What threatens stock markets more - The coronavirus or the hype around it?

Abstract: This introductory article lays the groundwork for the special issue on "Social Media and Society in Indonesia." It examines the co-constitutive relationship between social media and Indonesian society, focusing on their mutual shaping within enduring socio-cultural dynamics. The article calls for a nuanced understanding of how technologies are localized and how diverse "social media publics" emerge in the Indonesian context. It begins by tracing the social and material histories of Indonesian social media and subsequently explores three key themes that emerge from the curated collection: the business and labor of cultivating social media publics; social media's political publics; and social media's religious publics. Challenging dominant narratives of social media as a homogenous phenomenon, the article positions Indonesia as a critical lens for analyzing and reimagining global social media discourse.

We investigate the interdependence between U.S., Japan, China and Korea stock markets using daily return data covered from September 14, 2005 to September 30, 2011. To effectively study the short-term information transmission among 4 major stock markets we separated the whole sample period into two sub-sample period, before and after 2007 financial crisis. For this purpose we introduced the Granger causality test and variance decomposition analysis based on VECM(vector error correction model). The main results of empirical tests are as follows;First, we test the stationarity of 4 countries’ stock market index using ADF and PP model. According to the empirical results we find that there are unit roots in the level variables of 4 countries’ stock market data but not in the returns data of the 4 countries stock market both before and after financial crisis. Second, we also try to estimate the long-run relationship among S&P500, Nikkei 225, Shanghai stock index and KOSPI 200 stock index. For this we introduce the Johansen co-integration model and come to the conclusion that there is a long-run relationship among the level variables of four national stock markets. Third, in order to the short-term information transmission mechanism among the four national stock market index, we employ the Granger causality test based on the VECM(3). According to the empirical test, we find that during the whole sample period, S&P 500 and Nikkei225 stock index have an impact on the KOSPI 200 stock index in the statistically significant level but there is no information transmission effect from Shanghai stock market to Korea stock market. The impact of S&P500 stock index on the KOSPI200 stock index is relatively greater than that of Nikkei225 stock index on the KOSPI200 stock index. Fourth, in case of the empirical result on before and after financial crisis period, we find that the interdependence among the four national stock markets after financial crisis period is much greater than that of before financial crisis period. Fifth, we also compare the influence of US and Japan stock market on China stock market and we find that the US stock market has an greater impact on China stock market than Japan stock market. we also find that after financial crisis of 2008 the comovements between four stock markets are greater than before financial crisis. After the financial crisis the impact of China stock market to other countries is increased but that of Japan is decreased. Sixth, in case of information transmission US and Japan stock markets, there is a feed information transmission between the two national stock market but the influence of US stock market on other countries' stock is more dominant since the financial crisis. The empirical results by variance decomposition analysis are consistent with those of Granger causality test. From these empirical results, we infer that market integration among stock markets is increasing over time and these empirical results are informative to stock market investors and regulators to set up a investment strategies and government policy.

The paper investigates the asymmetric volatility effect of five major cryptocur- rencies and their bilateral linkages with major indices in the Indian stock market. To inves- tigate the bilateral relationship of crypto currency with stock indices, researchers used two major stock indices in the Indian stock market namely, BSE Sensex and NSE Nifty. The study used GARCH, EGARCH and TGARCH models, asymmetric to model the asymmetric volatility effect in the conditional volatility of crypto currencies and stock indices. Johansen’s cointegration and Vector Error Correction Model is used for examining the presence of cointe- gration between selected variables and to analyse the strength of causality among them. The study finds the evidence of cointegration between cryptocurrencies and stock market indices, implying that cryptocurrencies are related to stock indices. Further there is unidirectional relationship among stock and crypto market and crypto currencies having short-lived re- sponse to shocks in stock markets. Even with these currencies’ explosive growth, there are still not many research examining their connection to stock markets. This study will help investor’s those who making investment in currency market or in the stock market to eval- uate the pattern of volatility, interconnection among them, so that they can make crucial investment decisions and diversification strategies. This will help them to gain knowledge about how these two markets move together so they may avoid underestimating risk when building portfolios that contain both kinds of assets.

In the current technological driven society, the role and impact of social media has versatile significant in different fields / sectors at gross in all over the world. The impact of social media on stock markets of India as well as world is one of these fields / sectors. The social media impact can be measured by no of persons using social media and how many of them are interested in stock market. The increase rate in use of internet leads the popularity of social media. Social Media is one of the important media which can connect the each and every people of India as well as whole World. Today stock market and the investors are not restricted to geographical boundaries of a country. Today, we can say the whole world is market as well as a stock market. It is only possible for the development of technology and use of technology by the most of the people of the world. A lot study has been undertaken by various researchers, academician, and scholars on the impact of social media on stock market in India as well as the whole World. But, no remarkable study has been undertaken on the impact of social media on stock market of Odisha. So, this is an attempt to study impact of social media on stock market of India and potential investors of Odisha. The present study based on both primary and secondary data. The primary data has been collected through a well-defined questionnaire which is designed for this specific purpose. The secondary data have collected through a well-designed strategy, and these have been collected from various e-journals, e-magazines, e-annual reports of companies, and various reputed websites. There are various statistical tools, i.e., percentage calculations; correlation, and chi-square test have used for analysis and interpretation of results. The present study concluded that the social media has vital role and impact on stock market. The social media is also helping stock market and investors in their trading in the current market scenario.

In today's technologically-driven society, social media plays a multifaceted and significant role across various fields and sectors worldwide. One such domain where its impact is noteworthy is the stock markets, both in India and globally. The influence of social media on stock markets can be gauged by the number of individuals engaging with these platforms and their interest in stock market activities. The escalating use of the internet has further propelled the popularity of social media. Social media serves as a crucial medium connecting people not only within India but across the entire world. The dynamics of the stock market have transcended geographical boundaries, making it a global marketplace accessible to investors worldwide. This interconnectedness is made possible through the widespread adoption and utilization of technology by a large portion of the global population. While numerous studies have explored the impact of social media on stock markets in India and globally, there is a noticeable gap in understanding its influence on the stock market in Odisha. This study seeks to fill this gap by examining the impact of social media on the stock market in India and its potential influence on investors in Odisha. The research utilizes both primary and secondary data sources, with a structured questionnaire designed for primary data collection. Secondary data is obtained from various electronic journals, magazines, annual reports of companies, and reputable websites. Statistical tools such as percentage calculations, correlation, and chi-square tests are employed for analysis and interpretation. The findings of the study affirm that social media plays a pivotal role in shaping and impacting the stock market. It emerges as a valuable tool aiding stock market participants and investors in navigating the complexities of the current market scenario.

The stock market-inflation nexus has been the focus of academic and policy research for decades, with perplexing results. Yet the traditional linear time series methods applied in earlier studies ignored the influence of asymmetry adjustment and might have led to an inaccurate assessment. Consequently, this study intends to reduce the gap by examining the asymmetric influence of the consumer price index on the stock index using Malaysian data from December 1993 through June 2022 and the threshold autoregressive and momentum threshold autoregressive models. By using the stock market index and bank sector index, the results of the threshold autoregressive model provide evidence that supports the asymmetric adjustment process of the long-run stock index and consumer price index. We discovered that rising CPI drives up the stock index, but the rates of adjustment back to equilibrium were insignificant. On the other hand, both changes in stock market and bank indices respond to negative deviations from the long-run equilibrium, with 9.24% and 8.42% of a unit negative change from the long-run changes in the consumer price index, respectively. The study's findings indicate that investing in equities does not shield investors from rising inflation. The results suggest that when inflation changes, it is critical for investors to take different policy responses into account.

Quantitative estimates show that India's nuclear tests caused important economic damage to India and its neighbors, Pakistan and China. Pakistan's tests caused further economic damage to all three countries. In response to India's tests, India's stock market fell by 7.26 percent, Pakistan's by 10.59 percent and China's by 2.70 percent. In response to Pakistan's tests, India's stock market fell by another 5.57 percent, Pakistan's by 16.82 percent and China's by 3.93 percent. Overall, the two countries' tests caused India's stock market to fall by 12.83 percent, Pakistan's by 27.41 percent and China's by 6.63 percent. Some argue that going nuclear increased India and Pakistan's national security and their international political standing. In the financial markets' opinion, these tests caused major reductions in both countries' economic security, and harmed China's economic security. The tests had no important effects on the Group of Ten countries' stock markets; thus, the tests' economic effects seem for the moment of be confined to southern Asia.

We study the dynamics of stock market volatility and retail investors' attention to the stock market. The latter is measured by internet search queries related to the leading stock market index. We find a strong co‐movement of the Dow Jones' realised volatility and the volume of search queries for its name. Furthermore, search queries Granger‐cause volatility: a heightened number of searches today is followed by an increase in volatility tomorrow. Including search queries in autoregressive models of realised volatility improves volatility forecasts in‐sample, out‐of‐sample, for different forecasting horizons, and in particular in high‐volatility phases.

The epidemic of COVID-19 has swept the world, which has had a very serious impact on the social economy. The stock market, as a barometer of the economy, has also been hit. This paper selects the stock indexes of Britain, Germany and France as the research object to explore the influence of COVID-19 on the stock index. The results show that the European stock market will fluctuate several days before the epidemic, and the volatility of the stock market is an early warning for the outbreak of COVID-19. The specific days of early warning for COVID-19 in stock markets of various countries are not quite the same. In Britain, the number of early warning days is 14 days, and the number of daily new confirmed cases of COVID-19 is strongly related to the country's stock market index. France's early warning days are 7 days, and the number of daily newly diagnosed COVID-19 is weakly related to the country's stock market index. Germany's early warning days are 5 days, and the daily new number of COVID-19 's confirmed cases is strongly related to the country's stock market index.

The purpose of the study had been aimed to further the understanding in exploring the relevance performance of the monopoly stock of Tenaga Nasional Berhad (TNB) to assess the stock performance return in comparison against the Malaysian stock market with reference towards the measurement of the Kuala Lumpur Stock Exchange (KLSE) market index performance as the benchmark. With reference to the previous studies, there is relevance support to identify the tendency of the findings to suggest the higher performance for the major stocks like TNB stock where the business model of TNB being monopolizing the industry creating the upper hand for the stability in driving the revenue and profit leading to higher value in the stock price. The methodology of the research had further the quantitative analysis study using the historical data of 10years from 2014 to 2023 to identify the potential pattern and trend to assess the comparison for the performance and trading trend for both the TNB stock and market index of KLSE. The outcome of the research had suggested the sufficient evidence to identify the higher average return for the TNB stock over the negative return average being achieved by the KLSE market index putting clear picture on the higher performance of the monopoly TNB stock. In addition, the growth of the trading volume trend had suggested that the investors are being higher confidence towards the growth of the TNB stock where the growth of the trading volume for TNB stock is higher than the trading volume for KLSE market index and even exceeding the average return for the TNB stock. 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This study explores the factors that take hold on prices of stock which represent financial and real sector of our economy. These factors can be of any type like gold price, money supply, election, monetary policy, political change, investor sentiment, social media, unemployment, psychology, air quality, exchange rate, financial institutional investor, commodity channel index, consumer price index, domestic institutional investor, economic policy uncertainty, net profit margin, debt equity ratio, gross profit ratio and Morgan Stanley capital international in India. The study also reveals the relation between the factors with respect to change in stock prices. Study also determined whether there is any relation of income, education and investment with respect to stock market in India. Which factors affect the most to stock market in India? Study has done factor analysis of each factor with respect to Stock Market. The study reveals that there is relation among the factors affecting stock market in India. In thi study, ANOVA used to calculate the F value and it was founded that F value is greater than table or critic value. Hence null hypothesis is rejected.

The overarching goal is to help Indian investors better manage overseas risks and add to the existing body of knowledge. Company that can adapt to new circumstances. One indicator of India's economic strength is the country's stock market's integration with global stock markets. By analyzing the triggers and subsequent impacts of these occurrences. To predict how the Nifty50 would move in relation to the global market indexes, the variance decomposition approach is used. Because of its rapid economic development and improved access to international financial markets, India's stock market has been very unpredictable throughout the last several decades. The Indian economy faces several risks, despite the fact that integration has made things more flexible and maybe helpful. We call attention to three problems. For example, when the Indian economy is faltering, stock returns fall sharply, but they slowly recover when things improve. According to Report, factors like as FIls, market sentiment, and investor behavior are responsible for these reactions. We also have evidence indicating the Indian market is still somewhat isolated, even if it has become more connected to global markets. Not with standing limitations at the federal, state, or regional levels or maybe shocks that are sector-specific or global in scope? This study's findings highlight the significance of investors' and politicians' familiarity with the interrelated nature of the world's financial institutions. Knowing the impact and transmission of global shocks on India's stock market is crucial for achieving financial stability, making sound investment choices, and developing an effective risk management plan.

Stock market prediction has long been a pursuit of investors seeking to gain insights into the future trajectory of stock prices. Traditionally, analysts relied on fundamental analysis, technical indicators, and market sentiment to forecast stock movements. However, with the advent of machine learning (ML) and artificial intelligence (AI), predictive analytics in the stock market has seen a significant shift. ML algorithms offer the advantage of processing vast amounts of data and identifying complex patterns that might elude human analysts. In the context of stock market prediction, these algorithms can analyze historical stock prices, trading volumes, market indices, news sentiment, and various other factors to generate forecasts. One notable trend in this domain is the utilization of current stock market indices as input features for ML models. By training on historical data of these indices and their corresponding effects on individual stock prices, algorithms can learn to make predictions based on the current state of the market. One common approach is to use techniques like regression, time series analysis, or deep learning models such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). These models can capture temporal dependencies and nonlinear relationships in the data, allowing for more accurate predictions. Additionally, ensemble methods like random forests or gradient boosting can be employed to combine the strengths of multiple models for enhanced forecasting performance. Keywords: Stock market prediction,, financial stocks, stock market indices

(Study on the Impact of Us Stock Market's Shock Due to Global Financial Crisis on Korean Financial Markets)

Purpose This study aims to provide preliminary information to the investor by determining which indices co-movement, with the data mining method. Design/methodology/approach In this context, data sets containing daily opening and closing prices between 2001 and 2019 have been created for 11 stock market indexes in the world. The association rule algorithm, one of the data mining techniques, is used in the analysis of the data. Findings It is observed that the US stock market indices take part in the highest confidence levels between association rules. The XU100 stock index co-movement with both the European stock market indices and the US stock indices. In addition, the Hang Seng Index (HSI) (Hong Kong) takes part in the association rules of all stock market indices. Originality/value The important issue for data sets is that the opening/closing values of the same day or the previous day are taken into account according to the open or closed status of other stock market indices by taking the opening time of the stock exchange index to be created. Therefore, data sets are arranged for each stock market index, separately. As a result of this data set arranging process, it is possible to find out co-movements of the stock market indexes. It is proof that the world stock indices have co-movement, and this continues as a cycle.