A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Stock market analysis plays an indispensable role in gaining knowledge about the stock market, developing trading strategies, and determining the intrinsic value of stocks. Nevertheless, predicting stock trends remains extremely difficult due to a variety of influencing factors, volatile market news, and sentiments. In this study, we present a hybrid data analytics framework that integrates convolutional neural networks and bidirectional long short-term memory (CNN-BiLSTM) to evaluate the impact of convergence of news events and sentiment trends with quantitative financial data on predicting stock trends. We evaluated the proposed framework using two case studies from the real estate and communications sectors based on data collected from the Dubai Financial Market (DFM) between 1 January 2020 and 1 December 2021. The results show that combining news events and sentiment trends with quantitative financial data improves the accuracy of predicting stock trends. Compared to benchmarked machine learning models, CNN-BiLSTM offers an improvement of 11.6% in real estate and 25.6% in communications when news events and sentiment trends are combined. This study provides several theoretical and practical implications for further research on contextual factors that influence the prediction and analysis of stock trends.

Event Study: Advanced Machine Learning and Statistical Technique for Analyzing Sustainability in Banking Stocks

Machine learning has grown in popularity in recent years as a method for evaluating financial text data, with promising results in stock price projection from financial news. Various research has looked at the relationship between news events and stock prices, but there is little evidence on how different sentiments (negative, neutral, and positive) of such events impact the performance of stocks or indices in comparison to benchmark indices. The goal of this paper is to analyze how a specific banking news event (such as a fraud or a bank merger) and other co-related news events (such as government policies or national elections), as well as the framing of both the news event and news-event sentiment, impair the formation of the respective bank’s stock and the banking index, i.e., Bank Nifty, in Indian stock markets over time. The task is achieved through three phases. In the first phase, we extract the banking and other co-related news events from the pool of financial news. The news events are further categorized into negative, positive, and neutral sentiments in the second phase. This study covers the third phase of our research work, where we analyze the impact of news events concerning sentiments or linguistics in the price movement of the respective bank’s stock, identified or recognized from these news events, against benchmark index Bank Nifty and the banking index against benchmark index Nifty50 for the short to long term. For the short term, we analyzed the movement of banking stock or index to benchmark index in terms of CARs (cumulative abnormal returns) surrounding the publication day (termed as D) of the news event in the event windows of (−1,D), (D,1), (−1,1), (D,5), (−5,−1), and (−5,5). For the long term, we analyzed the movement of banking stock or index to benchmark index in the event windows of (D,30), (−30,−1), (−30,30), (D,60), (−60,−1), and (−60,60). We explore the deep learning model, bidirectional encoder representations from transformers, and statistical method CAPM for this research.

Why would you buy an electric car on Jetski Friday? Or, a critique of financial markets from an options trading room

This article presents a close, dialogue-based ethnographic account of a group of contemporary options market makers making a decision about pricing options in Tesla, Inc. Careful attention to their deliberations reveals how the rise of algorithms and automation on financial markets have rendered traders alienated and estranged from the markets they work on for their livelihood. This alienation arises, in part, due to novel cascade effects between futures and underlying equities, which algorithmic and automated trading seems to afford, and which also relate to news events as well as the actions of politicians and prominent business people. Emerging from this alienation, traders produce a critique of how highly automated financial markets allocate capital and how ripe they are for political manipulation.

Rethinking International News Translations: Toward a Foreignizing Approach to News Events Translations

The present paper investigates the ideological manipulation that creeps in translated news headlines and falsifies the produced translated version since such process involves both the imposition of dominant ideologies and the negative portrayals of the other in mediated news. Thus, international news translation basically exploits and manipulates the original news events in such a way that misrepresents the image of otherness and creates a positive representation of patrons. In this vein, this paper brings to the fore the influence of extra-textual factors on the translation of headlines. Many strategies and translation techniques are utilized and translators do intervene to align produced headlines with the two networks’ ideological affiliations and editorial policies. The present paper adopts descriptive approach where I attempted to compare translated news headlines and pinpoint the alterations and transformations undertaken over them, also it aims to call for rethinking strategies undertaken while translating global news in a cosmopolitan context where openness to the other and appreciation of difference are conducive to an effective cross cultural and linguistic interactions. Accordingly, it proposes foreignizing approach to global news translation because it retains the image of otherness which is essential in the original event.

Vaping-related news events and their relationship with sentiment in the online vaping environment: A computational interrupted time series analysis with large-scale public data

Background: Vaping-related news coverage may have furthered misconceptions around the relative harms of vapes. Also, some positive opinions around vaping may be derived from misinformation, perhaps creating inimical health outcomes. Thus, we need to study how vaping-related news events (e.g. 2019 vaping illness epidemic, COVID-19) are associated with sentiment in the online vaping environment, to better understand how to promote vaping as a potential harm reduction technique for those who smoke and are unable to quit, and to minimize vape-centric misinformation that could lead to reduced health outcomes. Methods: We obtained vaping-related online data through web-scraping several online environments from August 1 2019 - April 21 2020. Sentiment analysis was performed to understand changes in sentiment in the online vaping environment in relation to vaping-related events, such as the Trump administration's planned ban on flavored vaping products, and when COVID-19 was first reported to the WHO. Results: For all online environments, we observed a statistically significant negative association of 15% (Estimate: -0.16; 95% CI: -0.29, -0.03; P: 0.01) between sentiment score and the Trump administration's move towards a ban on flavored vaping products, and a statistically significant positive association of 7% between sentiment score (Estimate: 0.07; 95% CI: 0.01, 0.14; P: 0.02) and when COVID-19 was first reported to the WHO (December 31 2019). Conclusions: News events may be related to sentiment in the online vaping environment, depending on the event. Depending on the nature of the event, we suggest that public health messaging may improve health outcomes.

News Events and Their Relationship With US Vape Sales: An Interrupted Time Series Analysis

Objective News coverage around vaping-related events may have furthered misconceptions regarding the relative harms of vapes. Such information may influence the decisions of individuals who smoke, around switching to vaping, potentially affecting the overall tobacco mortality burden. Thus, it is prudent to study how news events (e.g., 2019 vaping illness epidemic) are associated with vape sales in the United States, to possibly reduce the tobacco mortality burden. Methods We used weekly retail sales data for e-cigarettes (30 December 2018 - 28 December 2019) from the US retail scanner data compiled by the Nielsen Company. We used an interrupted time series design with segmented regression analysis to determine immediate and longer-term impacts of individual news events (e.g. Trump administration's planned ban on flavored vaping products) on vape sales, controlling for pre-existing trends.Results Unexpectedly, we noted a statistically significant positive relationship between vape sales and the CDC announcing an investigation into vaping-related illnesses (Change: 6.59%, Estimate: 0.064; 95% CI: 0.036, 0.092; P<0.001). We also observed a similar positive association between vape sales and the CDC's announcement on the link between Vitamin E acetate and EVALI (Change: 2.93%, Estimate: 0.029; 95% CI: 0.003, 0.055; P<0.05). There was a steep decline in sales after these events.ConclusionsNews events are associated with US vape sales. Findings have implications for the management of risk perceptions around vaping to improve health outcomes of tobacco users. Information-based policy instruments can be applied to balance the effects of news events that may influence vape sales.