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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.

Negative media coverage has important impacts on firm financial performance, but existing studies have inconsistent views of this relationship and lack a unified theoretical framework to explain how such impacts arise. This study aims to bridge this gap in the literature.

This study uses two sets of data encompassing publicly listed companies in Shanghai and Shenzhen stock exchanges from 2013 to 2019, which are covered by the China Stock Market and Accounting Research Database.

This study finds that the number of negative news coverages has an inverted U-shaped relationship with firm financial performance; this relationship is weakened by the proportion of shares held by institutional investors and strengthened by advertising intensity.

This study suggests that corporate executives should be aware of the potential value of a limited amount of negative news coverage and react with tolerance and caution when their companies encounter it.

This study uses two different routes provided in the elaboration likelihood model theory to fully explain the processes underlying changes in investors’ attitudes toward firms experiencing negative media coverage.

This study examines the effect of firm-level investor sentiment on a firm's level of financial distress.

The authors use Bloomberg's firm-level, daily investor sentiment scores derived from firm-level news and Twitter content in a beta-regression model to explain the variability in a firm's financial distress.

The results indicate that improvements (deterioration) in investor sentiment derived from both news articles and Twitter content lead to a decrease (increase) in the average firm's financial distress level. We also find that the effect of sentiment derived from Twitter on a firm's financial distress is significantly stronger than the sentiment derived from news articles.

Our proxy for financial distress is Bloomberg's financial distress measures, which may be an imperfect measure of financial distress. Our results have important implications for market participants in assessing the determinants of financial distress.

Our sample period covers four years (2015–2019), which is determined by Bloomberg sentiment data availability.

Market participants are increasingly using social media to express views on firms and seek information that might be used to determine a firm's level of financial distress. Our study links investor sentiment derived from social media (Twitter) and traditional news articles to financial distress.

By examining the relationship between a firm's sentiment and its financial distress, this paper advances our understanding of the factors that drive a firm's financial distress. To our knowledge, this is the first study to link US firms' investor sentiment derived from firm-level news and Twitter content to a firm's financial distress.

This is a literature survey paper. The purpose of this paper is to focus on the latest developments in textual analysis on China’s financial markets, highlighting its differences from existing works in the US markets.

The authors review the literature and carry out an experiment of sentiment analysis based on a small sample of Chinese news articles.

Based on the experiment of sentiment analysis, there is limited evidence on the association between sentiment and other contemporaneous or future returns.

The supply of financial textual information has grown exponentially in the past decades. Technological advancements in recent years make the programming-based analysis an effective tool to digest such information. The authors highlight the use of credible textual information and discuss directions of research in this important field.

The purpose of this study is to develop the dictionary with grammar and multiword structure has to be used in conjunction with sentiment analysis to investigate the relationship between financial news and stock market volatility.

An algorithm has been developed for calculating the sentiment orientation and score of data with added information, and the results of calculation have been integrated to construct an empirical model for calculating stock market volatility.

The experimental results reveal a statistically significant relationship between financial news and stock market volatility. Moreover, positive (negative) news is found to be positively (negatively) correlated with positive stock returns, and the score of added information of the news is positively correlated with stock returns. Model verification and stock market volatility predictions are verified over four time periods (monthly, quarterly, semiannually and annually). The results show that the prediction accuracy of the models approaches 66% and stock market volatility with a particular trend-predicting effect in specific periods by using moving window evaluation.

Only one news source is used and the research period is only two years; thus, future studies should incorporate several data sources and use a longer period to conduct a more in-depth analysis.

Understanding trends in stock market volatility can decrease risk and increase profit from investment. Therefore, individuals or businesses can feasibly engage in investment activities for profit by understanding volatility trends in capital markets.

The ability to exploit textual information could potentially increase the quality of the data. Few scholars have applied sentiment analysis in investigating interdisciplinary topics that cover information management technology, accounting and finance. Furthermore, few studies have provided support for structured and unstructured data. In this paper, the efficiency of providing the algorithm, the model and the trend in stock market volatility has been demonstrated.

This paper aims to investigate the impact of climate change news on the dynamics of US stock indices.

The empirical basis of the study was 3,209 news articles. Sentiment analysis was performed by a pre-trained bidirectional FinBERT neural network. Thematic modeling is based on the neural network, BERTopic.

The results show that news sentiment can influence the dynamics of stock indices. In addition, five main news topics (finance and politics natural disasters and consequences industrial sector and Innovations activism and culture coronavirus pandemic) were identified, which showed a significant impact on the financial market.

First, we extend the theoretical concepts. This study applies signaling theory and overreaction theory to the US stock market in the context of climate change. Second, in addition to the news sentiment, the impact of major news topics on US stock market returns is examined. Third, we examine the impact of sentimental and thematic news variables on US stock market indicators of economic sectors. Previous works reveal the impact of climate change news on specific sectors of the economy. This paper includes stock indices of the economic sectors most related to the topic of climate change. Fourth, the research methodology consists of modern algorithms. An advanced textual analysis method for sentiment classification is applied: a pre-trained bidirectional FinBERT neural network. Modern thematic modeling is carried out using a model based on the neural network, BERTopic. The most extensive topics are “finance and politics of climate change” and “natural disasters and consequences.”

I was editor of the City Press weekly newspaper from 1966 to 1975. From 1970 I also produced for BBC sound radio a daily report on the city. For 5 years I did daily pieces for BBC Radio London; and for two years in addition I did a spot on the Stock Exchange and the financial news of the day for the ‘P.M.’ programme at 5.50. On top of this, at City Press we briefed both BBC Radio London and ‘The World at One’ on any exciting City events which took place in the morning, and often broadcast about them.

Financial price forecast issues are always a concern of investors. However, the financial applications based on machine learning methods mainly focus on stock market predictions. Few studies have explored credit risk predictions. Understanding credit risk trends can help investors avoid market risks. The purpose of this study is to investigate the prediction model that can effectively predict credit default swaps (CDS).

A novel generative adversarial network (GAN) for CDS prediction is proposed. The authors take three features into account that are highly relevant to the future trends of CDS: historical CDS price, news and financial leverage. The main goal of this model is to improve the existing GAN-based regression model by adding finance and news feature extraction approaches. The proposed model adopts an attentional long short-term memory network and convolution network to process historical CDS data and news information, respectively. In addition to enhancing the effectiveness of the GAN model, the authors also design a data sampling strategy to alleviate the overfitting issue.

The authors conduct an experiment with a real dataset and evaluate the performance of the proposed model. The components and selected features of the model are evaluated for their ability to improve the prediction performance. The experimental results show that the proposed model performs better than other machine learning algorithms and traditional regression GAN.

There are very few studies on prediction models for CDS. With the proposed novel approach, the authors can improve the performance of CDS predictions. The proposed work can thereby increase the commercial value of CDS predictions to support trading decisions.

We document how inflation expectations evolved in the United States during the fall of 1933 using narrative evidence from historical news accounts and the forecasts of contemporary business analysts. We find that inflation expectations, after rising substantially during the spring of 1933, moderated in the fall in response to mixed messages from the Roosevelt Administration. The narrative accounts and our econometric model connect the dramatic swings in output growth in 1933 – the rapid recovery in the spring and the setback in the fall – to these sudden movements in inflation expectations.

Examines corporate fraud and its relationship with the media. Discusses rise in volume of fraud, due to technological advances, more teamwork and the involvement of organised crime. Looks at the decline in financial reporting this century, with the fourth estate or watchdog traditions of the press not overly concerned with financial news, due to the growth in market‐driven journalism. This means that financial crime in the press is seen as a downer in the market and therefore not encouraged. UK investigative journalists also face tough defamation laws and cannot be expected to act as early warning systems when the crime is undetected by the company involved. Looks at examples of major financial crimes that have been uncovered by journalistic investigation, but concludes that too much is demanded of news media and its resources and expertise is best used in developing news coverage once the financial scandal has been uncovered.