scholarly journals Time-Varying Nexus between Investor Sentiment and Cryptocurrency Market: New Insights from a Wavelet Coherence Framework

2021 ◽  
Vol 14 (6) ◽  
pp. 275
Author(s):  
Hashem A. AlNemer ◽  
Besma Hkiri ◽  
Muhammed Asif Khan
Keyword(s):  
Predictive Power ◽  
Investor Sentiment ◽  
Wavelet Coherence ◽  
Significant Information ◽  
Investor Confidence ◽  
Dependent Relationship ◽  
Confidence Index ◽  
Relationship Of ◽  
Multiple Wavelet Coherence

This study attempts to investigate the nexus between investor sentiment and cryptocurrencies prices. Our empirical investigation merges bivariate and multivariate wavelet tools to examine the investor sentiment nexus to inter-cryptocurrencies prices. The study outcomes show that the Sentix Investor Confidence index provides significant information in explaining long-term changes in Bitcoin and Litecoin prices. Moreover, the findings generated from the multiple wavelet coherence illustrate the simultaneous contribution of cryptocurrencies and the Sentix Investor Confidence index in explaining the Bitcoin index movement across frequencies and over horizons, especially during bubble burst periods. The study also suggests a time-dependent relationship of Bitcoin prices with alternative cryptocurrencies and the Sentix Investor Confidence index, mostly pronounced during the Bitcoin bubble. We discuss our results using GSV-based investor sentiment. Our findings remain robust and confirm the strong predictive power of investor sentiment in cryptocurrencies price movements over time and across scales.

Investor Sentiment and Stock Returns

2015 ◽  
pp. 315-328
Author(s):  
Serkan Yılmaz Kandır ◽  
Veli Akel ◽  
Murat Çetin
Keyword(s):  
Stock Returns ◽  
Investor Sentiment ◽  
Consumer Confidence ◽  
Cointegration Test ◽  
Sample Period ◽  
Monthly Data ◽  
Out Of Sample ◽  
Confidence Index ◽  
The Relationship

In this chapter, the authors investigate the relationship between investor sentiment and stock returns in an out of sample market, namely Borsa Istanbul. The authors use the Consumer Confidence Index as an investor sentiment proxy, while utilizing BIST Second National Index as a measure of small capitalized stock returns. The sample period spans from January 2004 to May 2014. By using monthly data, the authors employ cointegration test and error–correction based Granger causality models. The authors' findings suggest that there is a long-term relationship between investor sentiment and stock returns in Borsa Istanbul. Moreover, a unidirectional causal relationship from investor sentiment to stock returns is also found.

REVISITING THE “PURE” OIL-EXCHANGE CO-MOVEMENT FROM A TIME-DOMAIN PERSPECTIVE

2020 ◽  
pp. 1-20
Author(s):  
Zhe Ma ◽  
Lu Yang
Keyword(s):  
Monetary Policy ◽  
Crude Oil ◽  
Oil Price ◽  
Wavelet Coherence ◽  
Crude Oil Price ◽  
Low Degree ◽  
The Difference ◽  
Japanese Yen ◽  
Multiple Wavelet Coherence

In this paper, we examine the differences between CNY and other major currencies in coherence and the lead–lag relationship across the different time horizons to clarify whether crude oil, monetary factors, or both drive the movement of exchange rates. We employ partial and multiple wavelet coherence analyses to examine oil-exchange co-movement by excluding the influence of Federal Reserve System (FED) monetary policy — namely, the stance and uncertainty of monetary policy — and the difference in domestic and foreign monetary policy rates. Overall, we find that monetary easing by the FED is a major factor driving the co-movement. Specifically, after excluding the possible effects of monetary policy factors, the movement of the Euro exhibits the strongest and the Japanese yen the weakest dependence on crude oil price changes, whereas the British pound shows a moderate dependence. By contrast, the CNY shows strong co-movement with the crude oil price only over the long term implying the low degree of integration with the global markets. Our empirical results provide meaningful information for investors and policymakers.

scholarly journals Domain randomization-enhanced deep learning models for bird detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Mao ◽  
Jun Kang Chow ◽  
Pin Siang Tan ◽  
Kuan-fu Liu ◽  
Jimmy Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Monitoring ◽  
Bird Species ◽  
Training Data ◽  
Learning Models ◽  
Fine Grained ◽  
Bird Detection ◽  
Relationship Of ◽  
The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

Effect of economic indicators, biomass energy on human development in China

2021 ◽  
pp. 0958305X2110220
Author(s):  
Ngo Thai Hung
Keyword(s):  
Human Development ◽  
Global Financial Crisis ◽  
Trade Openness ◽  
Biomass Energy ◽  
Wavelet Coherence ◽  
Causality Test ◽  
Frequency Domains ◽  
Bidirectional Relationship ◽  
The Global Financial Crisis

Previous studies ignored the distinction between short, medium, and long term by decomposing macroeconomic variables and human development index at different time scales. We re-visit the causal association between biomass energy (BIO), economic growth (GDP), trade openness (TRO), industrialization (IND), foreign direct investment (FDI), and human development (HDI) in China on a quarterly scale by scale basis for the period 1990 to 2019 using the tools of wavelet, i.e., wavelet correlation, wavelet coherence and scale by scale Granger causality test. The main findings uncover that IND, TRO, GDP, and BIO positively drive the HDI at low and medium frequencies, while FDI negatively impacts HDI during the sample period. Additionally, there is a bidirectional relationship between GDP and HDI at different time and frequency domains. Specifically, we discover that the positive co-movement is more robust in the aftermath of the global financial crisis, particularly for HDI, BIO, GDP, and TRO at medium frequencies throughout the period under research. Our empirical insights have significant implications for achieving human development sustainability in China.

scholarly journals Non-Local and Multi-Scale Mechanisms for Image Inpainting

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3281
Author(s):  
Xu He ◽  
Yong Yin
Keyword(s):  
Markov Random Fields ◽  
Receptive Fields ◽  
Image Inpainting ◽  
Long Distance ◽  
Visual Appearance ◽  
Fine Grained ◽  
Multi Scale ◽  
Non Local ◽  
Relationship Of

Recently, deep learning-based techniques have shown great power in image inpainting especially dealing with squared holes. However, they fail to generate plausible results inside the missing regions for irregular and large holes as there is a lack of understanding between missing regions and existing counterparts. To overcome this limitation, we combine two non-local mechanisms including a contextual attention module (CAM) and an implicit diversified Markov random fields (ID-MRF) loss with a multi-scale architecture which uses several dense fusion blocks (DFB) based on the dense combination of dilated convolution to guide the generative network to restore discontinuous and continuous large masked areas. To prevent color discrepancies and grid-like artifacts, we apply the ID-MRF loss to improve the visual appearance by comparing similarities of long-distance feature patches. To further capture the long-term relationship of different regions in large missing regions, we introduce the CAM. Although CAM has the ability to create plausible results via reconstructing refined features, it depends on initial predicted results. Hence, we employ the DFB to obtain larger and more effective receptive fields, which benefits to predict more precise and fine-grained information for CAM. Extensive experiments on two widely-used datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art approaches both in quantity and quality.

scholarly journals Light a lamp and look at the stock market

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Radeef Chundakkadan
Keyword(s):  
Stock Market ◽  
Financial Distress ◽  
Investor Sentiment ◽  
Downside Risk ◽  
Bond Yields ◽  
Risky Assets ◽  
Market Participants ◽  
Market Return ◽  
The Impact

AbstractIn this study, we investigate the impact of the light-a-lamp event that occurred in India during the COVID-19 lockdown. This event happened across the country, and millions of people participated in it. We link this event to the stock market through investor sentiment and misattribution bias. We find a 9% hike in the market return on the post-event day. The effect is heterogeneous in terms of beta, downside risk, volatility, and financial distress. We also find an increase (decrease) in long-term bond yields (price), which together suggests that market participants demanded risky assets in the post-event day.

Technology and Employment in Developing Countries

1978 ◽  
Vol 16 (4) ◽  
pp. 549-564 ◽  
Author(s):  
J. W. Garmany
Keyword(s):  
Developing Countries ◽  
Optimum Combination ◽  
Current Consumption ◽  
The Creation ◽  
Relationship Of ◽  
The Relationship ◽  
Choice Of Technology

This article discusses some of the issues involved in the choice of technology in developing countries, especially those in Africa, and the relationship of this to employment and output. The problem is to find an optimum combination of productive resources that comes nearest to satisfying two objectives: the full and economically efficient utilisation of such resources, and the creation of as much surplus as possible over current consumption, thereby making possible new investment and long-term growth.

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