AGOA exclusion adds to economic pressure on Ethiopia

Significance This adds to the bad economic news for the country, which recently had to postpone its second telecoms licensing round, is still waiting to restructure its foreign debt and faces huge reconstruction costs in conflict-affected areas. Impacts Foreign companies that have evacuated personnel, let alone tourists, may be slow to return until the security situation improves markedly. A debt restructuring deal should be reached this year, easing fiscal pressure after the end of the G20 Debt Service Suspension Initiative. Ethiopia’s AGOA exclusion is not up for review again until 2023, and could even be extended absent real improvement in the situation.

Analysis of COVID-19 news coverage by Televisión Española (TVE1)

This article analyzes the news coverage of the COVID-19 health crisis by Televisión Española (TVE1), to determine if this media reported the news with rigor and in accordance with journalism professional codes and best practice guidelines. For this purpose, content analysis was conducted on the universe of news stories (n=1,449) in the TVE1 daily newscast, starting with the first outbreak of the pandemic on the Iberian Peninsula on February 26, 2020, until the end of the first state of emergency on June 21, 2020. Our categories of analysis were: information sources, news frames, predominant topics, resources used for dramatic effects, and breaches of journalism ethics in reporting news. In general, TVE1 did not engage in sensationalized or dramatized news coverage, but instead attempted to transmit a message that was educational and instructional. Its policy was to provide information on measures adopted by authorities to help prevent the spread of the pandemic. Nevertheless, TVE’s benevolent attitude towards the government and its policies can be observed in its news reporting, revealing a lack of impartiality and editorial independence by this media. Despite the importance of specialized and expert information in times of a pandemic, eyewitness sources were those most used in reporting news, even in economic news framing. Furthermore, these latter sources were employed instead of expert ones, which were in fact the least used, and whose presence progressively declined during the analyzed period.

IMPACT OF COVID-19 ON CRYPTOCURRENCIES: EVIDENCE ON INFORMATION TRANSMISSION THROUGH ECONOMIC AND FINANCIAL MARKET SENTIMENTS

This paper investigates the relationship between the COVID-19 crisis and the two leading cryptocurrencies, Bitcoin and Ethereum, from 31 December 2019 to 18 August 2020. We also use an economic news sentiment index and financial market sentiment index to explore the possible mechanisms through which COVID-19 impacts cryptocurrency. We employ a VAR Granger Causality framework and Wavelet Coherence Analysis and find the cryptocurrency market was impacted in the early phase of the sample period through economic news and financial market sentiments, but this effect diminished after June 2020.

Last hour momentum in the Chinese stock market

PurposeTo capture the last hour momentum over the intraday session, the authors develop a trading strategy for the exchange-traded fund (ETF) that is effective because of the T+0 trading rule. This strategy generates annualized excess return of 9.673%.Design/methodology/approachIn this study, the authors identify a last hour momentum pattern in which the sixth (seventh) half-hour return predicts the next half-hour return by employing high frequency 2012–2017 data from the China Securities Index (CSI) 300 and its ETF.FindingsOverall, both the predictability and the trading strategy are statistically and economically significant. In addition, the strategy performs more strongly on high volatility days, high trading volume days, high order-imbalance days and days without economic news releases than on other days.Originality/valueNoise trading, late-information trading, infrequent rebalancing and disposition effects from retail investors may account for this phenomenon.

A Corpus-Based Study on Conceptual Metaphors in the Finance & Economics Column of The Economist

Traditional metaphor researches consider metaphors as a rhetoric device for ornamental study. In 1980, Lakoff and Johnson put forward the Conceptual Metaphor Theory, which marks the shift of metaphor study from rhetoric view to cognitive view. Since then, numerous studies at home and abroad on conceptual metaphors have emerged. Economic news has also become a research interest. However, few research concerns about The Economist, let alone Finance & Economics Column inside. Therefore, this study explores the conceptual metaphors in the Finance & Economics Column of the Economist with Conceptual Metaphor Theory as a theoretical foundation. In order to address the above questions, the paper selects articles from October 2019 to December 2019 in The Economist and employs both qualitative and quantitative approaches to analyze conceptual metaphors in the self-constructed corpus. The results show that: firstly, altogether 443 conceptual metaphors are identified in the corpus, covering structural metaphor, ontological metaphor and orientational metaphor. Due to space limitation, only JOURNNEY metaphor, HUMAN BEING metaphor and UP/DOWN metaphor with high frequency is selected to be analyzed in detail. And their frequency varies from each other. Secondly, these three metaphors are identified in the corpus function by mapping from the source domain to the target domain. Finally, the frequency of these three metaphors is different lies in the systematicity, cultural coherence of metaphors and characteristics of economic news. This study enlarges the scope of conceptual metaphor and helps enhance their metaphorical awareness in economic discourses.

Stock Trend Prediction Using Deep Learning Approach on Technical Indicator and Industrial Specific Information

A stock trend prediction has been in the spotlight from the past to the present. Fortunately, there is an enormous amount of information available nowadays. There were prior attempts that have tried to forecast the trend using textual information; however, it can be further improved since they relied on fixed word embedding, and it depends on the sentiment of the whole market. In this paper, we propose a deep learning model to predict the Thailand Futures Exchange (TFEX) with the ability to analyze both numerical and textual information. We have used Thai economic news headlines from various online sources. To obtain better news sentiment, we have divided the headlines into industry-specific indexes (also called “sectors”) to reflect the movement of securities of the same fundamental. The proposed method consists of Long Short-Term Memory Network (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) architectures to predict daily stock market activity. We have evaluated model performance by considering predictive accuracy and the returns obtained from the simulation of buying and selling. The experimental results demonstrate that enhancing both numerical and textual information of each sector can improve prediction performance and outperform all baselines.

Analysis of attitudes toward china in different types of Russian media reports.

As a world power, Russia is China's largest neighbor and plays a special role in strengthening my country's international communication capabilities. Russia's reports related to China have both positive and negative content, but the viewing angle is more practical and objective. China's image is rarely distorted, belittled, and vilified by political bias and ideological divisions. The overall image of China and China in the international arena The role is positive. Media reports are mainly political and economic news, with a particular focus on information on political and economic and trade exchanges between China and Russia. What are the tendencies in the attitude of the Russian media towards China? How does society and theory view the situation in the trilateral relations between Russia, China and the United States? Are there any new trends in the perception of China's image by political elites and ordinary people? What has been causing concern about China in the Russian media lately? What direction are the media and think tanks showing on the burning issues of the Sino-Indian border conflict? These problems reflect Russians' perception of China's image, are related to the effectiveness of my country's "telling Chinese stories" and external communications, and also reflect the development of Sino-Russian relations and the level of my country's influence in the international arena.