The authors first use the DCC GARCH model in order to study the correlation between variables. Second, the authors use a continuous wavelet decomposition technique so as to capture both time- and frequency-varying features of co-movement variables. Finally, the authors examine the spillover effects by estimating the BEKK GARCH model.

Findings

The wavelet coherency results indicate a substantial co-movement between oil and Chinese stock markets in the periods of high volatility. BEKK GARCH model outcomes confirm this relation and report the noteworthy bidirectional transmission of volatility between oil market shocks and the Chinese investor’s sentiment, chiefly in the crisis period. These results support the behavioral theory of contagion and highlight that the Chinese investor’s sentiment is a channel through which shocks are transmitted between the oil and Chinese equity markets. Thus, these results are important for Chinese authorities that should monitor the investor’s sentiment to better control the interaction between financial and real markets.

Originality/value

This study makes three major contributions to the existing literature. First, it pays attention to the recent 2015 Chinese stock market bumble. Second, it has gone some way toward enhancing our understanding of the volatility spillover between the investor’s sentiment, investor’s sentiment variation, oil prices and stock market returns (variables of interest) during oil and stock market crises. Third, it uses the continuous wavelet decomposition technique since it reveals the linkage between variables of interest at different time horizons.

Details

International Journal of Emerging Markets, vol. 15 no. 2
Type: Research Article
ISSN: 1746-8809

Keywords

Article
Publication date: 28 June 2022

Hayet Soltani and Mouna Boujelbene Abbes

This study aims to investigate the impact of the COVID-19 pandemic on both of stock prices and investor's sentiment in China during the onset of the COVID-19 crisis.

Abstract

Purpose

This study aims to investigate the impact of the COVID-19 pandemic on both of stock prices and investor's sentiment in China during the onset of the COVID-19 crisis.

Design/methodology/approach

In this study, the ADCC-GARCH model was used to analyze the asymmetric volatility and the time-varying conditional correlation among the Chinese stock market, the investors' sentiment and its variation. The authors relied on Diebold and Yilmaz (2012, 2014) methodology to construct network-associated measures. Then, the wavelet coherence model was applied to explore the co-movements between these variables. To check the robustness of the study results, the authors referred to the RavenPack COVID sentiments and the Chinese VIX, as other measures of the investor's sentiment using daily data from December 2019 to December 2021.

Findings

Using the ADCC-GARCH model, a strong co-movement was found between the investor's sentiment and the Shanghai index returns during the COVID-19 pandemic. The study results provide a significant peak of connectivity between the investor's sentiment and the Chinese stock market return during the 2015–2016 and the end of 2019–2020 turmoil periods. These periods coincide, respectively, with the 2015 Chinese economy recession and the COVID-19 pandemic outbreak. Furthermore, the wavelet coherence analysis confirms the ADCC results, which revealed that the used proxies of the investor's sentiment can detect the Chinese investors' behavior especially during the health crisis.

Practical implications

This study provides two main types of implications: on the one hand, for investors since it helps them to understand the economic outlook and accordingly design their portfolio strategy and allocate decisions to optimize their portfolios. On the other hand, for portfolios managers, who should pay attention to the volatility spillovers between investor sentiment and the Chinese stock market to predict the financial market dynamics during crises periods and hedge their portfolios.

Originality/value

This study attempted to examine the time-varying interactions between the investor's sentiment proxies and the stock market dynamics. Findings showed that the investor's sentiment is considered a prominent channel of shock spillovers during the COVID-19 crisis, which typically confirms the behavioral contagion theory.

Details

Asia-Pacific Journal of Business Administration, vol. 15 no. 5
Type: Research Article
ISSN: 1757-4323

Keywords

Article
Publication date: 9 August 2022

Xiangnan Liu and Kuanfang He

The purpose of this paper is to propose a new fault feature extraction scheme for the rolling element bearing.

Abstract

Purpose

The purpose of this paper is to propose a new fault feature extraction scheme for the rolling element bearing.

Design/methodology/approach

The generalized Stockwell transform (GST) and the singular value ratio spectrum (SVRS) methods are combined. A time-frequency distribution measurement criterion named the energy concentration measurement (ECM) is initially used to determine the parameter of the optimal GST method. Then, the optimal GST is applied to conduct a time-frequency transformation for a raw signal. Subsequently, the two-dimensional time-frequency matrix is obtained. Finally, the improved singular value decomposition (SVD) analysis is used to conduct a noise reduction of the time-frequency matrix. The SVRS is proposed to select the effective singular values. Furthermore, the time-domain feature of the impact signal is obtained by taking the inverse GST transform.

Findings

The simulated and experimental signals are used to verify the superiority of the proposed method over conventional methods. The obtained results show that the proposed method can effectively extract fault features of the rolling element bearing.

Research limitations/implications

This paper mainly discusses the application of GST and SVRS methods to analyze the weak fault feature extraction problem. The next research direction is to explore the application of the Hilbert Huang transform (HHT) and variational modal decomposition (VMD) in the impact feature extraction of rolling bearing.

Originality/value

In the present study, a new SVRS method is proposed to select the number of effective singular values. This paper proposed an effective way to obtain the fault feature in monitoring of rotating machinery.

Article
Publication date: 19 January 2021

Fatma Alahouel and Nadia Loukil

This study examines co-movements between global Islamic index and heterogeneous rated/maturity sukuk. It tests the impact of financial uncertainty on these movements.

Abstract

Purpose

This study examines co-movements between global Islamic index and heterogeneous rated/maturity sukuk. It tests the impact of financial uncertainty on these movements.

Design/methodology/approach

Firstly, we conduct a bivariate wavelet analysis to assess the co-movements between stocks and sukuk indexes. Secondly, we use General dynamic factor model and stochastic volatility to construct financial uncertainty index from Islamic stock indexes. Finally, we run regression analysis to determine the impact of uncertainty on the obtained correlations.

Findings

Our results suggest the absence of flight to quality phenomenon since correlations are positive especially at a short investment horizon. There is evidence of contagion phenomena across assets. Financial uncertainty may be considered as a determinant of stock-sukuk co-movements. Our results show that a rise in financial uncertainty induces correlation to move in the opposite direction in the short term, (exception for correlation with AA-Rated sukuk). However, the sign of stock market uncertainty becomes positive in the long term, which leads sukuk and stocks to move in the same direction (exception for 1–3 Year and AA Rated sukuk).

Practical implications

Investors may combine sukuk with 1–3 Year maturity and AA Rated when considering long holding periods. Further, all sukuk categories provide diversification benefit in time high financial uncertainty expectation for AA Rated sukuk when considering short holding periods.

Originality/value

To the best of our best knowledge, our study is the first investigation of the impact of financial uncertainty on Stock-sukuk co-movements and provides recommendation considering sukuk with different characteristics.

Details

International Journal of Emerging Markets, vol. 17 no. 8
Type: Research Article
ISSN: 1746-8809

Keywords

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