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This paper studies the information spillover effects over price and volatility across countries by using open-to-close (daytime) returns and close-to-open (overnight) returns of NASDAQ 100 and KOSDAQ 50 index futures data from January 1, 2001 to December 31, 2001. Based on the time-varying AR(1)-GARCH (1,1)-M models, we document that statistically significant conditional mean and volatility spillover effects from the daytime returns of NASDAQ 100 index futures to both overnight returns and daytime returns of KOSDAQ 50 index futures were observed. We also find that there were information spillover effects from overnight returns of NASDAQ 100 index futures to daytime returns of KOSDAQ 50 index futures returns because investors in Korean stock markets can get information on U.S. stock market movement on real time basis due to the ECN transaction with its trading hour overlapped. Finally, we find that the daytime returns of KOSDAQ 50 index futures significantly influence the overnight and daytime returns of the NASDAQ 100 index futures.

This study aims to determine whether, by means of the application of genetic algorithms (GA) through the traditional technical analysis (TA) using moving average convergence/divergence (MACD), is possible to achieve higher yields than those that would be obtained using technical analysis investment strategies following a traditional approach (TA) and the buy and hold (B&H) strategy.

The study was carried out based on the daily price records of the NASDAQ financial asset during 2013–2017. TA approach was carried out under graphical analysis applying the standard MACD. GA approach took place by chromosome encoding, fitness evaluation and genetic operators. Traditional genetic operators (i.e. crossover and mutation) were adopted as based on the chromosome customization and fitness evaluation. The chromosome encoding stage used MACD to represent the genes of each chromosome to encode the parameters of MACD in a chromosome. For each chromosome, buy and sell indexes of the strategy were considered. Fitness evaluation served to defining the evaluation strategy of the chromosomes in the population according to the fitness function using the returns gained in each chromosome.

The paper provides empirical-theoretical insights about the effectiveness of GA to overcome the investment strategies based on MACD and B&H by achieving 5 and 11% higher returns per year, respectively. GA-based approach was additionally capable of improving the return-to-risk ratio of the investment.

Limitations deal with the fact that the study was carried out on US markets conditions and data which hamper its application in some extend to markets with not as much development.

The findings suggest that not only skilled but also amateur investors may opt for investment strategies based on GA aiming at refining profitable financial signals to their advantage.

This paper looks at machine learning as an up-to-date tool with great potential for increasing effectiveness in profits when applied into TA investment approaches using MACD in well-developed stock markets.

The prediction of stock market (SM) indices is a fascinating task. An in-depth analysis in this field can provide valuable information to investors, traders and policy makers in attractive SMs. This article aims to apply a correlation feature selection model to identify important technical indicators (TIs), which are combined with multiple deep learning (DL) algorithms for forecasting SM indices.

The methodology involves using a correlation feature selection model to select the most relevant features. These features are then used to predict the fluctuations of six markets using various DL algorithms, and the results are compared with predictions made using all features by using a range of performance measures.

The experimental results show that the combination of TIs selected through correlation and Artificial Neural Network (ANN) provides good results in the MADEX market. The combination of selected indicators and Convolutional Neural Network (CNN) in the NASDAQ 100 market outperforms all other combinations of variables and models. In other markets, the combination of all variables with ANN provides the best results.

This article makes several significant contributions, including the use of a correlation feature selection model to select pertinent variables, comparison between multiple DL algorithms (ANN, CNN and Long-Short-Term Memory (LSTM)), combining selected variables with algorithms to improve predictions, evaluation of the suggested model on six datasets (MASI, MADEX, FTSE 100, SP500, NASDAQ 100 and EGX 30) and application of various performance measures (Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error(RMSE), Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE)).

This paper aims to provide swift feedback to readers and investors on the early effect of novel coronavirus (COVID-19) pandemic outbreak on tourism industry.

Three leading consolidators of hotel accommodations, airline tickets and travel services in the tourism industry around the globe, namely, Booking Holdings Inc., Expedia Group and Trip.com Group Ltd. are chosen in this study. First, numerical description is performed on their shares prices and a set of control variables to compare their performances before and during the lockdown because of COVID-19 outbreak. Next, this paper estimates ordinary least squares models with and without exponential generalized autoregressive conditional heteroskedastic specification to establish the nature, significance and magnitude of the pandemic’s early effect on the shares performance of these online travel companies (OTCs).

This paper discovers a rapid decline in the performance of tourism industry amid the pandemic outbreak, from the perspective of three leading OTCs, which derive their profits from tourists by providing them online hotel reservation, air-ticketing and packaged-tour business services around the globe. These significant adverse direct and indirect effects testify that tourism-related businesses are extensively locked down by the pandemic outbreak.

Future studies are encouraged to examine each of the tourism sectors for individual effects.

This paper provides implications for investors to protect their wealth, and for policymakers to ensure sustainability of tourism industry in the pandemic outbreak and in the future.

From the perspective of corporate finance, this paper empirically quantifies the early effect of COVID-19 on tourism industry for a quick snapshot.

The use of social media tools by companies is common, but the links between the use of multiple social media tools by companies and stock price changes are largely unknown. Therefore, this study aims to analyze the value-relevance of social media activities on Facebook (FB), Instagram (IG), LinkedIn (LI), Twitter (TW) and YouTube (YT).

Stock market data and hand-picked social media data in this study were collected from Finland, a small language area with consistent International Financial Reporting Standards (IFRS) reporting practices, in the expectation of better comparability and lower noise in the data.This study uses correlation, regression and factor analyses for a sample of 105 Finnish public limited companies listed on the Nasdaq Helsinki stock exchange.

This paper finds evidence that social media activity is an important area of analysis and that the activity and popularity of a company in social media are value-relevant variables in forecasting stock prices.

Not all social media activities are necessarily equally important for managers and investors. Focus on visual messages in social media is recommended.

The findings of this study highlight the value-relevance of using multiple visual social media channels, particularly IG and YT. This paper suggests avenues for future research and for analyzing social media information.

The purpose of this study is to examine empirically the conditional correlation between the major US indices (S&P500 index and Dow Jones Industrial index) and three selected meat commodities as: Feeder Cattle, Leen Hogs and Live Cattle during the period from July 22, 2010 to June 30, 2017.

In this study, the authors use for the first time the GARCH-DECO (1,1) to examine empirically the conditional nexus between the major US indices (S&P500 index and Dow Jones Industrial index) and three selected meat commodities as; Feeder Cattle, Leen Hogs and Live Cattle during the period from July 22, 2010 to June 30, 2017.

From the empirical findings, the authors conclude the existence of a highly significance of conditional heteroscedasticity parameters can demonstrate us to distinguish the nature of the volatility dependency between S&P500 index and Dow Jones Industrial index and three selected meat commodities indices.

This can find clear the significance of relationship in the process of financialization of the major US index and meat commodities indices in the case of this paper.

The purpose of this article is to present the results of empirical research concerning the identification of the impact of the transfer of companies from the alternative market to the regulated market of the Warsaw Stock Exchange on their operating and net performance.

The study was conducted based on the empirical data of the companies that changed the listing place on the Warsaw Stock Exchange. Data regarding the years before the transfer were collected from the prospectuses of companies prepared mandatorily in connection with the transition to the regulated market. Data regarding the years of the event and subsequent years were obtained from companies' annual reports. As in other studies in the analysis, the operational metrics were used (operating return on sale, operating return on assets, total asset turnover), which was further extended to net profitability ratios (net return on ale, net return on asset, net return on equity). The significance analysis was based on the Student's t-test and Wilcoxon’s test.

The results show that before the transfer from the alternative market to the regulated market, companies improved financial performance. As a result of the change of listing venues, the results already collapsed in the year of the event. The downward trend continued in the following two years, with a noticeable improvement in the third year after the transfer.

The literature lacks such studies based on the Polish market. To the best knowledge of the author, this is one of the first studies in Poland showing the changes in operating and net performance of companies changing the stock listing venues. The research is based on a large group including all companies that have changed listing venues since the beginning of the alternative market in Poland. The article presents an original empirical result that can be used both by managers and investors in their decisions.

The study uses the multivariate GARCH-BEKK model (which was first proposed by Baba et al. (1990) and then further developed by Engle and Kroner (1995)) to examine the return and volatility spillover between India and four leading Asian (namely, China, Japan, Singapore and Hong Kong) and two global (namely, the United Kingdom and the United States) equity markets.

The study employs a multivariate GARCH-BEKK model to quantify return correlation and volatility transmission across the pre- and post-2008 global financial crisis periods (apart from other conventional time series modelling like cointegration, Granger causality using vector error correction model (VECM)).

The results show a tendency of the Indian stock market index to move along with the US and Hong Kong market indices. The decrease in the value of the co-integration coefficient during the recession was explained by reduced investor confidence in developing countries. The result further shows a clear distinction in terms of volatility spillover between the Asian market vis-a-vis US and UK markets. Volatility transmission from India to Asian markets was found to be significantly higher as compared to the US and UK. So also, the study’s results show a puzzling result giving us comparable co-integration ranks for phase 2 (expansion) and phase 3 (slow-down) of the business cycle in most cases.

In Granger causality testing, the results were unable to ascertain the difference between phase 2 (expansion) and phase 3 (slowdown). However, the multivariate GARCH (MGARCH)-BEKK model showed a clear reduction in volatility transmission to NIFTY50 (is the flagship index on the National Stock Exchange of India Ltd. (NSE)) as India entered slow-down. This shows that the Indian economy does go through different business cycles, and the changes in parameters hence prove hypothesis 3 to be true with respect to volatility transmission to India from International markets.

The results show that for all countries, the volatility transmitted to India increases significantly going from phase 1 (recession) to phase 2 (expansion) and reduces again once the countries enter slow-down in phase 3 (slowdown). This shows that during expansion shocks and impulses in international markets affect the Indian markets significantly, supporting the increase in co-integration in phase 2 (expansion). During expansion, developing markets like India become profitable for investors, due to the high growth rate when compared to developed countries. This implies that a significant amount of capital enters Indian markets, which is susceptible to the volatility of international markets. The volatility transmission from India to the US and UK was insignificant in phase 1 (recession and recovery) and phase 3 (slow-down) showing a weak linkage between the markets during volatile time periods.

The purpose of this paper is to study whether switching trading venues create value in the Hong Kong stock market.

By using an event study, the paper investigates the abnormal returns (AR) earned by firms in the Growth Enterprise Market (GEM) relating to switching to the Main Board (MB). Two measures, turnover of the stock and Amihud’s (2002) illiquidity ratio, are used to examine the liquidity effects.

The switch is accompanied by a long-term increase in stock price for low liquidity firms only. High liquidity firms underperform with persistent negative excess returns after switching, while the transient negative excess returns in low liquidity firms reverse gradually. The results further show a significant increase in trading activity for low liquidity firms following the switch, while there is a significant decline in both trading activity and liquidity in firms with high liquidity. The overall results suggest that moving from GEM to the MB is beneficial to low liquidity firms but detrimental to high liquidity firms.

This study is the first to investigate whether moving from GEM to the MB creates value in the Hong Kong stock market.

The purpose of this paper is to research how corporate communication regarding a specific corporate event (i.e. Tesla’s tweets about a new product) as well as the framing of both the event itself and the market reactions therewith in the news media influence the formation of the share price of the respective company over time. In so doing, the study provides insights into the nature of market-moving information and the role of financial news flows in shaping market reactions in today’s high-frequency news and information environment.

Using a multi-method case study approach, combining quantitative intraday event studies with a qualitative text analysis of financial online news and tweets by Elon Musk and Twitter, the authors shed light on the complex interaction between market events, financial information and stock market reactions. The analysis covers a period of four days, encompassing the announcement and introduction of the new battery pack for Model S and X by Tesla as well as the accompanying and follow-up reporting by the financial news media.

Findings show that market reactions are driven by business events and expectations among the market rather than the follow-up reporting by financial news media. Financial online news instead seems to heavily rely on Elon Musk’s attention-triggering news to sustain its 24-h airtime with a variety of reporting tools, keeping the highly demanded audience engaged. Eventually, Twitter accounts of media visible companies and personalities, such as Tesla and its CEO Elon Musk, have been found to be useful market information sources for day traders and shareholders to trade at a profit.

The study is a response to recent discussions about the legitimacy of Twitter communication by CEOs or representatives of listed companies. The findings show that Twitter communication needs to be well considered in light of strict market regulations (e.g. SEC in the USA) regarding insider-trading and the publication of market-relevant information. In addition, corporate financial communication should avoid impetuous communication via social media channels as this could have deterrent effects on the market valuation of a listed company.