A classification-based approach to economic event detection in Dutch news text

Breaking news on economic events such as stock splits or mergers and acquisitions has been shown to have a substantial impact on the financial markets. As it is important to be able to automatically identify events in news items accurately and in a timely manner, we present in this paper proof-of-concept experiments for a supervised machine learning approach to economic event detection in newswire text. For this purpose, we created a corpus of Dutch financial news articles in which 10 types of company-specific economic events were annotated. We trained classifiers using various lexical, syntactic and semantic features. We obtain good results based on a basic set of shallow features, thus showing that this method is a viable approach for economic event detection in news text

Semantics-based information extraction for detecting economic events

As today's financial markets are sensitive to breaking news on economic events, accurate and timely automatic identification of events in news items is crucial. Unstructured news items originating from many heterogeneous sources have to be mined in order to extract knowledge useful for guiding decision making processes. Hence, we propose the Semantics-Based Pipeline for Economic Event Detection (SPEED), focusing on extracting financial events from news articles and annotating these with meta-data at a speed that enables real-time use. In our implementation, we use some components of an existing framework as well as new components, e.g., a high-performance Ontology Gazetteer, a Word Group Look-Up component, a Word Sense Disambiguator, and components for detecting economic events. Through their interaction with a domain-specific ontology, our novel, semantically enabled components constitute a feedback loop which fosters future reuse of acquired knowledge in the event detection process

When humans using the IT artifact becomes IT using the human artifact

Following Demetis & Lee (2016) who showed how systems theorizing can be conducted on the basis of a few systems principles, in this conceptual paper, we apply these principles to theorize about the systemic character of technology and investigate the role reversal in the relationship between humans and technology. By applying systems-theoretical requirements outlined by Demetis & Lee, we examine conditions for the systemic character of technology and, based on our theoretical discussion, we argue that humans can now be considered artifacts shaped and used by the (system of) technology rather than vice versa. We argue that the role reversal has considerable implications for the field of information systems that has thus far focused only on the use of the IT artifact by humans. We illustrate these ideas with empirical material from a well-known case from the financial markets: the collapse (“Flash Crash”) of the Dow Jones Industrial Average

When Humans Using the IT Artifact Becomes IT Using the Human Artifact

Following Lee & Demetis [20] who showed how systems theorizing can be conducted on the basis of a few systems principles, in this paper, we apply these principles to theorize about the systemic character of technology and investigate the role-reversal in the relationship between humans and technology. By applying systems-theoretical requirements outlined by Lee & Demetis, we examine conditions for the systemic character of technology and, based on our theoretical discussion, we argue that humans can now be considered artifacts shaped and used by the (system of) technology rather than vice versa. We argue that the role-reversal has considerable implications for the field of information systems that has thus far focused only on the use of the IT artifact by humans. We illustrate these ideas with empirical material from a well known case from the financial markets: the collapse (“Flash Crash”) of the Dow Jones Industrial Average

Towards Building a Knowledge Base of Monetary Transactions from a News Collection

We address the problem of extracting structured representations of economic events from a large corpus of news articles, using a combination of natural language processing and machine learning techniques. The developed techniques allow for semi-automatic population of a financial knowledge base, which, in turn, may be used to support a range of data mining and exploration tasks. The key challenge we face in this domain is that the same event is often reported multiple times, with varying correctness of details. We address this challenge by first collecting all information pertinent to a given event from the entire corpus, then considering all possible representations of the event, and finally, using a supervised learning method, to rank these representations by the associated confidence scores. A main innovative element of our approach is that it jointly extracts and stores all attributes of the event as a single representation (quintuple). Using a purpose-built test set we demonstrate that our supervised learning approach can achieve 25% improvement in F1-score over baseline methods that consider the earliest, the latest or the most frequent reporting of the event.Comment: Proceedings of the 17th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '17), 201

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

Impersonal efficiency and the dangers of a fully automated securities exchange

This report identifies impersonal efficiency as a driver of market automation during the past four decades, and speculates about the future problems it might pose. The ideology of impersonal efficiency is rooted in a mistrust of financial intermediaries such as floor brokers and specialists. Impersonal efficiency has guided the development of market automation towards transparency and impersonality, at the expense of human trading floors. The result has been an erosion of the informal norms and human judgment that characterize less anonymous markets. We call impersonal efficiency an ideology because we do not think that impersonal markets are always superior to markets built on social ties. This report traces the historical origins of this ideology, considers the problems it has already created in the recent Flash Crash of 2010, and asks what potential risks it might pose in the future

FIDE Congress 2020 - EU Competition Law and the Digital Economy: United Kingdom Report

This report was prepared for the 29th biennial Congress of the International Federation of European Law (FIDE) to be held in The Hague in May 2020. It is the national report for the United Kingdom in response to Topic 3 of the 2020 FIDE Congress, titled ‘EU Competition Law and the Digital Economy’. This report offers an overview of UK competition enforcement in digital economy markets by answering twelve questions organised into four sections. Part A summarises key UK antitrust and merger decisions, agency publications, priorities and goals of enforcement in digital economy markets. Part B focuses upon the definition of markets and conceptualisation of market power by UK authorities in digital economy cases in light of their challenges and particularities. Part C offers a detailed overview of the issues underpinning UK antitrust and merger scrutiny in this field: the types of conduct investigated, relevant factors and concepts, theories of harm, efficiency justifications and remedies in digital economy cases. Finally, Part D identifies the potential for incoherent enforcement in this field from two different sources: the overlap between UK competition law and ex ante regulatory regimes (e.g. consumer protection, data protection); and the overlap between the powers of various UK competition decision-makers (e.g. sectoral regulators, the Competition Appeal Tribunal, and the courts)

Automated Trading Systems VS Manual Trading in Forex Exchange Market

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementIn the recent decades, automated trading has been widely used in Forex and Money Markets, as well as in financial markets. This auto trading provided substantial benefits to transaction efficiency. Many trading robots have been created to substitute humans, capable of simulating trading strategies and continuously making profits. Nevertheless, programs cannot reproduce all human behaviour and most robots are over-sensitive, therefore, it is difficult to have the same results as human traders. The study focuses on evaluating the trading machines sensitivity and effectiveness. The economic markets can benefit from the machine in several ways, through continuous operation, increasing diversification, short/term trading opportunities and by forecasting opportunities e. g. currency price changes. The further investigation indicates that the majority of forex trading robots are profitable, in fact, there is a great tendency for curve-fitting or data-mining. There are some impressive robots out there; of course, these systems maintain an advantage and successfully manage risk. The best ones are more about position sizing and cutting losses quickly and less about high win rates. The greater the sensitivity the greater the trading opportunities, but this decreases the performance. This research will contain interviews with experts that will validate the study

Artificial Intelligence & Machine Learning in Finance: A literature review

In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical Researc