Stock price network autoregressive model with application to stock market turbulence

Khoojine, Arash Sioofy; Han, Dong

doi:10.1140/epjb/e2020-100419-9

Cited by 12 publications

(3 citation statements)

References 41 publications

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“…On the basis of this condition, it is expected that in the event of a rebound, the recovery will be faster. It was also found that stocks that suffered major declines/losses were stocks of capital-intensive companies that relied on leverage [19,20]. The losses are of greater due to the transmission of Covid-19 handling larger disbanding other types of companies.…”

Section: Discussionmentioning

confidence: 99%

Impact of COVID-19 News on Performance of Indonesia Stock Market

Haryanto¹,

Mawardi²

2021

ujaf

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This research tries to show that information about COVID-19 affects market arousal indicated by the frequency of transactions, and the market performance shown by Jakarta Composite Index (JCI). The theory used for analysis is the prospect theory and efficient market hypothesis (EMH). The results of statistical analysis indicate that information about COVID has a negative effect on JCI, as well as trading volume the previous day. The evidence can briefly prove that there is an effect of COVID-19 and weakening daily transactions on JCI. The research findings show that the JCI market uncertainty is in line with the VUCA and Prospect theory. In this case, it occurs that uncertainty affects the behavior of investors' decision making. Investors' decision-making behavior is accumulated in market behavior, and is subsequently manifested in index changes in accordance with the efficient market hypothesis. The contribution of this research to the study of financial market behavior is that uncertainty and uncertainty faced by investors affect market behavior and changes as measured by the index.

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Section: Discussionmentioning

confidence: 99%

Impact of COVID-19 News on Performance of Indonesia Stock Market

Haryanto¹,

Mawardi²

2021

ujaf

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“…Therefore, α 2 s i(t−1) is not incorporated into the first term. It can be proved that S t has a stationary property (for more details see [31]). To estimate α = (α 0 , α 1 , α 2 ), maximum likelihood estimation (MLE) is also used as follows:…”

Section: Nari Modelmentioning

confidence: 99%

Network Autoregressive Model for the Prediction of COVID-19 Considering the Disease Interaction in Neighboring Countries

Khoojine

Shadabfar

Hosseini

et al. 2021

Entropy

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Predicting the way diseases spread in different societies has been thus far documented as one of the most important tools for control strategies and policy-making during a pandemic. This study is to propose a network autoregressive (NAR) model to forecast the number of total currently infected cases with coronavirus disease 2019 (COVID-19) in Iran until the end of December 2021 in view of the disease interactions within the neighboring countries in the region. For this purpose, the COVID-19 data were initially collected for seven regional nations, including Iran, Turkey, Iraq, Azerbaijan, Armenia, Afghanistan, and Pakistan. Thenceforth, a network was established over these countries, and the correlation of the disease data was calculated. Upon introducing the main structure of the NAR model, a mathematical platform was subsequently provided to further incorporate the correlation matrix into the prediction process. In addition, the maximum likelihood estimation (MLE) was utilized to determine the model parameters and optimize the forecasting accuracy. Thereafter, the number of infected cases up to December 2021 in Iran was predicted by importing the correlation matrix into the NAR model formed to observe the impact of the disease interactions in the neighboring countries. In addition, the autoregressive integrated moving average (ARIMA) was used as a benchmark to compare and validate the NAR model outcomes. The results reveal that COVID-19 data in Iran have passed the fifth peak and continue on a downward trend to bring the number of total currently infected cases below 480,000 by the end of 2021. Additionally, 20%, 50%, 80% and 95% quantiles are provided along with the point estimation to model the uncertainty in the forecast.

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“…ese studies show that not only the information in the financial market has an important predictive value for stock price fluctuation but also the media sentiment and government supervision strategy based on the information have an important impact on stock price fluctuation. At the same time, in view of the randomness and periodicity of the stock market fluctuation and the speculative nature of the investors in the stock market, the stock price is vulnerable to fluctuation due to various uncertain factors [4][5][6]. However, once the stock price fluctuates abnormally, the stock price fluctuation risk may be induced [7,8].…”

Section: Introductionmentioning

confidence: 99%

Media Sentiment, Government Supervision Strategy, and Stock Price Fluctuation Risk

Ju-fang

2021

Discrete Dynamics in Nature and Society

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From the cross perspective of communication science and administration management, based on complex network theory, this paper constructs a model of stock price fluctuation risk contagion, which comprehensively considers media sentiment and government supervision strategy, and deeply analyzes the contagion mechanism of stock price fluctuation risk under the interaction of media sentiment and government supervision strategy. The main conclusions are as follows: The stock association network established by random way is more likely to cause contagion of stock price fluctuation risk. Media sentiment tendency, media sentiment intensity, and media attention persistence have positive “U” relationship, inverted “U” relationship, and positive correlation with contagion intensity of stock price fluctuation risk, respectively. There is a negative correlation between the strength, persistence, and timeliness of government supervision and the contagion intensity of stock price fluctuation risk. There is a positive correlation between market noise and contagion intensity of stock price fluctuation risk, and market noise has a restraining effect on media sentiment and government supervision strategy. In addition, the stock price fluctuation risk is inherent risk in the stock market, which cannot be eliminated by adjusting media sentiment and government supervision strategy, but its contagion intensity can be effectively controlled.

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Stock price network autoregressive model with application to stock market turbulence

Cited by 12 publications

References 41 publications

Impact of COVID-19 News on Performance of Indonesia Stock Market

Impact of COVID-19 News on Performance of Indonesia Stock Market

Network Autoregressive Model for the Prediction of COVID-19 Considering the Disease Interaction in Neighboring Countries

Media Sentiment, Government Supervision Strategy, and Stock Price Fluctuation Risk

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