2018 SDSU Data Science Symposium Program

Table of Contents: Letter from SDSU PresidentLetter from SDSU Department of Mathematics and Statistics Dept. HeadSponsorsGeneral InformationKeynote SpeakersInvited SpeakersSunday ScheduleWorkshop InformationMonday ScheduleAbstracts| Invited SpeakersAbstracts | Oral PresentationsPoster PresentationCommittee and Volunteer

Using dynamic semantic structure of news flow to enhance financial forecasting: a twelve-year study on twitter news channels

This research holds significance for advancing financial forecasting methodologies by shifting the focus from traditional sentiment analysis of individual tweets to exploring intricate semantic relationships within news tweets from top-followed news channels on Twitter. Addressing a notable research gap in financial forecasting, often dominated by sentiment analysis, our study endeavors to fill the void left by the underexplored intricate relationships within news entities and their dynamic semantic evolution. Motivated by the inherent challenges in predicting the random walk behavior of stock prices, we contend that incorporating longitudinal data derived from the semantic relationships between news entities can enhance the accuracy of stock market forecasts. The study pioneers a twelve-year exploration, encompassing data from 55 leading news channels on Twitter, boasting a collective following of 714 million users. The approach employs natural language processing (NLP) to extract two million unique entities, whose semantics are analyzed through complex network analysis, laying the foundation for the forecasting model. Finally, this research introduces a model linked to the dynamic semantic structure of news flow. The predictive model considers the impact of exogenous variables influenced by the evolving relationships among news entities. The results offer a proof of concept, highlighting the potential of utilizing dynamic semantic relationships among news entities for financial prediction. On average, the model demonstrates an improvement in accuracy of 40.3% across ten different stock price predictions. These findings are expounded through relevant theories, offering a theoretical foundation for observed patterns and indicating a promising direction for future research in this domain

Artificial Intelligence for Sustainability—A Systematic Review of Information Systems Literature

The booming adoption of Artificial Intelligence (AI) likewise poses benefits and challenges. In this paper, we particularly focus on the bright side of AI and its promising potential to face our society’s grand challenges. Given this potential, different studies have already conducted valuable work by conceptualizing specific facets of AI and sustainability, including reviews on AI and Information Systems (IS) research or AI and business values. Nonetheless, there is still little holistic knowledge at the intersection of IS, AI, and sustainability. This is problematic because the IS discipline, with its socio-technical nature, has the ability to integrate perspectives beyond the currently dominant technological one as well as can advance both theory and the development of purposeful artifacts. To bridge this gap, we disclose how IS research currently makes use of AI to boost sustainable development. Based on a systematically collected corpus of 95 articles, we examine sustainability goals, data inputs, technologies and algorithms, and evaluation approaches that coin the current state of the art within the IS discipline. This comprehensive overview enables us to make more informed investments (e.g., policy and practice) as well as to discuss blind spots and possible directions for future research

The AI Revolution: Opportunities and Challenges for the Finance Sector

This report examines Artificial Intelligence (AI) in the financial sector, outlining its potential to revolutionise the industry and identify its challenges. It underscores the criticality of a well-rounded understanding of AI, its capabilities, and its implications to effectively leverage its potential while mitigating associated risks. The potential of AI potential extends from augmenting existing operations to paving the way for novel applications in the finance sector. The application of AI in the financial sector is transforming the industry. Its use spans areas from customer service enhancements, fraud detection, and risk management to credit assessments and high-frequency trading. However, along with these benefits, AI also presents several challenges. These include issues related to transparency, interpretability, fairness, accountability, and trustworthiness. The use of AI in the financial sector further raises critical questions about data privacy and security. A further issue identified in this report is the systemic risk that AI can introduce to the financial sector. Being prone to errors, AI can exacerbate existing systemic risks, potentially leading to financial crises. Regulation is crucial to harnessing the benefits of AI while mitigating its potential risks. Despite the global recognition of this need, there remains a lack of clear guidelines or legislation for AI use in finance. This report discusses key principles that could guide the formation of effective AI regulation in the financial sector, including the need for a risk-based approach, the inclusion of ethical considerations, and the importance of maintaining a balance between innovation and consumer protection. The report provides recommendations for academia, the finance industry, and regulators

How does artificial intelligence adoption differ across the consulting, banking, and human resources sectors? - The case of artificial intelligence in Banco De Portugal

This paper provides a comparative analysis of Artificial Intelligence (AI) adoption within the industries of Consulting, Banking, and Human Resources. Using the authors' professional experiences in Accenture, KPMG, Banco de Portugal, and Mercer, this paper examines the adoption and impacts of AI in these specific companies. It explores how these entities leverage AI to enhance services, detailing individual contributions and collaborative innovations. The research also assesses the impact on efficiency and client engagement and discusses the strategic, ethical, and operational challenges encountered

Textual Information in Analyst Reports and its Value for Decision Support

Das Berufsbild der Finanzanalysten schließt Fähigkeiten ein, die auch durch die fortschrittlichsten Analyseverfahren nicht ersetzt werden können. Vielmehr schaffen die fortschreitende Globalisierung und die zunehmende Verflechtung der internationalen Finanzmärkte ein neues Maß an Komplexität, das in dieser Ausprägung vor wenigen Jahrzehnten nicht gegeben war. Die vorliegende Arbeit widmet sich der verbesserten Aufbereitung und Nutzung von in Textform verfügbaren Analystenmeinungen sowie der Verwendung dieser Daten für die Entscheidungsunterstützung. Dazu wird eine pragmatische und nutzerorientierte Sichtweise eingenommen, die in der bisherigen Forschung zu grundlegenden Zusammenhängen zumeist im Hintergrund blieb. Diese Arbeit zielt zudem darauf ab, erfolgreich Informationen aus Analystenberichten in konkreten Anwendungsfällen einzusetzen. Ein spezielles Augenmerk wird dabei darauf gelegt, in welchen Konstellationen Analystenberichte mit Bedacht zu lesen sind und welche analystenspezifischen Merkmale bei der Entscheidungsfindung zu berücksichtigen sind. Vor dem Hintergrund der skizzierten Ziele gliedert sich die Arbeit in drei Forschungsbereiche: Methodische Grundlagen, Verhaltensmuster von Finanzanalysten sowie Analystenberichte und Finanzmärkte. Die ersten zwei Forschungsbereiche dienen als Grundgerüst. Sie umfassen zum einen ausgewählte methodische Ansätze des Text Mining und zum anderen in qualitativen Analystendaten sichtbar werdende Verhaltensmuster. Der erste Forschungsbereich, die methodischen Grundlagen, umfasst ein Rahmenwerk, das verschiedene Ansätze zur strukturierten Darstellung von Dokumenten abbildet und als Orientierungshilfe für die Durchführung von Text-Mining-Aufgaben in verschiedenen Anwendungsbereichen dienen kann. Darüber hinaus wird ein Sentiment-Wörterbuch entwickelt, welches durch Finanzanalysten erstellte Texte domänenspezifisch analysiert. Der zweite Forschungsbereich, Verhaltensmuster von Finanzanalysten, widmet sich der Erkennung von Herdenverhalten unter Finanzanalysten durch Anwendung von Topic Mining. Ferner wird ein in Analystenberichten besonders ausgeprägter sprachlicher Unterschied zu weiteren finanzspezifischen Texten veranschaulicht. Der dritte Forschungsbereich, Analystenberichte und Finanzmärkte, baut auf den vorangegangenen Bereichen auf und demonstriert die praktische Anwendung, indem die Implementierung einer Anlagestrategie und ein Risikomanagementansatz auf der Grundlage von Analystentexten dargestellt werden. Insgesamt spannen die drei Forschungsbereiche dieser Arbeit einen Bogen von der Betrachtung methodischer Aspekte und dem Verständnis der Besonderheiten von Analystentexten bis hin zu einer Vorstellung relevanter praktischer Anwendungen. In ihrer Kombination zielen die einzelnen Studien darauf ab, zu einem besseren Verständnis des Potenzials qualitativer Analysteninformationen beizutragen und diese Informationen als sinnvollen Bestandteil von Entscheidungsunterstützungssystemen herauszustellen.The profession of financial analysts requires skills that cannot be replaced even by today’s most cutting-edge analytical tools. Instead, ongoing globalization and the increasing interconnectedness of international financial markets are creating new levels of complexity that did not exist even a few decades ago. Therefore, this thesis is dedicated to the improved processing and use of textual analyst opinion and promoting the value of this data for decision support. The intention is to take a pragmatic and user-oriented view, which remained in the background of previous research on fundamental relationships. Specifically, this thesis aims to successfully apply the signals from analyst reports in actual use cases. Here, special attention is paid to the settings in which analyst reports should be read with caution and which analyst-specific characteristics should be considered in the decision-making process. In light of the outlined objectives, the thesis is divided into three research areas: methodological foundations, behavioral patterns of financial analysts, and analyst reports and financial markets. The first two research areas serve as a basis. They cover selected approaches in text mining on the one hand and typical behavioral patterns visible in qualitative analyst content on the other hand. The first research area, methodological foundations, proposes a framework that includes different approaches to document representation, providing a guideline for conducting text mining tasks in various domains. Additionally, a sentiment dictionary is developed that improves the domain-specific analysis of analyst content. The second research area, behavioral patterns of financial analysts, addresses the detection of herding behavior among financial analysts by applying topic mining. Furthermore, a particularly pronounced linguistic distinction between analyst reports and other finance-specific texts is illustrated. The third research area, analyst reports and financial markets, builds on the previous two areas and displays the practical application of textual analyst reports by showcasing the implementation of an investment strategy and a risk management approach based on textual analyst reports. Overall, the three research areas in this thesis span from considering methodological aspects and understanding the distinctive characteristics of texts in analyst reports to demonstrating relevant practical applications. In combination, the individual studies aim to contribute to a better understanding of the potential of qualitative analyst information and emphasize the role of this information as a sensible component of decision support systems.2024-06-0

Decision Support for Credit Risk Management Using Alternative Data

Die Digitalisierung der ökonomischen Aktivitäten von Individuen und Organisationen hat sich in den vergangenen Jahrzehnten zu einem bedeutenden Trend entwickelt. Dieser Wandel schafft Anreize für die Erhebung und die Verarbeitung von Daten, die für den Informationsbedarf unterschiedlicher Marktteilnehmer von Bedeutung sind. In vielen Fällen folgen die erfassten Daten keiner strikten Struktur mehr, da sie semi- oder sogar unstrukturiert sind. Diese Datenströme können jedoch Signale enthalten, die in etablierten Datenquellen nicht erfasst werden. Daraus ergibt sich eine Vielzahl von Anwendungsmöglichkeiten, wie der Entscheidungsprozess in verschiedenen Branchen unterstützt werden kann. Das Kreditrisikomanagement ist von dieser Entwicklung in besonderer Weise betroffen. Die Informationsasymmetrie zwischen Kreditgeber und Kreditnehmer fördert die Nachfrage nach zusätzlichen Signalen, welche die Risikobewertung verbessern können. Es handelt sich außerdem um einen Fachbereich von weitreichender Bedeutung, da die Existenz von Kreditrisiken fest in unserem Wirtschaftssystem verankert ist. Die Herausforderung bei der Nutzung alternativer Datenquellen besteht darin, aus ihnen relevante Signale zu extrahieren und diese mit dem Kreditrisiko zu verknüpfen. Da das Ziel ist, die Entscheidungsfindung zu unterstützen, sollte der gewählte Ansatz interpretierbar sein. Darüber hinaus müssen etablierte Verfahren und Erkenntnisse aus dem Bereich des Kreditrisikomanagements berücksichtigt werden. Die bisherige Forschung bietet zwar Vorschläge für die Bewältigung der entstehenden Herausforderungen, jedoch konnte noch kein einheitlicher und integrierter Ansatz gefunden und etabliert werden. Diese kumulative Dissertation untersucht die Verwendung alternativer Datenquellen zur Entscheidungsunterstützung im Kreditrisikomanagement. Die Arbeit besteht aus fünf Forschungsstudien, von denen jede zu einem der beiden folgenden Forschungsbereiche gehört. Forschungsbereich I widmet sich der Systematisierung und der Strukturierung des Themenfeldes. Zu diesem Zweck wird in der ersten Studie ein Literatur-Review durchgeführt. Dieser dient als Grundlage für die Identifizierung von Forschungslücken und ermöglicht das Ableiten einer Forschungsagenda. Die zweite Studie entwickelt eine Taxonomie, um Aspekte der Datenheterogenität hervorzuheben. Forschungsbereich II besteht aus empirischen Analysen, die untersuchen, wie alternative Daten zur Unterstützung von Entscheidungen im Rahmen des Kreditrisikomanagements genutzt werden können. Datensätze wie Analystenberichte und Finanznachrichten bilden die Grundlage für diese drei Studien. Dabei werden Text-Mining-Techniken angewandt, um Signale zu extrahieren, die zu Kreditrisikokennzahlen in Bezug gesetzt werden. Eine übergreifende Überlegung in den Studien ist die Interpretierbarkeit der angewandten Modelle, die aus den Bereichen der Statistik und des maschinellen Lernens stammen. Die Ergebnisse weisen auf einen Zusammenhang zwischen Kreditrisiko und den textbasierten Signalen hin, die aus Analystenberichten und Finanznachrichten extrahiert wurden.The digitization of economic activity of individuals and organizations has been a significant trend in recent decades. This shift creates incentives to collect and process data relevant to the information needs of diverse market participants. In many cases, the captured data no longer correspond to a strict structure as they are semi- or even unstructured. Such data streams may contain signals not captured in established data sources, creating a wide range of opportunities to support the decision making process in various industries. Credit risk management is positioned to be particularly impacted by this development. The information asymmetry between lender and borrower drives the demand for additional signals that can enhance risk assessment. It is also a field of far-reaching significance since the existence of credit risk is deeply embedded into our economic system. The challenge of using alternative data sources lies in extracting relevant signals and linking those to credit risk. Because the goal is to assist decision making, the chosen approach should be interpretable. Additionally, established procedures and findings from the field of credit risk management must be considered. Although existing research offers suggestions for addressing the emerging challenges, a unified and integrated approach has not yet been determined and established. This cumulative dissertation investigates the use of alternative data sources for decision support in credit risk management. The thesis consists of five research studies, each of which belongs to one of the following two research areas. Research Area I is dedicated to systematizing and structuring the subject area. For this purpose, the first study conducts a literature review. It serves as a basis for identifying research gaps and deriving a research agenda. The second study develops a taxonomy to highlight aspects of data heterogeneity. Research Area II consists of empirical analyses that examine how alternative data can be utilized to support decisions in the context of credit risk management. Data sets, such as analyst reports and financial news, represent the foundation for the three studies. Text mining techniques are applied to extract signals that are linked to credit risk measures. An overarching consideration throughout the studies is the interpretability of the applied statistical and machine learning models. The findings indicate a relationship between credit risk and the textual signals that originate from analyst reports and financial news.2024-06-0

How does artificial intelligence adoption differ across the consulting, banking and human resources sectors? - The case of artificial intelligence in Kpmg Portugal

This paper provides a comparative analysis of Artificial Intelligence (AI) adoption within the industries of Consulting, Banking, and Human Resources. Using the authors' professional experiences in Accenture, KPMG, Banco de Portugal, and Mercer, this paper examines the adoption and impacts of AI in these specific companies. It explores how these entities leverage AI to enhance services, detailing individual contributions and collaborative innovations. The research also assesses the impact on efficiency and client engagement and discusses the strategic, ethical, and operational challenges encountered

Unlocking Hidden Value: A Framework for Transforming Dark Data in Organizational Decision-Making

In today’s data-driven world, organizations generate and collect vast amounts of information, yet not all data is managed or utilized with the same degree of efficiency and purpose. This paper investigates the taxonomy and distinctions among white data, grey data, and dark data, offering a comprehensive analytical framework to better understand their characteristics, value, and implications. White data refers to structured, accessible, and actively managed information that supports strategic decision-making and operational processes. In contrast, grey data occupies an intermediate space, representing semi-structured or unstructured data that, while not fully optimized, holds potential value when properly integrated into organizational practices. Lastly, dark data comprises the large quantities of information that remain unexploited, often due to a lack of resources, awareness, or technology. By mapping these categories, this paper aims to highlight the importance of a systematic approach in managing diverse data types, underscoring both the risks and opportunities associated with each. The study ultimately provides practical insights and recommendations for organizations seeking to maximize the value of their data assets through effective taxonomy and governance strategies