Stock Market News Research Articles

A semi-supervised approach for Vietnamese stock news classification with deep learning

Stock-related news and articles on Vietnamese economic websites and blogs are rapidly increasing, but they are mixed with entertainment news, miscellaneous topics, and advertisements. This makes it annoy for real investors and analysts who only focus to find and analyze the stock-related information that matters (Boudoukh, 2013). This research introduces a novel method for automatically labeling the relevance of news articles to the stock market, based on a set of criteria derived from financial domain knowledge. In addition, this study also develops a deep learning classifier model that leverages the BERT architecture and the Vietnamese language model (viBERT) (Tran, 2020) to achieve high accuracy and efficiency in scoring the stock market news. This approach helps investors and analysts to filter out the irrelevant content on Vietnamese economic websites and access the most useful information for their mainstream analysis of stock movements.

Supervised Machine Learning Method for Ontology-based Financial Decisions in the Stock Market

For changing semantics, ontological and information presentation, as well as computational linguistics for Asian social networks, are one of the most essential platforms for offering enhanced and real-time data mapping, as well as huge data access across diverse big data sources on the web architecture, information extraction mining, statistical modeling and data modeling, database control, and so on. The concept of opinion or sentiment analysis is often used to predict or classify the textual data, sentiment, affect, subjectivity, and other emotional states in online text. Recognizing the message's positive and negative thoughts or opinions by examining the author's goals will aid in a better understanding of the text's content in terms of the stock market. An intelligent ontology and knowledge Asian social network solution can improve the effectiveness of a company's decision making support procedures by deriving important information about users from a wide variety of web sources. However, ontology is concerned primarily with problem-solving knowledge discovery. The utilization of Internet-based modernizations welcomed a significant effect on the Indian stock exchange. News related to the stock market in the most recent decade plays a vital role for the brokers or users. This article focuses on predicting stock market news sentiments based on their polarity and textual information using the concept of ontological knowledge-based Convolution Neural Network (CNN) as a machine learning approach. Optimal features are essential for the sentiment classification model to predict the stock's textual reviews' exact sentiment. Therefore, the swarm-based Artificial Bee Colony (ABC) algorithm is utilized with the Lexicon feature extraction approach using a novel fitness function. The main motivation for combining ABC and CNN is to accelerate model training, which is why the suggested approach is effective in predicting emotions from stock news.

A robotic reporter still lacks creativity, but it can already replace human journalists in several areas

Automated journalism is experiencing rapid advancements, prompting concerns among certain journalists who fear potential job displacement by robotic reporters. Conversely, there is a cohort of journalists embracing this progress, viewing the involvement of robotic reporters as a time-efficient measure and an avenue to engage in more creative forms of journalism. This article aims to explore topics in Czech Republic news that lend themselves to automation based on perplexity. Currently, the robotic reporter can generate articles in few domains (e.g., stock market news, fuel prices news, and traffic accident reports), while further exploration is underway for other areas. However, generating e.g. cultural news through automation remains challenging due to its less recurrent nature, as evidenced by perplexity measurements reflecting text complexity. Perplexity method can be used to determine which journalistic area is suitable for automated text. In this article, we focus on the Czech environment and the possibilities of automation and the reaction of selected Czech journalists who encounter automated texts in newsrooms. While automated journalism demonstrates superiority over human journalists in certain aspects, it still falls short in terms of creativity, an aspect where artificial intelligence remains unable to replace humans. Nevertheless, the robotic reporter can serve as an invaluable assistant to editors and reporters in their work. Consequently, the newsroom landscape is evolving, accommodating the integration of automated content editors.

Arabic Stock-News Sentiments and Economic Aspects using BERT Model

Stock-market news sentiment analysis (SA) aims to identify the attitudes of the news of the stock on the official platforms toward companies’ stocks. It supports making the right decision in investing or analysts’ evaluation. However, the research on Arabic SA is limited compared to that on English SA due to the complexity and limited corpora of the Arabic language. This paper develops a model of sentiments to predict the polarity of Arabic stock news in microblogs based on Machine Learning and Deep Learning approaches. Also, it aims to extract the reasons which lead to polarity categorization as the main economic causes or aspects based on semantic unity. Therefore, this paper presents an Arabic SA approach based on the logistic regression model and the Bidirectional Encoder Representations from Transformers (BERT) model. The proposed model is used to classify articles as positive, negative, or neutral. It was trained based on data collected from an official Saudi stock-market article platform that was later preprocessed and labeled. Moreover, the economic reasons for the articles based on semantic unit, divided into seven economic aspects to highlight the polarity, were investigated. The supervised BERT model obtained 88% article classification accuracy based on SA, and the unsupervised mean Word2Vec encoder obtained 80% economic-aspect clustering accuracy.

Comparing traditional news and social media with stock price movements; which comes first, the news or the price change?

Twitter has been responsible for some major stock market news in the recent past, from rogue CEOs damaging their company to very active world leaders asking for brand boycotts, but despite its impact Twitter has still not been as impactful on markets as traditional news sources. In this paper we examine whether daily news sentiment of several companies and Twitter sentiment from their CEOs have an impact on their market performance and whether traditional news sources and Twitter activity of heads of government impact the benchmark indexes of major world economies over a period spanning the outbreak of the SAR-COV-2 pandemic. Our results indicate that there is very limited correlation between Twitter sentiment and price movements and that this does not change much when returns are taken relative to the market or when the market is calm or turbulent. There is almost no correlation under any circumstances between non-financial news sources and price movements, however there is some correlation between financial news sentiment and stock price movements. We also find this correlation gets stronger when returns are taken relative to the market. There are fewer companies correlated in both turbulent and calm economic times. There is no clear pattern to the direction and strength of the correlation, with some being strongly negatively correlated and others being strongly positively correlated, but in general the size of the correlation tends to indicate that price movement is driving sentiment, except in the turbulent economic times of the SARS-COV-2 pandemic in 2020.

A study on stock price prediction system based on text mining method using LSTM and stock market news

The stock price reflects people's psychology, and factors affecting the entire stock market include economic growth rate, economic rate, interest rate, trade balance, exchange rate, and currency. The domestic stock market is heavily influenced by the stock index of the United States and neighboring countries on the previous day, and the representative stock indexes are the Dow index, NASDAQ, and S & P500. Recently, research on stock price analysis using stock news has been actively conducted, and research is underway to predict the future based on past time series data through artificial intelligence-based analysis. However, even if the stock market is hit for a short period of time by the forecasting system, the market will no longer move according to the short-term strategy, and it will have to change anew. Therefore, this model monitored Samsung Electronics' stock data and news information through text mining, and presented a predictable model by showing the analyzed results.

التعرض للقضایا الاقتصادیة عبر وسائل الإعلام الرقمی وعلاقته بمستوى الثقة فی الأداء الاقتصادی لدى الجمهور المصری

هدف البحث إلى التعرف على مدى تعرض الجمهور للقضايا الاقتصادية عبر وسائل الإعلام الرقمي وعلاقته بمستوى الثقة في الأداء الاقتصادي لديهم، وتنتمي هذه الدراسة إلى الدراسات الوصفية، وتعتمد الدراسة على منهج المسح، وطبقت الدراسة على عينة قوامها (400) مفردة من أربع محافظات مصرية: (القاهرة، والسويس، والأسکندرية، والمنيا)، وتم الاعتماد على استمارة الاستقصاء کأداة أساسية للدراسة، وکانت أهم النتائج ما يلى: جاءت أبرز الخدمات الاقتصادية التي تقدمها وسائل الإعلام الرقمي من وجهة نظر الجمهور ما يلي: ظهر بمقدمة تلک الخدمات أنهاتنشر إعلانات المناقصات، ثم تقدم ملفات ومعلومات اقتصادية متخصصة، تنشر أخبار قطاع البنوک، ثم تنشر أخبار قطاع شرکات التحويل العقاري، تقدم تغطية مستمرة للسلع والخدمات الرئيسة، ثم تقوم بتغطية المؤتمرات الاقتصادية، بنشر أخبار البورصة، وجاءت أهم مؤشرات الأداء الاقتصادي التي تعرف عليها الجمهور عينة الدراسة من خلال وسائل الإعلام الرقمي ما يلي: جاء تحرر سعر الصرف، انخفاض الواردات، ثم انخفاض معدل البطالة، ارتفاع الصادرات، ثم زيادة الاحتياطات الدولية من النقد الأجنبي، يليها تراجع معدل التضخم السنوي، ثم ارتفاع تدفقات النقد الأجنبي، وأخيرا جاء انخفاض أسعار الفائدة، کما لا يوجد فرق دال إحصائيا بين متوسطات درجات الذکور ومتوسطات درجات الإناث على مقياس تناول وسائل الإعلام الرقمي للأداء الاقتصادي. بينما توجد فروق ذات دلالة إحصائية بين متوسطات درجات المبحوثين على مقياس تناول وسائل الإعلام الرقمي للأداء الاقتصادي تبعا لاختلاف المحافظة (القاهرة- الأسکندرية- المنيا- السويس). Exposure to economic issues via digital media and its relationship The level of confidence of Egyptian public towards economic performance The research sought to determine public's exposure to economic issues through digital media and its relationship to level of confidence in their economic performance. This study belongs to descriptive studies, and study relies on survey methodology. The study was applied to a sample of (400) respondents from four Egyptian governorates (Cairo, Suez, Alexandria, and Minya). The survey form was used as an essential tool for study.The most important results conclude as following: -The most prominent economic services provided by digital media from public’s point of view, following came: introduction of those services appeared that it “publishes tender announcements”, then “provides specialized economic files and information” followed by “publishes news of banking sector” then “publishes news of real estate conversion companies sector It is followed by “continuous coverage of main goods and services” and then “it covers economic conferences” followed by “the exchange of stock market news”.-The most important indicators of economic performance that public became familiar with study sample came through digital media. The following came: “Liberalization of exchange rate” followed by “low imports” then “low unemployment rate” followed by “higher exports” and then “increased international reserves of foreign exchange” Followed by the decline in annual inflation rate and then the recent rise in foreign exchange flows and last one low interest rates. - There are no statistically significant differences between mean scores of males and mean scores of females on scale of digital media intake for economic performance. -There are statistically significant differences between mean scores of respondents on scale of digital media intake of economic performance according to difference in governorate (Cairo - Alexandria - Minya - Suez).

Sustainability ratings and corporate control: Debacle of shareholder over stakeholder theory

Corporate sustainability rating is frequently observed by different stakeholders, thereby finding interest in academic studies as well. Shareholders of sustainable-companies respond to different types of stock market news, be it financial or non-financial news. Announcements relating to ratings obtained by sustainability-compliant businesses appear to create anticipation in the mind of the investor. If these announcements are viewed by investors with interest, then it can have a greater implication for corporate governance and control and the corporate leaders can find a clear path to resolve the much debated “shareholder” versus “stakeholder” view in decision-making. This paper aims to explain whether or not the declaration of sustainability ratings contributes to the stock market reaction in emerging markets such as India. Short-run event analysis was carried out on a set of selected BSE listed companies following sustainable practice (2017-2019) and the entire data set was split into categories of the upgrade, downgrade, no change, and no ratings. The study results show that the announcement of sustainability ratings is not regarded by investors with a great deal of interest and there is inherent indifference to such news in the stock market. These findings are relevant for stock exchanges, investors, and corporate control as it raises a serious issue of rethinking stakeholder awareness levels, which in an emerging economy such as India currently seem to be in a nascent stage. In order to meet the stakeholders interested in the process of business becoming sustainable corporate leaders through proper governance should explore ways and means to approach sustainability in a more systematic way

Accessing the Equity Return Volatility Effect of East and South Asian Nations: The Econometrics Modelling Method

In financial management, the equity market performance is the critical element of equity market returns volatility wherever the shareholder’s resilience around the instability subsists. The data is collected from the authenticated secondary sources for the analysis. This paper shows that the 2008economicpredicament, as well as the effect above proceeding developing financial prudence of the globe, is found in the equity return instability connation of developing financial prudence (2004-2015). By the GARCH model, it can be examined that as the information from the U.S.A. stock market news has an essential consequences on the earnings of the S&P 500 stock market index, the indices of the east, as well as south Asian nations, has also influenced by the news of U.S.A. The GARCH model is estimated for the U.S.A. stock market news has a substantial effect or not on East and South Asian nation's daily share market returns. The outcomes show that market earnings in the equity market in east and south Asian nations are incredibly reliant on their historical earnings. It is found that Tokyo Topic (4.8929) is a highly volatile stock index among the East and South Asian stock returns, and the low volatile stock index is DSEX (0.0068). The news of the U.S.A. stock market has affected the equity market of India, Japan, China, and Korea, which are included in the East and South Asian stock market. In all the country’s share markets, found most significant variance in the equity income instability. This study is essential for the shareholders looking for the diversification in the portfolio, domestic institutional investors and foreign institutional investors

Stock Futures of a Flawed Market Index

I present evidence that transactions of the stock futures of a flawed market index cause mispricing in individual stocks. In particular, I analyze whether stocks overweighted on the index are mispriced, especially when market movements driven by futures trading are observed. To detect such movements, I use a qualitative indicator based on daily stock market news and a quantitative indicator based on the intraday lead-lag relationship between the spot and futures markets. I first find that overweighted stocks experience higher (lower) returns when an upward (downward) market movement driven by futures trading occurs. Second, they experience lower (higher) returns after such periods, i.e., their performance is reversed. These findings suggest that overweighted stocks experience significant trading pressure from the transactions of the index futures, resulting in mispricing of individual stocks. By contrast, such price behavior is not observed for non-constituent stocks. These results strongly support the view that the transactions of stock futures of a flawed index cause mispricing in individual stocks.

Asymmetries in the African financial markets

We investigate the asymmetries in the African financial markets; both stock and exchanges markets, namely Botswana, Egypt, Kenya, Mauritius and South Africa. The dataset begins on January 1, 2001 and ends on January 20, 2018, for a total of 4479 trading days. We apply an asymmetric threshold approach with an error-correction model with four dummy variables for both positive/negative and small/large news. Our findings reveal that the foreign exchange markets in Africa are statistically more prone to larger-scale news. We also found a direct reaction to short-term positive/negative stock market news after the global financial crisis. Furthermore, we investigate the total effects of South African economic news releases with an Asymmetric Power Arch model. Our findings suggest substantial discrepancy, which belong to the Southern African Development Community and Common Market for Eastern and Southern Africa free trade area. SADC stock markets are strongly affected by the economic news releases from South Africa.

Modeling public mood and emotion: Blog and news sentiment and socio-economic phenomena

The development of online virtual communities has raised the importance in analyzing massive volume of text from websites and social networks. This research analyzed financial blogs and online news articles to develop a public mood dynamic prediction model for stock markets, referencing the perspectives of behavioral finance and the characteristics of online financial communities. This research applies big data and opinion mining approaches to the investors’ sentiment analysis in Taiwan. The proposed model was verified using experimental datasets from ChinaTimes.com, cnYES.com, Yahoo stock market news, and Google stock market news over an 18 month period. Empirical results indicate the big data analysis techniques to assess emotional content of commentary on current stock or financial issues can effectively forecast stock price movement.

Is default risk priced equally fast in the credit default swap and the stock markets? AN empirical investigation

We examine whether default risk is priced equally fast in the credit default swap (CDS) and the stock markets in the main economic sectors of North America, Europe, the UK, and Asia. We find significant evidence in all of these regions and economic sectors that the stock market leads the price discovery process because it reflects default risk faster than the CDS market. We also find weak evidence that the documented lead-lag relation is not regime-dependent and that is stronger for negative stock market news. Our findings do not confirm the theoretical prediction that the CDS market responds faster than the stock market to changing credit conditions. Consistent with the market selection theories, our findings imply that informed traders prefer to trade default risk mostly in the stock market but uninformed traders mostly in the CDS market.

Stock Futures of a Flawed Market Index

We present evidence that transactions of the stock futures of a flawed market index cause stock mispricing. In particular, we analyze whether stocks overweighted on the index are mispriced, especially when a market movement driven by futures trading is observed. To detect such a movement, we use two indicators: a qualitative indicator based on daily stock market news and a quantitative indicator based on the intraday lead-lag relationship between the spot and futures markets. We find that overweighted stocks are overpriced (underpriced) when an upward (downward) market movement driven by futures trading is observed. By contrast, such mispricing is not observed for non-constituent stocks.

Exploring the interaction between stock price index and exchange rates: an asymmetric threshold approach

This article examines the impact of stock market news on the foreign exchange markets of USA, Canada and UK, employing an innovative extension of the asymmetric threshold model of Apergis and Miller (2006). Under this framework we can disentangle the reaction of foreign exchange market to bad or good news and small or large news of stock returns. Our comprehensive daily data-set spans the period from January 1990 to June 2014. Using a cointegration and error correction model, we document the existence of a causal relationship between stock market and foreign exchange markets. Most interestingly, our results derived from the asymmetric threshold model confirm that the relationship between stock and foreign exchange markets is sensitive to short-term good or bad news and short-term small or large news. Our findings entail significant implications for policymakers, governments, risk managers and international investors.

A Comprehensive Review of Sentiment Analysis of Stocks

This paper comprehensively studies the sentiment analysis of stock market news and explains the maturity of sentiment analysis in the stock market scenario. I explain in its entirety the procedure, difficulties, and limitations concerning sentiment analysis of financial news to predict stocks. The stock market movements are regarded as highly unpredictable and a large number of factors contribute to that unpredictability. Factors such as market sentiment, government policies and company announcements are some of the major contributing factors however the list is not exhaustive. Technological advancements over the past two decades has enabled researchers and market professionals to develop mathematical models to optimize their returns and keep the risk in check. These advancements have given way to social media platforms especially Twitter to more conveniently express opinions and reviews. Narrowing it only to the stock market and financial scenario, Twitter is an attractive platform for the user community to discuss company health, company announcements, major news, and government policies etc to name a few. Companies or organizations in turn also boast their success on Twitter. This entire process of sharing news and opinions yields a large amount of financial data to be searched for an overall sentiment or a possible prediction of the stocks. This paper attempts to dive deep into the specifics of procedure, the current stage of its maturity, and the importance of Machine Learning in finance and the stock market.

What Types of Macroeconomic Announcements Affect Stock Markets in Emerging Eastern Europe?

This study considers the impact of foreign and local macroeconomic announcements on emerging Eastern European stock markets. Stock market and macroeconomic news from 2006I 2010 for Russia, Poland, Hungary, and the Czech Republic are analyzed for differences across countries and to see if foreign macroeconomic announcements have more impact than local macroeconomic news on stocks. We find a direct linkage of stock markets and macroeconomic announcements, as well as evidence of integration among the stock markets in emerging Eastern Europe. Macroeconomic news appears to affect local market volatility, and in rare instances stock returns themselves. We also find bad news has a leverage effect for emerging Eastern European stock markets, generating greater volatility in the stock market than positive news. The results have implications for asset pricing and portfolio selection for international financial institutions and portfolio managers assessing their investment decisions in light of macroeconomic news releases.

Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information

The interaction between stock price process and market news has been widely analyzed by investors on different markets. Previous works, however, focus either on market news purely as exogenous factors that tend to lead price process or on the analysis of how past stock price process can affect future stock returns. To take a step forward, we quantitatively integrate information from both market news and stock prices in order to improve the accuracy of prediction on stock future price return in an intra-day trading context. In this paper, we present the design and architecture of our approach for market information fusion. By means of multiple kernel learning, the hidden information behind the two sources is effectively extracted, and more importantly, seamlessly integrated rather than simply combined by a single kernel approach. Experiments on comprehensive comparisons between our approach and three baseline methods (which use only one type of information, or naively combine the two sources) have been conducted on the intra-day tick-by-tick data of the Hong Kong Stock Exchange and market news archives of the same period. It has been shown that for both cross-validation and independent testing, our approach is able to achieve the best results.

An Analysis of Short-Term Overreaction to Stock Market News: Iranian Evidence

In Financial markets information is in the form of signs, news and different predictions coming from inside or outside of the company which makes reactions and as a result changes in stock prices. Such as increase and decrease over the limit or long period of times. However, this behavior is non-rational behavior of market which can be a rational response to perceived uncertainty that is understood by investors. We investigate the stock market short –term overreaction. The Aim of this project is to analyze overreaction of investors to news and information includes Earnings announcement (Pleasant or unpleasant) in Bear and Bull Market in Tehran Stock exchange (TSE) Using daily data over the period 2005-2011 . Over the window of 14 days before and 14 days after news announcements. The result indicates that investors in Tehran Stock exchange did not have over reaction to information in short time period.

Using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock market news

Sentiment classification of stock market news involves identifying positive and negative news articles, and is an emerging technique for making stock trend predictions which can facilitate investor...