Effect Of Bad News Research Articles

Fake news text detection based on convolutional neural network

The swift evolution of mobile devices and multimedia technology has made the Internet one of the primary means of learning new information today. However, the huge amount of news information is often mixed with erroneous fake news, which can cause bad news events to spread and trigger people's bad emotions, putting the healthy development of society and economy at risk. Addressing the real-world application problem of swiftly and accurately detecting fake news is imperative. To mitigate the aforementioned challenges, we propose a method that uses deep learning to detect fake news and validate it through empirical studies. We begin by collecting a sizeable fake news dataset from domestic social media platforms and use a pre-trained deep learning model to extract textual features. Furthermore, we amalgamate convolutional neural networks and deep learning models to effectively glean and encompass the patterns and attributes of disinformation through an analysis of the text's semantic and structural characteristics. Finally, we experimentally evaluate the effectiveness of the method. The experimental findings demonstrate that the suggested approach exhibits commendable performance in the task of detecting fake news, effectively discerning between authentic and fabricated information. Our deep learning-based approach proves to be both efficient and highly impactful in addressing the issue of fake news within the realm of social media.

Do investors prefer multiple small bad news events or a single big one? Evidence from the Chinese stock market

This paper focuses on the relation between the hedonic editing hypothesis and the stock market. Based on the data of China's stock market from 2014 to 2020 and the utilization of an exogenous shock arising from the “New Regulation of Insider Selling”, we find that investors will respond more pessimistically to the negative information that is on the same event but released multiple times. This result provides new evidence for the hedonic editing hypothesis to be tenable in the capital market.

Effects of Bad News on Stock Returns and Analysts’ Recommendations: The Influence of Executive Gender

Although women are more conservative and more ethical than men, the proportion of female executives is still lower than that of men. Both Chief Executive Officers (CEOs) and Chief Financial Officers (CFOs) have strong influences in making corporate financial decisions. However, most of the literature focuses only on CEOs and ignores CFOs. Therefore, when bad news happens, it is possible that a female CEO/CFO can better alleviate the negative impact. We thus employ 4,405 firm-year observations over the period of 1996–2018, of which 680 are unique companies listed on the S&P1500 index, to examine how CEO and CFO gender influences stock returns and analyst recommendations. Our ordinary least squares and logistic regression results show that investors are pessimistic about companies led by female CEOs/CFOs, especially female CEOs. That is, when something bad happens, stock returns and analyst recommendations are worse for firms with female CEOs. Overall, this study is first to use stock returns to observe market reactions to firms with female CEOs/CFOs. In other words, the corporate remains unfriendly toward women, even those who are qualified as CEOs/CFOs

Post-FOMC Drift

We study the patterns of stock returns around the Federal Reserve monetary policy announcements. Much of the existing literature interprets changes in short rates around the announcement windows as policy surprises. In contrast, we follow the “Fed information effect” literature, which posits that financial markets react to central bank announcements not just for unexpected changes in monetary policy stances (monetary policy news), but also for central bank’s assessment of economic conditions (non-monetary policy news). We identify the good/bad news using a combination of sign restrictions with high-frequency financial data. “Bad news” events are times when the market interpreted the Fed decisions/announcements as revealing negative Fed information about the economy, and vice versa for “good news” events. A novel finding is that following bad news events, we observe significantly positive stock returns in a 20-day period. This observation is largely consistent with a story of asymmetric effects of good and bad news on the level of uncertainty. Further analysis shows that the post-FOMC drift to economic news in Fed announcements is a market-wide phenomenon. A trading strategy that buys following “bad news” earns an excess return of 2.5% per year with a Sharpe ratio of 0.43.

Price extremes and asymmetric dependence structures in stock returns: the emerging market evidence

PurposeEquity research in experimental psychology reveals investors' overreactions to bad news events. This study of asymmetric price structures in equity markets investigates whether such behavior predicts stock returns in an emerging market of India.Design/methodology/approachThe research decomposes Bombay Stock Exchange (BSE) Sensex returns into Extremely Positive Returns (EPR) and Extremely Negative Returns (ENR) based on extreme values at first and then tests their lead–lag relations.FindingsThe empirical finding is consistent with the existing evidence of asymmetric news effects on stock returns in India. In precise, ENR robustly predicts one-month-ahead EPR for the sample period from January 1991 to March 2020. This predictive power persists even in the presence of popular valuation ratios and business cycle variables.Practical implicationsThe paper explains the rationale of extreme value modeling in price forecasting. Investors can find additional utility gains from market cycle information while predicting extreme returns in Indian stock market.Originality/valueThe paper is unique to understand business cycle effects in extreme return reversals in emerging markets.

Practical Applications of Is It Good to Sin When Times Are Bad?An Investigation of the Defensive Nature of Sin Stocks

In <b>Is It Good to Sin When Times Are Bad? <i>An Investigation of the Defensive Nature of Sin Stocks</i></b> from the October 2020 issue of <i>The Journal of Investing</i>, author <b>Greg Richey</b> (of the <b>University of California at Riverside</b>) examines a variety of so-called sin industries, individually and altogether, to evaluate their resistance to downside risk as compared with the S&amp;P 500 Index. Using an EGARCH model, Richey is able to show that negative market shocks, or “bad news events,” have a less negative impact on sin stock returns than do positive market shocks, or “good news events.” Using the VIX (also known as the <i>fear index</i>) for stock market volatility, he also indicates that VIX increases lead to delayed increases in anticipated volatilities for a mixed portfolio of various sin stocks. Overall, this makes sin stocks seem like a wise defensive choice for resisting negative shocks. <b>TOPICS:</b>Portfolio theory, portfolio construction, ESG investing

How Does News Affect Stock Return Volatility in a Frontier Market?

Many empirical studies have analysed the effect of good news and bad news on equity market return volatility using both developed and emerging markets data, with scant literature for frontier stock markets. This study evaluates how news affects stock market return volatility in a frontier market using Uganda data. It specifically analyses the reaction of stock return volatility to news filtering into a frontier market using the exponential generalized autoregressive conditional heteroscedasticity (GARCH) model on daily data ranging from 1 September 2011 to 31 December 2017. Estimates of the shape parameter from generalized error distribution indicate the existence of leptokurtic return distribution. Results from the exponential GARCH model show that the effect of bad news and good news on the frontier market return volatility differs, thus suggesting existence of leverage effect in the period studied. Overall results from the study suggest that positive news impacts stock market returns volatility more than negative news of the same magnitude. An important implication of our results is that investors, analysts, brokers and dealers should be conscious of the nature of news filtering into the stock market as such information might improve their expected volatility forecast.

Is It Good to Sin When Times Are Bad? An Investigation of the Defensive Nature of Sin Stocks

In this article, I use an EGARCH model to investigate whether a portfolio of sin stocks is less resistant to downside risk or losses during market downturns than is the S&P 500, a benchmark to approximate the market portfolio of risky stocks. In addition, I use the aforementioned model to analyze the defensive nature of individual sin industries (alcohol, tobacco, gambling, and defense) against the market portfolio. This article contributes to the existing literature on sin investing in two ways. First, my results illustrate the defensive nature of sin stocks and help reinforce the notion that sin stocks have some immunity from downside risk due to cyclical fluctuations and economic downturns. Second, I show results not only from a portfolio of sin stocks, but from various sin industries as well. The industry results highlight the defensive nature of sin stocks, an important component of their risk-adjusted returns. TOPICS:Portfolio theory, portfolio construction, ESG investing Key Findings • Bad-news events have a lesser impact on sin stock return volatility than do good-news events. • Increases in the volatility index (VIX) lead to increases in the overall volatility of sin stocks. • Sin stocks have had higher returns and Sharpe ratios than the market portfolio and sin industry funds’ returns have been less correlated with each other than they have been with the market portfolio.

What Do Employees Know? Evidence from a Social Media Platform

ABSTRACT We use employee predictions of their companies' six-month business outlook from Glassdoor.com to assess the information content of employee social media disclosures. We find that average employee outlook is incrementally informative in predicting future operating performance. Its information content is greater when the disclosures are aggregated from a larger, more diverse, more knowledgeable employee base, consistent with the wisdom of crowds phenomenon. Average outlook predicts bad news events more strongly than good news events, suggesting that employee social media disclosures are relatively more important as a source of bad news. Consistent with the organizational theory, we find systematic differences in the quantity and nature of the information in employee disclosures when the disclosures are grouped based on employee attributes and job responsibilities. Finally, average outlook predicts future returns of firms that attract less attention by analysts and investors, suggesting that investors in these firms use outlook inefficiently.

What Do Employees Know? Evidence from a Social Media Platform

We use employee predictions of their companies’ six-month business outlook from Glassdoor.com to assess the information content of employee social media disclosures. We find that average employee outlook is incrementally informative in predicting future operating performance. Its information content is greater when the disclosures are aggregated from a larger, more diverse, more knowledgeable employee base, consistent with the wisdom of crowds phenomenon. Average outlook predicts bad news events more strongly than good news events, suggesting that employee social media disclosures are relatively more important as a source of bad news. Consistent with the organizational theory, we find systematic differences in the quantity and nature of the information in employee disclosures when the disclosures are grouped based on employee attributes and job responsibilities. Finally, average outlook predicts future returns of firms that attract less attention by analysts and investors, suggesting that investors in these firms use outlook inefficiently.

Predicting Market Reactions to Bad News

Our Applied Finance Project aims to develop a framework to predict short-term and medium-term market reactions to bad news shocks. The study is based on a sample of 18,497 bad news articles and time series of 1,008 Russell 3000 stocks returns during the period 2005 to 2017. Our research proposes a three-stage model for the analysis. Firstly, given a dataset of bad news events and stock prices, we employ time series clustering techniques on cumulative abnormal returns of stocks, by which the news articles related to those stocks are grouped into different clusters. Secondly, we apply Natural Language Processing and multi-class classification algorithms on relevant news articles to extract features of each cluster. Then, by applying Support Vector Machine model, whenever specific bad news is released, we can predict the subsequent short-term, and medium-term market reactions post negative news. Finally, we develop long/short trading strategy for both short-term and medium-term horizons that asset managers in the real world can apply every day.

Financial Forecasting by Autoregressive Conditional Heteroscedasticity (ARCH) Family: A Case of Mexico

Understanding and modeling the volatility measurements is important for forecasting the risk and for evaluating asset allocation decisions of stock market. The study have used the daily frequency data from January 1, 2002 to September 30, 2016 as an in-sample period to perform empirical analyses for modeling and predicting the volatility dynamics of Mexican stock market (IPC). To facilitate the variance forecast, the competing models are ARCH (p, q), GARCH (p, q), and its variations i.e. Glosten Jagnnathon Runkle GARCH, GARCH in Mean, Exponential GARCH, and Quadratic GARCH. The results of residual diagnostics suggested that stock market of Mexico is characterized by heteroskedasticity, multicolinearity, non-normality, and serial correlation. Volatility measurements by ARCH and GARCH signify that the current conditional variance of Mexico is determined by its past price behavior and previous day volatility. Today’s volatility does impact the current stock returns as indicated by GARCH-M. Results of EGARCH explained that any large size news produces high volatility as compared to small size news. Effects of bad news are greater on the volatility of the Mexican stock market than good news. GJR GARCH described the asymmetric behavior of returns and variance in the politically conflicted regime during 2006-2012. Moreover, QGARCH effect is not linear. Findings have the implications for individuals and corporate investors about retaining their risky stocks.

Modelling Co-movement of Different Sectors in Dhaka Stock Exchange (DSE) Using Asymmetric BVAR-GARCH Models

This paper tries to investigate the financial shock transmission dynamics using daily return data under different sectors traded in Dhaka Stock Exchange (DSE). Bayesian VAR model was used as conditional mean in GJR-GARCH, scalar-diagonal VECH and BEKK GARCH models to test return and volatility spillover effects. Lagged squared residuals and lagged conditional variances were used as variance regressors in conditional variance of GJR-GARCH to test the spillover effects. Finding reveals a highly significant memory effect on all sector returns except one. Asymmetric versions of GARCH, VECH and BEKK identified significant effect of news about volatility, past memory and differential effect of bad news on conditional volatility of almost all series. GJR-GARCH results identified pharmaceutical sector spillover free yet all other pairs were found to be unidirectional or in some cases bidirectional. Co-volatility among almost all sectors is also observed from VECH and BEKK output.

Modeling Stock Market Volatility Using Univariate GARCH Models: Evidence from Bangladesh

This paper investigates the nature of volatility characteristics of stock returns in the Bangladesh stock markets employing daily all share price index return data of Dhaka Stock Exchange (DSE) and Chittagong Stock Exchange (CSE) from 02 January 1993 to 27 January 2013 and 01 January 2004 to 20 August 2015 respectively. Furthermore, the study explores the adequate volatility model for the stock markets in Bangladesh. Results of the estimated MA(1)-GARCH(1,1) model for DSE and GARCH(1,1) model for CSE reveal that the stock markets of Bangladesh capture volatility clustering, while volatility is moderately persistent in DSE and highly persistent in CSE. Estimated MA(1)-EGARCH(1,1) model shows that effect of bad news on stock market volatility is greater than effect induced by good news in DSE, while EGARCH(1,1) model displays that volatility spill over mechanism is not asymmetric in CSE. Therefore, it is concluded that return series of DSE show evidence of three common events, namely volatility clustering, leptokurtosis and the leverage effect, while return series of CSE contains leptokurtosis, volatility clustering and long memory. Finally, this study explores that MA(1)-GARCH(1,1) is the best model for modeling volatility of Dhaka stock market returns, while GARCH models are inadequate for volatility modeling of CSE returns.

A Friendly Turn: Advertising Bias in the News Media

This paper investigates whether newspapers report more favorably about advertising corporate clients than about other firms. Our identification strategy based on high-dimensional fixed effects and high frequency advertising data shows that advertising leads to more positive press coverage. This advertising bias in reporting is found among local and national newspapers. Further results show that advertising bias manifests particularly in less negative reporting after bad news events such as negative earnings surprises or extremely negative stock returns. Our findings cast doubt on the independence of the press from corporate pressure and hint at important information frictions.

Communicating with stakeholders when bad news is uncertain

This paper explores the question of if and when organizations should communicate bad news to their stakeholders. I argue that stakeholder theory provides an ethical obligation for organizations to communicate bad news that has potential impacts on their stakeholders but does not answer the question of when this bad news should be communicated. The literature on crisis communication recommends communicating quickly and persuasively to minimize losses but predominantly relies on one-way communication approaches. In this paper I argue that under conditions of uncertainty, when the bad news suffers from an unknown cause or unclear problem definition, both stakeholder theory and the crisis communication literature fail to inform the organization how they should act. In order to provide additional guidelines to organizations, I propose a typology of uncertain bad news that provides contextual communication strategies to organizations when they experience a bad news events. These bad news communication strategies will allow organizations to fulfill their ethical obligations while minimizing the negative impact of the bad news on their stakeholders.

The Effect of Bad News and CEO Apology of Corporate on User Responses in Social Media.

While social media has become an important platform for social reputation, the emotional responses of users toward bad news have not been investigated thoroughly. We analyzed a total of 20,773 Twitter messages by 15,513 users to assess the influence of bad news and public apology in social media. Based on both computerized, quantitative sentiment analysis and in-depth qualitative analysis, we found that rapid public apology effectively and immediately reduced the level of negative sentiment, where the degree of change in sentiments differed by the type of interactions users engaged in. The majority of users who directly conversed with corporate representatives on the new media were not typical consumers, but experts and practitioners. We extend the existing cognitive model and suggest the audiences’ psychological reaction model to describe the information processing process during and after an organizational crisis and response. We also discuss various measures through which companies can respond to a crisis properly in social media in a fashion that is different from conventional mass media.

Prior Return Effect in Indian Stock Market: An Intra-day Analysis

In a first of this kind, this paper examines the issue of prior return effect in Indian stock market in intra-day analysis using high frequency data. We document that in Indian stock market, security returns exhibit a reversal in their direction within few minutes of extreme price rises as well as price falls. However the speed with which the correction takes place is slightly different for good news events and bad news events. Indian investors tend to be optimistic as they immediately bring stock prices up following unjustified price falls but take time to bring stock prices down following unjustified price rises. These findings lend a further support to short-term overreaction literature. More importantly, these findings serve as a proof of predictability of the direction of future stock prices and consequent returns on an intra-day basis. It forwards important investment implications for traders, fund managers, and investors at large.

The Effect of Money Supply on the Volatility of Korean Stock Market

We examined the potential relationships between changes in the money supplies of Korea and the United States and volatility of the Korean stock market using the GARCH, GJR-GARCH, and EGARCH models. We did not identify any such relationships, implying that changes in money supply do not influence the flow of information to the market. However, we found that the asymmetric effect of bad news on volatility was higher when contemporaneous changes in Korean and US money supply variables were included in the models. This indicates that changes in money supply did not affect Korean stock volatility directly. Finally, the results based on a variance model indicated that the money supply of the two countries had no effect on the Korean stock market. This formal study suggests that there is no significant forecasting power of past changes in money supply. Although stock returns and volatility are not directly affected by changes in the money supply, the influence of supply on macroeconomic activity should not be disregarded.

News or Noise? Using Twitter to Identify and Understand Company‐specific News Flow

AbstractThis study presents a methodology for identifying a broad range of real‐world news events based on microblogging messages. Applying computational linguistics to a unique dataset of more than 400,000 S&amp;P 500 stock‐related Twitter messages, we distinguish between good and bad news and demonstrate that the returns prior to good news events are more pronounced than for bad news events. We show that the stock market impact of news events differs substantially across different categories.