Empirical distribution of daily stock returns of selected developing and emerging markets with application to financial risk management

Pekár, Juraj; Pčolár, Mário

doi:10.1007/s10100-021-00771-4

Cited by 15 publications

(6 citation statements)

References 23 publications

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“…Nowadays, with the rapid development of the Internet, more auxiliary information can be obtained through different channels. Research shows that making rational use of these auxiliary information and giving appropriate examples to communicate with users not only can improve the accuracy and credibility of a personalized recommendation system but also can greatly improve the possibility of recommending users to choose appropriate personalized recommended products by themselves and convenient and comprehensive supervision of product quality [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Data Mining Method of Intelligent Market Management Based on Collaborative Recommendation Algorithm

Wu¹,

Wang²,

Cai³

2022

Computational Intelligence and Neuroscience

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In order to solve the problems of low coverage and accuracy and large mean absolute error and root mean square error when traditional algorithms recommend market management data, this paper proposes an intelligent market management data mining method based on a collaborative recommendation algorithm. According to the preference value of the attribute characteristics of market management data, predict and score the attribute characteristics of market management data; use data mining technology to preprocess the information of market management data, combined with the design of collaborative filtering recommendation algorithm; and realize the collaborative filtering recommendation of market management data. With 50 recommendations, AGCAN improves the accuracy of MovieLens-1M by 43.81%, 5.43%, 1.87%, 0.42%, and 1.67%, respectively, compared with the five benchmark algorithms. For MovieLens-100K, compared with the five benchmark algorithms, AGCAN improves the accuracy by 51.17%, 10.52%, 3.37%, 0.1%, and 0.30%, respectively. Compared with the five benchmark algorithms, Amazon-baby and AGCAN have improved the accuracy by 34.37%, 28.12%, 31.25%, 29.1%, and 3.12%, respectively. The algorithm proposed in this paper uses a graph neural network to mine useful information between users and projects, but it lacks the use of other personalized interest information of users, such as user interest, user purchase time, and so on.

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Section: Introductionmentioning

confidence: 99%

Data Mining Method of Intelligent Market Management Based on Collaborative Recommendation Algorithm

Wu¹,

Wang²,

Cai³

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Similarly, Odhiambo et al (2020) modeled the returns from Nairobi Securities Exchange, where the lognormal distribution was seen better fitted than the normal distribution. Pekár and Pčolár (2022) found generalized skewed t-distribution for daily returns from 30 stock markets around the world. Hong (2022) also disclosed that the returns from the stocks and markets do not follow normality but an increase in sample size might increase the reliability of the normality test.…”

Section: Introductionmentioning

confidence: 97%

Testing Normality for Daily Returns from the Nepalese Stock Market

Vaidya

Sharma

Dangol

2022

JBMR

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Researchers and investors are interested in the normality of the return from the stock market and the establishment of the Efficient Market Hypothesis (EMH). Nevertheless, a statistical distribution of the normality of the return helps investors predict the stock market return with the help of the basic mean and standard deviation values of the return. Hence, the paper tested the normality of the daily returns from the Nepalese stock market, Nepal Stock Exchange Limited (NEPSE). The paper followed the statistical results and data visualization to determine the normality of the stock market return. The data visualization and statistical results have shown that the daily return from the NEPSE follows a normal distribution. The test statistics for the normality of the data also show a normal distribution for the NEPSE daily return. Similarly, the parameters for the fitted distribution also reflect normality. The daily transaction volume at the stock market normally leads the daily stock market return in normality as well. The fitting of a normal distribution for the daily returns reflects that the Nepalese investors could predict market risk and return using two statistical parameters, i.e., standard deviation and mean, respectively.

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“…Pekar and Pcolar built an analytical model of an arbitrarily structured fnancial network and empirically studied it. His fndings suggested that the fnancial system is a stable and fragile system whose contagion efect would be very common with less risk [6]. However, the research on fnancial risk of related models still pays too much attention to the impact of a single time, and lacks the discussion of spatial models.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Spatial Spillover Effect of Multidimensional Hybrid Financial Risk Contagion Based on the DAI Spatial Econometric Model

Dong

2023

Advances in Multimedia

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The process of global integration has accelerated, and the international financial market has become increasingly closely linked. The financial risks that come with them are becoming more complex and difficult to guard against. Multimedia modeling in Health Cloud biometric authentication and data management systems can be applied to the analysis of financial markets. Most of the current financial risk analysis models are based on a single time, and the models are relatively simple and cannot adapt to the current complex multidimensional mixed financial risk environment. Therefore, this paper aimed to analyze the spatial spillover effect of financial risk contagion based on the directed asymmetric (DAI) spatial econometric model. This paper proposed to transfer entropy information weight information and introduce the GARCH (generalized auto-regressive conditional heteroskedasticity) model to improve the traditional econometric model. By constructing a DAI measurement model, the spatial contagion of multidimensional mixed financial risks was analyzed, and on this basis, a generalized multidimensional economic space was established to analyze spatial spillover effects and analyze the specific path of spatial spillover effects. The model results in this paper showed that the degree of correlation between the stock and bond market varied greatly between countries. Among them, the change coefficient W s ⟶ b r s D of the event period was judged to have a large degree of negative change in the United Kingdom, Germany, and France in the European Union, which were −0.9885, −0.9876, and −0.9748, respectively. This showed that the model in this paper had a good and reliable ability to cope with the current multidimensional mixed complex financial risk environment and could be used as a reference for financial risk-related research. At the same time, it also proved that multimedia modeling in health cloud biometric authentication and data management system could provide a role in financial risk contagion analysis.

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Empirical distribution of daily stock returns of selected developing and emerging markets with application to financial risk management

Cited by 15 publications

References 23 publications

Data Mining Method of Intelligent Market Management Based on Collaborative Recommendation Algorithm

Data Mining Method of Intelligent Market Management Based on Collaborative Recommendation Algorithm

Testing Normality for Daily Returns from the Nepalese Stock Market

Evaluation of Spatial Spillover Effect of Multidimensional Hybrid Financial Risk Contagion Based on the DAI Spatial Econometric Model

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