Positive Tone in Management Reports and Volatility of Stock Returns

This article aims to analyse the effects of positive tone in management reports on stock return volatility. It is expected that this article contributes to the literature about disclosure by proposing an objective textual content analysis of management reports, focussing on optimistic words or expressions employed by firms and their effect on stock return volatility. The sample consisted of management reports and financial data from 576 different Brazilian firms’ stocks. Regarding volatility, our measure is based on daily stock returns from 1 April 2011 to 23 October 2020. The data related to positive tone and control variables were based on the fiscal years 2010–2019. Therefore, the database contains 3,945 stock-year observations. The study hypothesis was tested through a regression model with panel data. The main results suggest that companies with higher positive disclosure tone scores do not necessarily present lower stock return volatility in the subsequent period. The objective content of financial reports (for example, in relation to profitability) seems to be related to stock volatility; however, the tone of subjective expressions does not represent the main determinant of stock volatility.

Modelling & Forecasting Volatility of Daily Stock Returns Using GARCH Models: Evidence from Dhaka Stock Exchange

Modelling volatility has become increasingly important in recent times for its diverse implications. The main purpose of this paper is to examine the performance of volatility modelling using different models and their forecasting accuracy for the returns of Dhaka Stock Exchange (DSE) under different error distribution assumptions. Using the daily closing price of DSE from the period 27 January 2013 to 06 November 2017, this analysis has been done using Generalized Autoregressive Conditional Heteroscedastic (GARCH), Asymmetric Power Autoregressive Conditional Heteroscedastic (APARCH), Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH), Threshold Generalized Autoregressive Conditional Heteroscedastic (TGARCH) and Integrated Generalized Autoregressive Conditional Heteroscedastic (IGARCH) models under both normal and student’s t error distribution. The study finds that ARMA (1,1)- TGARCH (1,1) is the most appropriate model for in-sample estimation accuracy under student’s t error distribution. The asymmetric effect captured by the parameter of ARMA (1,1) with TGARCH (1,1), APARCH (1,1) and EGARCH (1,1) models shows that negative shocks or bad news create more volatility than positive shocks or good news. The study also provides evidence that student’s t distribution for errors improves forecasting accuracy. With such an error distribution assumption, ARMA (1,1)-IGARCH (1,1) is considered the best for out-of-sample volatility forecasting.

Modelling & Forecasting Volatility of Daily Stock Returns Using GARCH Models: Evidence from Dhaka Stock Exchange

Modelling volatility has become increasingly important in recent times for its diverse implications. The main purpose of this paper is to examine the performance of volatility modelling using different models and their forecasting accuracy for the returns of Dhaka Stock Exchange (DSE) under different error distribution assumptions. Using the daily closing price of DSE from the period 27 January 2013 to 06 November 2017, this analysis has been done using Generalized Autoregressive Conditional Heteroscedastic (GARCH), Asymmetric Power Autoregressive Conditional Heteroscedastic (APARCH), Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH), Threshold Generalized Autoregressive Conditional Heteroscedastic (TGARCH) and Integrated Generalized Autoregressive Conditional Heteroscedastic (IGARCH) models under both normal and student’s t error distribution. The study finds that ARMA (1,1)- TGARCH (1,1) is the most appropriate model for in-sample estimation accuracy under student’s t error distribution. The asymmetric effect captured by the parameter of ARMA (1,1) with TGARCH (1,1), APARCH (1,1) and EGARCH (1,1) models shows that negative shocks or bad news create more volatility than positive shocks or good news. The study also provides evidence that student’s t distribution for errors improves forecasting accuracy. With such an error distribution assumption, ARMA (1,1)-IGARCH (1,1) is considered the best for out-of-sample volatility forecasting.

COVID-19 Pandemic, Stimulus Packages and Stock Returns in Vietnam

This paper investigates the impacts of COVID-19’s new cases and stimulus packages on the daily stock returns of five key economic sectors (Finance, Fast-moving-consumer-goods (FMCG), Healthcare, Oil and Gas, and Telecommunication) in Vietnam – one of the best countries in the world for handling COVID-19. The research team uses the Pool OLS method, with the panel data of 11 342 observations from 107 listed firms in these five sectors in the period January-June 2020. The key findings are (i) all sectors’ stock returns are negatively affected by daily new confirmed cases of COVID-19, the hardest hit is on the financial sector, followed by FMCG, healthcare, oil and gas, and telecommunications sectors. Vietnam did not have many affected cases, but low average income makes investors and consumers more careful and hesitate to spend/invest; (ii) in contrast to prior studies, stimulus packages did not accelerate the growth of stock returns in all sectors, with the order from most to least negatively affected: finance, oil and gas, telecommunication, healthcare, and FMCG. The slow implementation made investors skeptical of the growth potential of firms, they assess the stimulus packages as the signs of economic downturn. This fact leads to different recommendations for the Vietnamese Government in combating COVID-19.

Forecasting the conditional heteroscedasticity of stock returns usingasymmetric models based on empirical evidence from Eastern Europeancountries: Will there be an impact on other industries?

This empirical study investigates the leverage effect in six Eastern European countries under normal and non-normaldistribution densities for the sample period from January 2020 to August 2020. We find three countries, Bulgaria, CzechRepublic and Russia which are subject to ARCH effect whereas Poland, Romania and Hungary do not exhibit ARCHeffect in daily stock returns. Further, our study finds leverage effect, where past bad news affects is asymmetrical, pastnegative returns cause more volatility in current stock returns as compared to past positive returns, in three EasternEuropean countries. Based on the AIC and BIC model selection criteria we find that the non-normal student t-distributionand GED produce reliable estimates for Bulgaria, Czech Republic and Poland, respectively. The autocorrelation functionQ1 statistic confirms the insignificance of autocorrelation in residuals of TGARCH model. The impact of stock marketdynamics on other industries, such as pharmaceutical industry, textile and clothing industry, automotive industry issignificant, especially in the conditions of COVID-19 pandemic

FENOMENA ANOMALI MUSIMAN INDEKS HARGA SAHAM DI INDONESIA

This Research aims to obtain empirical evidence about the existence of anomaly seasonal effects on market returns on a daily and monthly basis on the IHSG and the LQ-45 Index in Indonesia throughout January 2015 untill September 2020. The diversity of arguments and research results regarding the phenomenon of seasonal anomalies in stock returns derived from previous studies make this phenomenon interesting to study. We analyze daily stock returns by using the Kruskal Wallis test, while the average monthly return is analyzed using the one-way Anova. The findings show that the phenomenon of stock anomaly returns according to the daily pattern of the week (day of the week effect) and the monthly pattern (month of the year effect) on IHSG and the LQ-45 Index are not proven within the range research from January 2015 to September 2020. The results of stock price forecasting provide benefits in supporting investors to develop their investment strategies. Futhermore, this information is also important to choose and determine which stocks should be bought and sold. In addition to investors, this information is also useful for management to monitor the pattern of stock price movements, so that they can plan, formulate strategies and take anticipatory steps based on possible threats that could arise.Keywords :Anomaly Seasonal Effect, day of the week effect, month of the year effect, market Return

Event studies with daily stock returns in Stata: Which command to use?

In this article, I provide an overview of existing community-contributed commands for executing event studies. I assess which command could have been used to conduct event studies that have appeared in the past 10 years in 3 leading accounting, finance, and management journals. The older command eventstudy provides a comfortable graphical user interface and good functionality for event studies that do not require hypotheses testing. The command estudy, described in Pacicco, Vena, and Venegoni (2018, Stata Journal 18: 461–476; 2021, Stata Journal 21: 141–151), provides a set of commonly applied test statistics and useful exporting routines to spreadsheet software and LATEX for event studies with a limited number of events. The most complete command in terms of available test statistics and benchmark models as well as its ability to handle events with insufficient data, thin trading, and large samples is eventstudy2.

Dependent structure and risk analysis of S&P 500 Index's continuously rising returns and continuously falling returns

PurposeThe stock market price time series can be divided into two processes: continuously rising and continuously falling. The authors can effectively prevent the stock market from crashing by accurately estimating the risk on continuously rising returns (CRR) and continuously falling returns (CFR).Design/methodology/approachThe authors add an exogenous variable into Log-autoregressive conditional duration (Log-ACD) model, and then apply our extended Log-ACD model and Archimedean copula to estimate the marginal distribution and conditional distribution of CRR and CFR. Plus, the authors analyze the conditional value at risk (CVaR) and present back-test results of the CVaR. The back-test shows that our proposed risk estimation method has a good estimation power for the risk of the CRR and CFR, especially the downside risk. In addition, the authors detect whether the dependent structure between the CRR and CFR changes using the change point test method.FindingsThe empirical results indicate that there is no change point here, suggesting that the results on the dependent structure and risk analysis mentioned above are stable. Therefore, major financial events will not affect the dependent structure here. This is consistent with the point that the CRR and CFR can be analyzed to obtain the trend of stock returns from a more macro perspective than daily stock returns scholars usually study.Practical implicationsThe risk estimation method of this paper is of great significance in understanding stock market risk and can provide corresponding valuable information for investment advisors and public policy regulators.Originality/valueThe authors defined a new stock returns, CRR and CFR, since it is difficult to analyze and predict the trend of stock returns according to daily stock returns because of the small autocorrelation among daily stock returns.

COVID-19’s Impacts on the Kuwaiti Stock Market’s Performance

This study investigates the impact of COVID-19 pandemic on daily stock returns in Kuwait Stock Market (KSE) over the period from 28 March to 20 April 2020. By applying the event study methodology (ESM) approach, the results reveal that the pandemic has positively impacted stocks of banks, consumer goods and telecommunications sectors. However, oil & gas, real estate, financial, basic materials, industrials, consumer services, and insurance stocks have been negatively impacted by the pandemic. The COVID-19 pandemic's most negatively affected are services and financial stocks. The cumulative average abnormal returns (CAAR) of all sectors were affected negatively by the COVID-19 pandemic.