Bitcoin and stock markets: a revisit of relationship

The volatility of bitcoin (BTC) and time horizon is the center point for investment decisions. However, attention is not often drawn to the relationship between BTC and equity indices. Thus, the purpose of this paper is to investigate the volatility and time frequency domain of BTC with stock markets.

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Investigating the impact of Covid-19 on stock markets

International Journal of Research in Business and Social Science (2147-4478) ◽

10.20525/ijrbs.v10i7.1461 ◽

2021 ◽

Vol 10 (7) ◽

pp. 199-219

Author(s):

Mustapher Faque ◽

Umit Hacioglu

Keyword(s):

Stock Markets ◽

Equity Markets ◽

Adverse Impact ◽

Vector Error Correction ◽

Vector Error ◽

Clear Sign ◽

Vector Error Correction Models ◽

Equity Indices ◽

The Impact ◽

The Relationship

This paper aims to examine the impact of Covid-19 pandemic on stock markets. This paper also analyses the stock market cointegration of selected global equity indices that performed better and have a quick speed of recovery during the pandemic. This paper also questions how increasing uncertainty and volatility deters investors’ perception of the diversification of equity investments. The dataset for the selected 12 global equity indices has been used from Thompson Reuters’s EIKON database in a given period of time between 2010 and 2021. This paper employs Vector Error Correction Models to assess the relationship among the selected global equity indices. Findings demonstrate that (i) there is an adverse impact of Covid-19 on the Global Equity markets, (ii) there is a clear sign of cointegration in global equity indices, (ii) investors can benefit from investing in particular equity indices that have exhibited quick speed of recovery from the pandemic records lows. The findings finally provide a strong foundation for constructing a resilient equity portfolio in a highly uncertain market environment.

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Dynamic dependence and risk connectedness among oil and stock markets: New evidence from time-frequency domain perspectives

Energy ◽

10.1016/j.energy.2020.119302 ◽

2021 ◽

Vol 216 ◽

pp. 119302

Author(s):

Jinxin Cui ◽

Mark Goh ◽

Binlin Li ◽

Huiwen Zou

Keyword(s):

Frequency Domain ◽

Stock Markets ◽

Time Frequency ◽

New Evidence

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Data-Driven Bearing Fault Diagnosis of Microgrid Network Power Device Based on a Stacked Denoising Autoencoder in Deep Learning and Clustering by Fast Search without Data Labels

Complexity ◽

10.1155/2020/5013871 ◽

2020 ◽

Vol 2020 ◽

pp. 1-29

Author(s):

Fan Xu ◽

Xin Shu ◽

Xin Li ◽

Xiaodi Zhang

Keyword(s):

Deep Learning ◽

Frequency Domain ◽

Permutation Entropy ◽

Cluster Center ◽

Multiple Model ◽

Denoising Autoencoder ◽

Time Frequency ◽

Fast Search ◽

Center Point ◽

Clustering Center

The traditional health indicator (HI) construction method of electric equipment devices in microgrid networks, such as bearings that require different time-frequency domain indicators, needs several models to combine. Therefore, it is necessary to manually select appropriate and sensitive models, such as time-frequency domain indicators and multimodel fusion, to build HIs in multiple steps, which is more complicated because sensitivity characteristics and suitable models are more representatives of bearing degradation trends. In this paper, we use the stacked denoising autoencoder (SDAE) model in deep learning to construct HI directly from the microgrid power equipment of raw signals in bearings. With this model, the HI can be constructed without multiple model combinations or the need for manual experience in selecting the sensitive indicators. The SDAE can extract the representative degradation information adaptively from the original data through several nonlinear hidden layers automatically and approximate complicated nonlinear functions with a small reconstruction error. After the SDAE extracts the preliminary HI, a model is needed to divide the wear state of the HI constructed by the SDAE. A cluster model is commonly used for this, and unlike most clustering methods such as k-means, k-medoids, and fuzzy c-means (FCM), in which the clustering center point must be preset, cluster by fast search (CFS) can automatically find available cluster center points automatically according to the distance and local density between each point and its clustering center point. Thus, the selected cluster center points are used to divide the wear state of the bearing. The root mean square (RMS), kurtosis, Shannon entropy (SHE), approximate entropy (AE), permutation entropy (PE), and principal component analysis (PCA) are also used to construct the HI. Finally, the results show that the performance of the method (SDAE-CFS) presented is superior to other combination HI models, such as EEMD-SVD-FCM/k-means/k-medoids, stacked autoencoder-CFS (SAE-CFS), RMS, kurtosis, SHE, AE, PE, and PCA.

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Media Coverage and Stock Return in the Taiwan Stock Market

Acta Oeconomica ◽

10.1556/032.65.2015.s2.4 ◽

2015 ◽

Vol 65 (s2) ◽

pp. 35-53 ◽

Cited By ~ 1

Author(s):

Kuei-Yuan Wang ◽

Chien-Kuo Chen ◽

Hsiao-Chi Wei

Keyword(s):

Stock Returns ◽

Stock Markets ◽

Media Coverage ◽

Stock Return ◽

Investment Decisions ◽

The Past ◽

Negative Effect ◽

The Media ◽

The Relationship ◽

Positive Effect

The purposes of this paper were to explore the relationship between media coverage and stock returns in Taiwan stock markets. The empirical results were as follows: (1) stock returns showed causality with either media coverage amounts or the degrees of good/bad media coverage; (2) when impacted by the past stock returns, the stock return might finish its response to the impulse around three days and showed a negative effect, whereas when impacted by the past media coverage amounts, the media coverage amount might also finish its response to the impulse within three day and showed a negative effect; (3) when impacted by the degrees of the past good media coverage, the good media coverage degree might finish its response in three days and showed a negative effect, in which a positive effect might be presented on the first two days, while the effect might turn negative on the third day. Given that, when impacted by the past stock returns, the stock return might finish its response to the impulse within three days and showed a negative effect and, when impacted by the degrees of the past good media coverage, the stock return might also finish its response in three days and showed a negative effect. That is, media coverage could be used as an indicator to predict stock returns in the Taiwan stock markets when making investment decisions.

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Bitcoin and Stock Markets: Are They Connected? Evidence from Asean Emerging Economies

10.21203/rs.3.rs-25524/v1 ◽

2020 ◽

Author(s):

Abdollah Ah Mand ◽

Hassanudin Mohd Thas Thaker

Keyword(s):

Emerging Economies ◽

Time Frequency ◽

Holding Period ◽

Long Run ◽

Daily Data ◽

Short Run ◽

Asean Countries ◽

Stock Portfolio ◽

Equity Indices ◽

The Relationship

Abstract Background: Cryptocurrencies, especially Bitcoin, has become popular for investors in recent years. The volatility of bitcoin and time horizon are the center point for investment decisions. However, attention is not often drawn to the relationship between bitcoin and equity indices. This study investigates the volatility and time frequency domain of bitcoin among five Asean countries through a rich database which covers daily data from July 2010 until April 2019.Methods: Advanced econometrics and Wavelets Cross-Coherence Spectrograms, this study investigates the existence of long run association between bitcoin and the studied market indices. M-GARCH analysis is been employed to investigate the unconditional volatility of market indices and Bitcoin.Results: The findings present the long run association with positive (Philippines) and negative (Japan, Korea, Singapore, Hong Kong) relations. Moreover, only one market (KOREA) shows a short run association with bitcoin. The M-GARCH analysis reveals, most of the selected Asean countries have a low unconditional volatility with bitcoin. Except for Philippines in which the co-movement is average, Wavelet analysis reveals the presence of a strong and long co-movements for most of the selected Asean countries with bitcoin.Conclusions: Most of our results are consistent and illustrate different dimensions of long and short run relationship, volatilities, correlations, and time-frequency analysis. This study utilized Asean emerging economies which are rarely available in the literature as existing studies are more skewed towards the West. We believe the outcomes of this study will be a significant for industry practitioners (i.e., retail and institutional investors) on designing better strategies to diversify the stock portfolio with different holding period horizons and dimensions.

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