Realizing EDGAR: eliminating information asymmetries through artificial intelligence analysis of SEC filings

The U.S. Securities and Exchange Commission (SEC) maintains a publicly-accessible database of all required filings of all publicly traded companies. Known as EDGAR (Electronic Data Gathering, Analysis, and Retrieval), this database contains documents ranging from annual reports of major companies to personal disclosures of senior managers. However, the common user and particularly the retail investor are overwhelmed by the deluge of information, not empowered. EDGAR as it currently functions entrenches the information asymmetry between these retail investors and the large financial institutions with which they often trade. With substantial research staffs and budgets coupled to an industry standard of “playing both sides” of a transaction, these investors “in the know” lead price fluctuations while others must follow. In general, this thesis applies recent technological advancements to the development of software tools that will derive valuable insights from EDGAR documents in an efficient time period. While numerous such commercial products currently exist, all come with significant price tags and many still rely on significant human involvement in deriving such insights. Recent years, however, have seen an explosion in the fields of Machine Learning (ML) and Natural Language Processing (NLP), which show promise in automating many of these functions with greater efficiency. ML aims to develop software which learns parameters from large datasets as opposed to traditional software which merely applies a programmer’s logic. NLP aims to read, understand, and generate language naturally, an area where recent ML advancements have proven particularly adept. Specifically, this thesis serves as an exploratory study in applying recent advancements in ML and NLP to the vast range of documents contained in the EDGAR database. While algorithms will likely never replace the hordes of research analysts that now saturate securities markets nor the advantages that accrue to large and diverse trading desks, they do hold the potential to provide small yet significant insights at little cost. This study first examines methods for document acquisition from EDGAR with a focus on a baseline efficiency sufficient for the real-time trading needs of market participants. Next, it applies recent advancements in ML and NLP, specifically recurrent neural networks, to the task of standardizing financial statements across different filers. Finally, the conclusion contextualizes these findings in an environment of continued technological and commercial evolution

AI and blockchain adoption in corporate governance

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonPurpose The purpose of this doctoral thesis sets out to explore and elaborate on the impact of artificial intelligence (AI) and blockchain adoption in corporate governance from ethical perspectives. Positioned within the corporate governance domain, this study adopts an explicit business perspective to study corporate governance change with emerging AI and blockchain technological tools in general and focuses on the ethical use of technologies specifically. As such, this empirical investigation aims to help organizations understand the ethical benefits and ethical dilemmas of using AI and blockchain in businesses and draw plans on how to govern these technologies ethically for the benefit of the business and society. Design/Methodology/Approach: This study adopts specific techniques and a pragmatic, step-by-step netnography approach to investigate online traces from social media sites and extends these online explorations with online semi-structured interviews. The research design of this investigation follows step-by-step procedures that are methodologically sound to ensure rigor in this investigation to enhance the trustworthiness of this study. In total, this research collects an abundance of data: 34 LinkedIn Posts with Comments; 12 Webinars; 22 YouTube Videos; 19 Videos; 10 Podcasts, and 17 semi-structured interview videos. The video, audio, and interview data have been transcribed into textual data total of 453065 words for thematic analysis using NVivo software. Enough time has been allocated to the iterative process of data collection and data analysis. The analysis moves back and forth to the point when theoretical saturation is achieved. The data structure extracts from data in this study illustrate the analytic claims that match the analysis and data together, to ensure a good fit between described method and reported analysis are consistent. Findings: This study develops a thematic framework that constitutes the corporate governance transformation with the ethical use of AI and blockchain technology. This framework provides a holistic understanding of why corporate governance needs to change, especially with the emergence of blockchain and AI technologies, what changes will corporate governance encounter, and how corporate governance can imperatively respond to the ethical use of these technologies. Specifically, it explicitly provides comprehensive understanding of the ethical benefits and ethical concerns of using AI and blockchain technologies in corporate governance, and reveals how companies can govern the use of these technologies ethically. In general terms, the findings of this study support the notion of corporate governance change to transform business models and processes to leverage the new capabilities of AI and blockchain technologies, to priories creativity, speed, and accountability, to replace the old business model, to foster agile or collaborative governance to deal with uncertainty, agility, adaptiveness, and cooperation in the digital world, to foster a network and platform strategies to drive success. This study goes beyond the extant corporate governance scholarship to assess the technological impact to capture values for companies in ethical ways to sustain future growth. Additionally, the notion of corporate governance is further specified and significantly expanded by this study to assess the adoption of AI and blockchain as new corporate governance tools or mechanisms, to enhance ethical benefits when used properly, and mitigate ethical dilemmas with proper checks and balances, safeguards in place, to help organizations stay relevant in this digital transformation and be ethical and sustainable. This study empirically corroborates that in theory, the use of blockchain and AI can enhance ethical practice by detecting fraud and anomaly activities, due to the unique capabilities of blockchain and AI technologies. Further, this research adds depth and specificity by identifying the ethical concerns of using blockchain and AI in corporate governance. The study empirically reveals the ethical concerns of privacy issues, unethical use of data, job transformation and replacement, and algorithm bias that companies will encounter when they use these technologies. In addition, the findings of this study suggest how companies can ethically govern the use of these technologies in socially responsible ways as they transform digitally. Originality/Value: The emergent thematic framework is constructed from the empirical and analytical procedures specifically and purposely designed for this study. This study makes theoretical contributions to knowledge and enriches the extant works of literature, and also provides practical contributions to the ethical use of disruptive technologies, future workforce, and regulations. However, the study was conducted within certain theoretical, methodological, empirical, and pragmatic conditions, which might constitute particular limitations and constraints. Therefore, the last section of this thesis elucidates and suggests the directions for future research

mHealth Research Applied to Regulated and Unregulated Behavioral Health Sciences

Behavioral scientists are developing new methods and frameworks that leverage mobile health technologies to optimize individual level behavior change. Pervasive sensors and mobile apps allow researchers to passively observe human behaviors “in the wild” 24/7 which supports delivery of personalized interventions in the real-world environment. This is all possible because these technologies contain an incredible array of sensors that allow applications to constantly record user location and can contextualize current environmental conditions through barometers, thermometers, and ambient light sensors and can also capture audio and video of the user and their surroundings through multiple integrated high-definition cameras and microphones. These tools are a game changer in behavioral health research and, not surprisingly, introduce new ethical, regulatory/legal and social implications described in this article

State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work

Ethical Testing in the Real World: Evaluating Physical Testing of Adversarial Machine Learning

This paper critically assesses the adequacy and representativeness of physical domain testing for various adversarial machine learning (ML) attacks against computer vision systems involving human subjects. Many papers that deploy such attacks characterize themselves as "real world." Despite this framing, however, we found the physical or real-world testing conducted was minimal, provided few details about testing subjects and was often conducted as an afterthought or demonstration. Adversarial ML research without representative trials or testing is an ethical, scientific, and health/safety issue that can cause real harms. We introduce the problem and our methodology, and then critique the physical domain testing methodologies employed by papers in the field. We then explore various barriers to more inclusive physical testing in adversarial ML and offer recommendations to improve such testing notwithstanding these challenges.Comment: Accepted to NeurIPS 2020 Workshop on Dataset Curation and Security; Also accepted at Navigating the Broader Impacts of AI Research Workshop. All authors contributed equally. The list of authors is arranged alphabeticall