News Announcements Research Articles

Public Response to Federal Electronic Cigarette Regulations Analyzed Using Social Media Data Through Natural Language Processing: Topic Modeling Study.

e-Cigarette (electronic cigarette) use has been a public health issue in the United States. On June 23, 2022, the US Food and Drug Administration (FDA) issued marketing denial orders (MDOs) to Juul Labs Inc for all their products currently marketed in the United States. However, one day later, on June 24, 2022, a federal appeals court granted a temporary reprieve to Juul Labs that allowed it to keep its e-cigarettes on the market. As the conversation around Juul continues to evolve, it is crucial to gain insights into the sentiments and opinions expressed by individuals on social media. This study aims to conduct a comprehensive analysis of tweets before and after the ban on Juul, aiming to shed light on public perceptions and sentiments surrounding this contentious topic and to better understand the life cycle of public health-related policy on social media. Natural language processing (NLP) techniques were used, including state-of-the-art BERTopic topic modeling and sentiment analysis. A total of 6023 tweets and 22,288 replies or retweets were collected from Twitter (rebranded as X in 2023) between June 2022 and October 2022. The encoded topics were used in time-trend analysis to depict the boom-and-bust cycle. Content analyses of retweets were also performed to better understand public perceptions and sentiments about this contentious topic. The attention surrounding the FDA's ban on Juul lasted no longer than a week on Twitter. Not only the news (ie, tweets with a YouTube link that directs to the news site) related to the announcement itself, but the surrounding discussions (eg, potential consequences of this ban or block and concerns toward kids or youth health) diminished shortly after June 23, 2022, the date when the ban was officially announced. Although a short rebound was observed on July 4, 2022, which was contributed by the suspension on the following day, discussions dried out in 2 days. Out of the top 50 most retweeted tweets, we observed that, except for neutral (23/45, 51%) sentiment that broadcasted the announcement, posters responded more negatively (19/45, 42%) to the FDA's ban. We observed a short life cycle for this news announcement, with a preponderance of negative sentiment toward the FDA's ban on Juul. Policy makers could use tactics such as issuing ongoing updates and reminders about the ban, highlighting its impact on public health, and actively engaging with influential social media users who can help maintain the conversation.

Manipulating stock prices based on insider information

Purpose This paper aims to investigate the trading behavior of insider investors before and after information releases, identifying information-based manipulation in the stock market and the characteristics of companies whose stock prices are manipulated. Design/methodology/approach This paper employs logit regression method and an event study approach, utilizing hand-collected data from 2010 to 2022, with information categorized into negative and positive types. Findings The results show no evidence of insider trading or negative information-based manipulation in both high and low transparency firms. However, in highly transparent companies, the Board of Directors (BOD) avoids direct manipulation by using relatives to evade market supervisors. In low transparency companies, both the BOD and family members (FM) exploit positive information to benefit personally by buying shares before releasing favorable news, causing a sharp stock increase, and selling afterward. Continued buying by the BOD and FM also suggests likely positive news announcements. Practical implications The characteristics of information-based manipulation in companies, as provided by this study, help individual investors avoid investing in stocks that are highly susceptible to manipulation. Originality/value Empirical research on information-based manipulation is scarce due to limited secondary data. Our study uses transaction data from insider investors in a frontier market with low transparency and high information asymmetry. This enables us to analyze information-based stock price manipulation. We identify manipulation by comparing insiders' trading behavior with their market information releases, resulting in stock price fluctuations greater than 5%.

Macroeconomic news, senior officials' speeches, and emerging currency markets: An intraday analysis of price jump reaction

We examine intraday emerging currency jumps in response to domestic and foreign macroeconomic news announcements, as well as US senior officials' speeches. We use the robust-to-jump volatility estimator to estimate currency jumps. We provide evidence that both emerging currency jumps and cojumps exhibit significant and pronounced responses to US monetary policy and real activity news releases. Moreover, we find that domestic monetary policy and economic real activity data, and senior Federal Reserve officials' speeches, trigger significant jumps in emerging currencies, leading to large currency fluctuations that would disrupt market stability, undermining economic resilience and potentially diminishing ESG ratings.

How does Chinese stock market react to breaking news about COVID-19? Evidence from event study

Although the COVID-19 pandemic is no longer considered a public health emergency of international concern, research is ongoing to determine whether and how financial markets have been affected. Using an event study methodology, we examine Chinese stock reactions to breaking news about COVID-19 during 2020–2023, covering nine important events from the first declaration of the novel coronavirus to classifying COVID-19 from top category A to B in China. The empirical results show that the Chinese stock market reacts negatively to bad news, such as the breakout of COVID-19 and the dynamic zero-COVID strategy, and positively to good news, such as COVID-19 vaccine approvals, ending the zero-COVID strategy, and reopening boards. The overall intensity of reactions to bad news is stronger than that to good news. Small-sized stocks react more positively to good news and more negatively to bad news. Different industries have different reactions to different events; however, the sector that responds strongly and negatively to bad news does not have to respond strongly and positively to good news, which, to some extent, reflects their profitability performance during the pandemic. In China, consumer discretionary, utilities and real estate are the most negatively affected by the pandemic, while materials, health care, and energy are the most positively affected. This study has important implications for reminding market participants to pay attention to breaking news announcements about COVID-19.

UBS's Acquisition of Credit Suisse: Case Study of Acquisition Results between Large Banks

UBS's acquisition of Credit Suisse is a typical case of acquisitions between large banks, which has a great influence on the development of UBS itself and the Swiss banking industry. This paper aims to evaluate the success of the acquisition based on the changes in UBS’s stock price at the time of the acquisition news announcement and the actual occurrence of the acquisition. Through the study of UBS’s stock price fluctuations before and after the release of acquisition news, this paper finds that investors generally hold a positive attitude toward the impact of the acquisition on UBS's operation and future revenues. Through analysis of the stock price and owners' equity per share changes during the acquisition, it can be indicated that the income of UBS also increased significantly after the actual occurrence of the acquisition, which is confirmed by both the positive reaction of market investors and UBS's financial statements. The generally upward indicators such as stock price, return on stocks, and owners 'equity reflect the overall positive financial information of UBS, therefore UBS's acquisition can be considered successful to a large extent. According to this acquisition case, the acquisition is a significant means for large banks to enhance market investors' optimism about them, as well as reduce their operating costs and increase future revenues.

How Much Can Machines Learn Finance from Chinese Text Data?

How much can we learn finance directly from text data? This paper presents a new framework for learning textual data based on the factor augmentation model and sparsity regularization, called the factor-augmented regularized model for prediction (FarmPredict), to let machines learn financial returns directly from news. FarmPredict allows the model itself to extract information directly from articles without predefined information, such as dictionaries or pretrained models as in most studies. Using unsupervised learned factors to augment the predictors would benefit our method with a “double-robust” feature: that the machine would learn to balance between individual words or text factors/topics. It also avoids the information loss of factor regression in dimensionality reduction. We apply our model to the Chinese stock market with a large proportion of retail investors by using Chinese news data to predict financial returns. We show that positive sentiments scored by our FarmPredict approach from news generate on average 83 basic points (bps) stock daily excess returns, and negative news has an adverse impact of 26 bps on the days of news announcements, where both effects can last for a few days. This asymmetric effect aligns well with the short-sale constraints in the Chinese equity market. The result shows that the machine-learned prediction does provide sizeable predictive power with an annualized return of 54% at most with a simple investment strategy. Compared with other statistical and machine learning methods, FarmPredict significantly outperforms them on model prediction and portfolio performance. Our study demonstrates the far-reaching potential of using machines to learn text data. This paper was accepted by Kay Giesecke, finance. Funding: This study was supported by the National Natural Science Foundation of China [Grants 71991471, 71991470, and 72204049], the National Key Research and Development Program [Grant 2020YFA0608604], the Shanghai Pujiang Scholar Project [Grant 21PJC010], the Shanghai Science Project [Grant 23692119300], and the China Postdoctoral Science Project [Grants 2019M650076 and 2020T130107]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01468 .

EVENT STUDY: THE IMPACT OF THE RUSSIA-UKRAINE INVASION ON STOCK PRICES AND ABNORMAL RETURNS

<p><strong>Background of The Study</strong>: The capital market is able to react quickly to issues that arise around it. In February 2022, Russia conducted military operations against Ukraine. Of course, this stole global attention. Investors need to pay attention to any information that may affect trading in the capital market.</p><p><strong>Purpose of The Study</strong>: Empirically analyze how the news of Russia's invasion of Ukraine affects the stock prices and abnormal returns of energy industry companies listed on the Indonesia Stock Exchange (IDX).</p><p><strong>Research Method</strong>: The research uses the event study method, with an observation period of 30 days before and after the announcement to measure the public reaction to the news of the invasion by Russia.</p><p><strong>Research Result</strong>: The results showed that there were visible changes in stock prices from before to after the news of the Russia-Ukraine invasion was announced, as shown in this study. Meanwhile, there is no visible difference in abnormal returns either before or after the news announcement of the invasion between Russia and Ukraine is published.</p><p><strong>Research Gap</strong>: Russia's invasion of Ukraine occurred in 2022, and this study examines companies in the energy industry.</p><p><strong>Keywords</strong>: Russia-Ukraine Invasion, Stock Price, Abnormal Return, Event Study.</p>

Informed Trading Intensity

ABSTRACTWe train a machine learning method on a class of informed trades to develop a new measure of informed trading, informed trading intensity (ITI). ITI increases before earnings, mergers and acquisitions, and news announcements, and has implications for return reversal and asset pricing. ITI is effective because it captures nonlinearities and interactions between informed trading, volume, and volatility. This data‐driven approach can shed light on the economics of informed trading, including impatient informed trading, commonality in informed trading, and models of informed trading. Overall, learning from informed trading data can generate an effective informed trading measure.

Crowdsourced Forecasts and the Market Reaction to Earnings Announcement News

ABSTRACT This study examines whether crowdsourced forecasts of earnings and revenues help investors unravel bias in earnings announcement news, which is commonly derived from analyst forecasts. Our results suggest that investors, on average, understand and price the predictive signals reflected in crowdsourced forecasts about the bias in analyst-based earnings and revenue surprises. Using the staggered addition of firms to the Estimize platform, we find that crowdsourced coverage is associated with reductions in the mispricing of forecast bias and declines in earnings announcement premia. We further find some evidence that managers use income-increasing accruals to meet the crowdsourced forecast benchmark and that they respond to crowdsourced coverage through increased downward earnings and revenue guidance. Overall, we conclude that user-generated content on crowdsourced financial information platforms helps investors discount biases in traditional equity research and thereby better process the news in earnings announcements. JEL Classifications: G14; G20; M41.

Analysing the impacts of unscheduled news events on stock market contagion during the epidemic

AbstractThis paper investigates the impact of unscheduled news announcements on market contagion during the COVID‐19 pandemic. Using coexceedance of stock returns as a metric for market contagion effect, we assess the contribution of news releases from the United States and China on the financial contagion of a representative group of global equity markets through a quantile analysis framework. The empirical results are mixed: news events originating in the United States have a greater impact on market contagion compared with those originating in China, especially at lower quantiles. Stock markets respond asymmetrically to good news versus bad news, and the latter lead to a sharper common fall among the markets than the boost to the market caused by good news. We also find evidence that conditional variance and investor sentiment play some role in the spread of financial market crises, despite differences in extent and direction.

Social Information Portal for Russian University Students in the form of a Mobile Application "Зачёт"

Relevance. The current direction in the development of the higher education system, set by the Ministry of Science and Higher Education of the Russian Federation, is the digitalization of education. This determines the relevance of creating a social information portal for students of Russian universities.The purpose. Development of a mobile application for a social information portal for students of Russian universities with the ability to view information provided by the university within the electronic information and educational environment, as well as to provide the possibility of social interaction between students through the application.Methodology. When creating a software and information system, the Kotlin programming language, the XML markup language, was used.Results. The following features are implemented in the developed application: navigation in the menu, views of points, chats for communication, publication of news announcements in the feed, viewing the schedule, authorization, registration and setting up an account and application. When developing a social information portal for students of Russian universities, the following tasks were solved: implementation of a service for viewing scores in subjects at a university for students; implementation of the possibility of publishing and viewing ads within the application; implementation of the possibility of viewing the schedule of classes; implementation of the service of internal chats of the application and the possibility of communication between students and teachers; the ability to update the version of the application.Conclusion. In the course of the work, a social information portal was designed and implemented for students of Russian universities in the format of a mobile application. As a result of the tests, it was found that the software system fully meets the functional requirements.

Foreign investors and stealth trading: An examination of price movements in developing markets

We investigate whether foreign investors have better information than domestic investors. We are motivated by (1) the controversy over the performance of foreign versus domestic investors and (2) market dynamics that require investors to adapt constantly. Using 123 positive and 81 negative news items from the Indonesia Stock Exchange (IDX), we find that foreign investors account for larger price movements before news announcements, suggesting that foreign investors tend to have better information than domestic investors. The result is robust, even after including the total trading volume, total number of transactions, and trading size. Further investigation reveals that foreign investors' small trades account for the largest price movements. Initiated trades account for larger price movements. Domestic investors have more foreign advantages than foreign investors. We also find that the foreign advantage disappears during non-event periods. Our results contrast with previous findings showing that domestic investors have better information than foreign investors. Our findings suggest that investor behavior is dynamic; investors adjust to their environment, which may lead to changes in their performance.

A Cross-Cultural Multi-Country Analysis of Unfavorable News Announcements in Public Companies

Value-relevant bad news leads to declining stockholder wealth. Various factors moderate the decline. We explore the moderating effect of culture (country), economic development levels, and insider trading laws. To this effect, we compile rich bad news announcement data from the US, Japan, China, and India. Ours is the first such study to cover comprehensive data from multiple countries.
 Stock markets in the US, Japan, and India experience a significant stock decline following the public announcement of bad news. In contrast, companies traded in the Chinese stock market experienced a positive stock impact. Companies in countries with high long-term orientation (Japan and China) perform better than those with low long-term orientation (the US and India). Economic development levels also play a significant mediating role. Countries with stronger trading laws do not experience stock decline before the public announcement of disruptions. Our study enriches the current state of the art by performing a multi-country analysis of stock impact from bad news announcements. The results are of interest to investors and policymakers.

Sentiment and Accruals Earnings Management: Does Governance and Regulatory Environment Matter?

This paper examines the implications of firm-level governance mechanisms and the regulatory environment on the relationship between investor sentiment and accrual-based earnings management. Our findings confirm the positive impact of sentiment on earnings management through accruals. Our results confirm that in the presence of a stringent regulatory environment, the likelihood of abnormal accruals following a positive sentiment environment is low. We document that a stringent regulatory environment reduces the likelihood of abnormal accruals following a positive sentiment environment. Results further confirm that improved governance characteristics like larger board, increased board independence, and stringent related party transaction norms help to monitor corporate behaviour and mitigates opportunistic earnings management activity of managers with an exogeneous effect of market sentiment. Thus, our results have important implications for regulators and policymakers for strengthening the regulatory and monitoring environment. Investors may also use earnings announcement news during an optimistic market sentiment scenario in a more informative way.

Examining the Supports and Advice That Women With Intimate Partner Violence Experience Received in Online Health Communities: Text Mining Approach.

Intimate partner violence (IPV) is an underreported public health crisis primarily affecting women associated with severe health conditions and can lead to a high rate of homicide. Owing to the COVID-19 pandemic, more women with IPV experiences visited online health communities (OHCs) to seek help because of anonymity. However, little is known regarding whether their help requests were answered and whether the information provided was delivered in an appropriate manner. To understand the help-seeking information sought and given in OHCs, extraction of postings and linguistic features could be helpful to develop automated models to improve future help-seeking experiences. The objective of this study was to examine the types and patterns (ie, communication styles) of the advice offered by OHC members and whether the information received from women matched their expressed needs in their initial postings. We examined data from Reddit using data from subreddit community r/domesticviolence posts from November 14, 2020, through November 14, 2021, during the COVID-19 pandemic. We included posts from women aged ≥18 years who self-identified or described experiencing IPV and requested advice or help in this subreddit community. Posts from nonabused women and women aged <18 years, non-English posts, good news announcements, gratitude posts without any advice seeking, and posts related to advertisements were excluded. We developed a codebook and annotated the postings in an iterative manner. Initial posts were also quantified using Linguistic Inquiry and Word Count to categorize linguistic and posting features. Postings were then classified into 2 categories (ie, matched needs and unmatched needs) according to the types of help sought and received in OHCs to capture the help-seeking result. Nonparametric statistical analysis (ie, 2-tailed t test or Mann-Whitney U test) was used to compare the linguistic and posting features between matched and unmatched needs. Overall, 250 postings were included, and 200 (80%) posting response comments matched with the type of help requested in initial postings, with legal advice and IPV knowledge achieving the highest matching rate. Overall, 17 linguistic or posting features were found to be significantly different between the 2 groups (ie, matched help and unmatched help). Positive title sentiment and linguistic features in postings containing health and wellness wordings were associated with unmatched needs postings, whereas the other 14 features were associated with postings with matched needs. OHCs can extract the linguistic and posting features to understand the help-seeking result among women with IPV experiences. Features identified in this corpus reflected the differences found between the 2 groups. This is the first study that leveraged Linguistic Inquiry and Word Count to shed light on generating predictive features from unstructured text in OHCs, which could guide future algorithm development to detect help-seeking results within OHCs effectively.

The impact of United States macroeconomic news announcements on the Johannesburg Stock Exchange

Orientation: Macroeconomic news announcement poses a risk to stock returns, especially that of United States (US) on emerging markets because of globalisation.Research purpose: The purpose was to investigate the impact that US macroeconomic news announcements have on the Johannesburg Stock Exchange (JSE).Motivation for the study: Investment in financial markets is volatile. Globalisation means that greater economies’ macroeconomic news announcements could impact emerging financial markets. Knowing which announcements have an influence could improve investment decisions.Research design, approach and method: This study is qualitative, following secondary data analysis. Analysis was carried out using volatility modelling, paired with an event study to add robustness.Main findings: The generalised autoregressive conditional heteroskedastic (GARCH) models indicated that US macroeconomic news announcements made in relation to unemployment were statistically significant, with a negative impact on the JSE. This was supplemented by the event study in which interest rate announcements that were made in August 2008 (during the global financial crisis) were found to have a statistically significant impact on the JSE. The coronavirus disease 2019 (COVID-19) pandemic did not appear to have a statistically significant impact in the results obtained from the event study.Practical/managerial implications: Investment industry professionals and policymakers should consider unemployment announcements from the US to enhance investment decisions while managing stock market volatility.Contribution/value-add: The results make a novel contribution towards the findings during the initial COVID-19 period and generalisability of the method as well as the theoretical expansion of the theoretical application of globalisation on stock markets.

Volatility spillovers across the spot and futures oil markets after news announcements

We study the influence of OPEC announcements on volatility transmission across the Brent and WTI oil markets. First, we examine the impact of information shocks with the use of a VEC-MGARCH model, allowing for asymmetry and structural breaks. Second, we examine the transmission of volatility across crude oil markets with the use of a dynamic connectedness approach. Our results demonstrate the existence of asymmetry in the responses to shocks during the Russia-Saudi Arabia oil price war. Furthermore, we find larger volatility responses for the Brent futures and spot prices after negative shocks in the COVID-19 period compared to shocks in the pre-COVID-19 period. Additionally, our results from the dynamic volatility connectedness model suggest that the WTI spot and futures markets are net transmitters of volatility shocks.

Unlocking the secrets of exchange rate determination in Malaysia: A Game-Changing hybrid model

Nominal exchange rate determination is a puzzling phenomenon throughout the literature. Thus, the study aims to analyze the nominal exchange rate determination with a hybrid approach of macroeconomic and microstructure determinants, i.e., interest rate differential, oil price, order flow, and bid-ask spread over the long- and short-run horizons in the context of Malaysia. The dataset consists of high-frequency daily data from 2010 to 2017, employing a nonlinear ARDL approach. The results indicate that the bid-ask spread and interest rate differential were found to be key determinants of exchange rate dynamics in the long and short run. The findings show strong evidence of long-run asymmetry in the interest rate differential, while short-run asymmetry effects exist between microstructure determinants and the exchange rate. In addition, it indicates that the bid-ask spread holds informative content to explain the dynamics of the exchange rate in Malaysia. Additionally, the negative changes in the oil price could potentially act as macroeconomic news announcements and the bid-ask spread as liquidity determinants in Malaysia, which play a significant role in exchange rate determination. The study concluded that the prominent short-run asymmetry effects captured in cumulative order flow and bid-ask spread While a long-run asymmetry exists between the oil price and exchange rate in Malaysia. The empirical results allow for long-run and short-run asymmetric pricing impacts of a hybrid approach on the nominal exchange rate in Malaysia. This study is helpful in providing policy direction and practical implications for monetary authorities and market dealers. The bid-ask spread and oil price could be considered influential exchange rate determinants in the short run in Malaysia.

Cryptocurrency markets, macroeconomic news announcements and energy consumption

Cryptocurrency markets, macroeconomic news announcements and energy consumption

The effect of macroeconomic news announcements on the implied volatility of commodities: The role of survey releases

AbstractWe examine the response of the implied volatility in crude oil, gold, and silver commodity markets to macroeconomic news announcements. We find that macroeconomic releases reduce uncertainty in commodity markets consistent with the hypothesis of uncertainty resolution following scheduled news announcements, while macroeconomic releases from the category of survey news announcements increase it. This result can be explained by the qualitative nature of these surveys and their ability to provide information in advance of major official macroeconomic indicators. Nevertheless, we show that survey news announcements can also be useful in resolving commodity market uncertainty in periods of heightened global financial turmoil and when the manufacturing sector of the largest economies is in contraction.