Retail investor attention and stock price crash risk: Evidence from China

Wen, Fenghua; Xu, Longhao; Ouyang, Guangda; Kou, Gang

doi:10.1016/j.irfa.2019.101376

Cited by 287 publications

(146 citation statements)

References 62 publications

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“…In other words, an unexpected drop in asset prices tends to increase volatility more than an unexpected increase in asset prices of the same magnitude. The increased volatility in response to negative shocks might be explained by the herding of investors (Yousaf et al 2018 ; Wen et al 2019 ), that is, the selling of cryptocurrencies due to the fear of loss after the huge decline in global business activity during the COVID-19 period.…”

Section: Resultsmentioning

confidence: 99%

Discovering interlinkages between major cryptocurrencies using high-frequency data: new evidence from COVID-19 pandemic

Yousaf

Ali

2020

Financ Innov

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Through the application of the VAR-AGARCH model to intra-day data for three cryptocurrencies (Bitcoin, Ethereum, and Litecoin), this study examines the return and volatility spillover between these cryptocurrencies during the pre-COVID-19 period and the COVID-19 period. We also estimate the optimal weights, hedge ratios, and hedging effectiveness during both sample periods. We find that the return spillovers vary across the two periods for the Bitcoin–Ethereum, Bitcoin–Litecoin, and Ethereum–Litecoin pairs. However, the volatility transmissions are found to be different during the two sample periods for the Bitcoin–Ethereum and Bitcoin–Litecoin pairs. The constant conditional correlations between all pairs of cryptocurrencies are observed to be higher during the COVID-19 period compared to the pre-COVID-19 period. Based on optimal weights, investors are advised to decrease their investments (a) in Bitcoin for the portfolios of Bitcoin/Ethereum and Bitcoin/Litecoin and (b) in Ethereum for the portfolios of Ethereum/Litecoin during the COVID-19 period. All hedge ratios are found to be higher during the COVID-19 period, implying a higher hedging cost compared to the pre-COVID-19 period. Last, the hedging effectiveness is higher during the COVID-19 period compared to the pre-COVID-19 period. Overall, these findings provide useful information to portfolio managers and policymakers regarding portfolio diversification, hedging, forecasting, and risk management.

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Section: Resultsmentioning

confidence: 99%

Discovering interlinkages between major cryptocurrencies using high-frequency data: new evidence from COVID-19 pandemic

Yousaf

Ali

2020

Financ Innov

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“…They are not adaptable to changes in client's behaviors and market conditions over time. Besides, the behavior of the market has not been consistent over the years to the features to predict the default are always debatable [19]. Limited work was also done to solve the problem of data imbalance by using few resampling techniques [20,21], but results were not efficient.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

et al. 2020

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Financial threats are displaying a trend about the credit risk of commercial banks as the incredible improvement in the financial industry has arisen. In this way, one of the biggest threats faces by commercial banks is the risk prediction of credit clients. Recent studies mostly focus on enhancing the classifier performance for credit card default prediction rather than an interpretable model. In classification problems, an imbalanced dataset is also crucial to improve the performance of the model because most of the cases lied in one class, and only a few examples are in other categories. Traditional statistical approaches are not suitable to deal with imbalanced data. In this study, a model is developed for credit default prediction by employing various credit-related datasets. There is often a significant difference between the minimum and maximum values in different features, so Min-Max normalization is used to scale the features within one range. Data level resampling techniques are employed to overcome the problem of the data imbalance. Various undersampling and oversampling methods are used to resolve the issue of class imbalance. Different machine learning models are also employed to obtain efficient results. We developed the hypothesis of whether developed models using different machine learning techniques are significantly the same or different and whether resampling techniques significantly improves the performance of the proposed models. Oneway Analysis of Variance is a hypothesis-testing technique, used to test the significance of the results. The split method is utilized to validate the results in which data has split into training and test sets. The results on imbalanced datasets show the accuracy of 66.9% on Taiwan clients credit dataset, 70.7% on South German clients credit dataset, and 65% on Belgium clients credit dataset. Conversely, the results using our proposed methods significantly improve the accuracy of 89% on Taiwan clients credit dataset, 84.6% on South German clients credit dataset, and 87.1% on Belgium clients credit dataset. The results show that the performance of classifiers is better on the balanced dataset as compared to the imbalanced dataset. It is also observed that the performance of data oversampling techniques are better than undersampling techniques. Overall, the Gradient Boosted Decision Tree method performs better than other traditional machine learning classifiers. The Gradient Boosted Decision Tree method gives the best results while utilizing the K-means SMOTE oversampling method. Using one-way ANOVA, the null hypothesis was rejected by a p-value <0.001, hence confirming that the proposed model improved performance is statistical significance. The interpretable model is also deployed on the web to ease the different stakeholders. This model will help commercial banks, financial organizations, loan institutes, and other decision-makers to predict the loan defaulter earlier.

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“…These highly inter-coupled systems have been the focus of a great number of researches, which have investigated influential nodes of the systems. The analysis of key nodes for complex systems is of crucial importance, as it can help in ensuring the more efficient spread of information for technological system [13,14], hindering crisis propagation for finance systems, and so on [19,[25][26][27]. In addition to the above discussed complex systems, recently, the global remittance has been incipiently investigated from a complex network perspective [28,29].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Global Remittance Based on Complex Networks

Wen

et al. 2020

Front. Phys.

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Remittance, one money flow between immigrants and their relatives, is a major source of foreign exchange revenue for economies. Consisting of economies linked by the money flows, global remittance constitutes a network. In this paper, we use bilateral remittances of 210 economies for the time period 2010-2016 to construct a global remittance network (GRN) and then investigate the network's structural properties. We study the degree distribution of the network and find that it is of heterogeneity. Analyses of centrality measures reveal that some key economies, such as the United States, France, India and China, are always ranked the highest. We also detect 6 communities in the network, where economies in the same regional economy cooperative organizations tend to be classified in the same community. Intra-community flows account for 66.07% of total remittances, indicating that economies present the characteristic of regionalization. In addition, the results of the topological stability test show that GRN is fragile to node removal, particular the selective removal based on betweenness centrality.

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Retail investor attention and stock price crash risk: Evidence from China

Cited by 287 publications

References 62 publications

Discovering interlinkages between major cryptocurrencies using high-frequency data: new evidence from COVID-19 pandemic

Discovering interlinkages between major cryptocurrencies using high-frequency data: new evidence from COVID-19 pandemic

An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

Analysis of Global Remittance Based on Complex Networks

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