Investigating Online Financial Misinformation and Its Consequences: A Computational Perspective

The rapid dissemination of information through digital platforms has revolutionized the way we access and consume news and information, particularly in the realm of finance. However, this digital age has also given rise to an alarming proliferation of financial misinformation, which can have detrimental effects on individuals, markets, and the overall economy. This research paper aims to provide a comprehensive survey of online financial misinformation, including its types, sources, and impacts. We first discuss the characteristics and manifestations of financial misinformation, encompassing false claims and misleading content. We explore various case studies that illustrate the detrimental consequences of financial misinformation on the economy. Finally, we highlight the potential impact and implications of detecting financial misinformation. Early detection and mitigation strategies can help protect investors, enhance market transparency, and preserve financial stability. We emphasize the importance of greater awareness, education, and regulation to address the issue of online financial misinformation and safeguard individuals and businesses from its harmful effects. In conclusion, this research paper sheds light on the pervasive issue of online financial misinformation and its wide-ranging consequences. By understanding the types, sources, and impacts of misinformation, stakeholders can work towards implementing effective detection and prevention measures to foster a more informed and resilient financial ecosystem.Comment: 32 pages, 2 figure

Stock Prediction Based on Social Media Data via Sentiment Analysis: a Study on Reddit

With the development of internet and information technology, online text data has become available and accessible for research in many fields including stock prediction. Social media, being one of the biggest content generators on the internet, is a great data resource for text mining and stock prediction. It has a large capacity, high data density, and fast information spread. In this thesis, analyses on the relationship between the stock-related text in social media (Reddit) and the price changes of corresponding stocks are implemented. In the analysis, sentiment analysis is first applied to extract the individual users’ emotions and opinions about the stocks. After that, the extracted features are analyzed via descriptive statistics and predictive analysis using the Pearson correlation coefficient and machine learning models. The predictive analysis is designed to examine the dependence between the social media text data and stock price change by evaluating the performance of predictions, four indicators are used in the evaluation including “prediction accuracy on price change direction” and three indicators in simulated algorithm trading experiments based on prediction results. They are “total profit with trading strategy for single stock”, “daily profit efficiency of trading strategy” and “total profit with Portfolio trading strategy”. From the results and the comparison with a Buy and Hold (B&H) baseline strategy, the predictions show good results in terms of “daily profit efficiency” and “total profit with Portfolio trading strategy”. Therefore, the online forum text from Reddit are proved to be correlated with future stock price changes and might be used to make more profit than B&H strategy by incorporating their information in portfolio trading strategies

Research on Automatic Identification of Rumors in Stock Forum Based on Machine Learning

When rumors prevail in securities market, it is very difficult for investors to identify valid information. In the meantime, investors have much more ways to access information with the evolution of internet. But there is an overwhelming quantity of information on the Internet, the coexistence of facts and rumors, namely, “widely circulated” and “specious”, yet “unconfirmed officially” vague information, makes it more difficult for investors who with limited rationality to distinguish facts from rumors. Existing studies are mainly devoted in the method of event study, namely screening rumors from “official channels” that clarified, which is neither timely efficient in terms of accessing to rumors nor providing the basis for decision-making. Traditional news has evolved into various forms of social media, including forums, blogs, micro-blogs etc., and users can not only gain quick access to more valuable and timely information, but also amplify information that embed the news effectively by participating in commenting on various social media. Dynamic information creation, sharing and coordination among Web users are exerting increasingly prominent impact on the securities market in now days. Thus, it is very necessary to study the effects of social media as online forums on the securities market. In this paper, the method of machine learning is adopted for the first time to identifying the Internet rumors automatically, and successfully in crawling massive forum data by smart computer technology. Unlike the case study and statistical sampling of rumors, this paper conduct automatic identification of Internet rumors by utilize the smart technology, thus paving the way for more in-depth analysis about the effects of Internet media on the securities market in future

Ethical Implications of Predictive Risk Intelligence

open access articleThis paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. The pa-per covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews. Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelli-gence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as so-cial media sites. Also, there are issues relating to the transparency and accountabil-ity of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In re-sponse to these issues, the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Re-sponsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees. This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society