TRADING GREEN BONDS USING DISTRIBUTED LEDGER TECHNOLOGY

2023 | European Conference On Information Systems | Citations: 0

Authors: Axelsen, Henrik; Rasmussen, Ulrik; Jensen, Johannes; Ross, Omri; Henglein, Fritz

Abstract: The promising markets for voluntary carbon credits are faced with crippling cha ... Expand

Abstract: The promising markets for voluntary carbon credits are faced with crippling challenges to the certification of carbon sequestration and the lack of scalable market infrastructure in which companies and institutions can invest in carbon offsetting. This amounts to a funding problem for green transition projects, such as in the agricultural sector, since farmers need access to the liquidity needed to fund the transition to sustainable practices. We explore the feasibility of mitigating infrastructural challenges based on a DLT Trading and Settlement System for green bonds. The artefact employs a multi-sharded architecture in which the nodes retain carefully orchestrated responsibilities in the functioning of the network. We evaluate the artefact in a supranational context with an EU-based regulator as part of a regulatory sandbox program targeting the new EU DLT Pilot regime. By conducting design-driven research with stakeholders from industrial and governmental bodies, we contribute to the IS literature on the practical implications of DLT. Collapse

Topics: distributed ledger technology network effect blockchain domain-specific language

Methods: design science qualitative interview design artifact testing design artifact

The effect of lockup and persuasion on online investment decisions: An experimental study in ICOs

2023 | Electronic Markets | Citations: 0

Authors: Bruckner, Moritz T.; Steininger, Dennis M.; Thatcher, Jason Bennett; Veit, Daniel J.

Abstract: Many firms use social media (SM) to solicit online investments. In this study, w ... Expand

Abstract: Many firms use social media (SM) to solicit online investments. In this study, we examine the interaction between SM attributes and online-investment attributes to determine how this interaction shapes users’ investment decisions. Specifically, we investigate initial coin offerings (ICOs) as an application domain of distributed ledger technology for peer-to-peer investment. We use signaling theory to develop a context-specific explanation for how the interplay of persuasion signals found in SM and technology-enforced lockups shapes individuals’ ICO investment decisions. To evaluate this interplay, we conducted a 2 × 2 factorial experiment with 473 participants. The results show that when an investment does not require a technology-enforced lockup, persuasion signals encourage investments in ICOs; however, when an investment requires a technology-enforced lockup, persuasion signals do not affect investments in ICOs. Furthermore, our analyses suggest that combining a technology-enforced lockup and persuasion signals reduces the ICO’s plausibility. This is the first study to investigate how the willingness to invest in ICOs is influenced by the relationship between technology-enforced lockups and persuasion signals. The findings have practical implications for individuals attempting to make sound decisions on ICO investments, policymakers regulating online investments, and firms seeking to attract investors. Collapse

Topics: internet technology crowdfunding crowdsourcing Facebook blockchain

Methods: experimental group parametric test repeated measures ANOVA experimental design cross sectional research

Theories: signaling theory

A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media

2023 | Information Systems Frontiers | Citations: 0

Authors: Chung, Wingyan; Zhang, Yinqiang; Pan, Jia

Abstract: The spreading of disinformation in social media threatens cybersecurity and unde ... Expand

Abstract: The spreading of disinformation in social media threatens cybersecurity and undermines market efficiency. Detecting disinformation is challenging due to large volumes of social media content and a rapidly changing environment. This research developed and validated a theory-based, novel deep-learning approach (called TRNN) to disinformation detection. Grounded in social and psychological theories, TRNN uses deep-learning and data-centric augmentation to enhance disinformation detection in financial social media. Temporal and contextual information is encoded as specific knowledge about human-validated disinformation, which was identified from our unique collection of 745,139 financial social media messages about four U.S. high-tech company stocks and their fine-grained trading data. TRNN uses multiple series of long short-term memory (LSTM) recurrent neurons to learn dynamic and hidden patterns to support disinformation detection. Our experimental findings show that TRNN significantly outperformed widely-used machine learning techniques in terms of precision, recall, F-score and accuracy, achieving consistently better classification performance in disinformation detection. A case study of Apple Inc.’s stock price movement demonstrates the potential usability of TRNN for secure knowledge management. The research contributes to developing novel approach and model, producing new information systems artifacts and dataset, and providing empirical findings of detecting online disinformation. Collapse

Topics: disinformation social media Twitter fake news usability

Methods: machine learning experiment design artifact deep learning recurrent neural network

Theories: psychological theory

A Taxonomy of Violations in Digital Asset Markets

2023 | International Conference on Information Systems | Citations: 0

Authors: Clapham, Benjamin; Jakobs, Jenny; Schmidt, Julian; Gomber, Peter; Muntermann, Jan

Abstract: Numerous frauds, market manipulations and other violations have recently shaken ... Expand

Abstract: Numerous frauds, market manipulations and other violations have recently shaken investor confidence in digital asset markets and digital assets themselves. Yet, investor confidence and market integrity are key requirements for the continued success of crypto and other digital assets. In order to facilitate the integrity of digital asset markets and avoid integrity incidents in the future, a systematic overview of violations and their main characteristics is needed to develop appropriate countermeasures. Therefore, we develop a taxonomy of violations in digital asset markets and evaluate the taxonomy based on real-world cases. Our results show that many types of market manipulation in traditional financial markets can also be observed in digital asset markets. However, there are new and additional violations in digital asset markets. We also find that many violations depend on specific capabilities of the violator, certain trading conditions, and asset-specific characteristics. Collapse

Topics: IT security threat crypto wallet accounting information technology infrastructure smart contract

Methods: robustness check cluster analysis

The Impact of High-Frequency Trading on Modern Securities Markets

2023 | Business & Information Systems Engineering | Citations: 0

Authors: Clapham, Benjamin; Haferkorn, Martin; Zimmermann, Kai

Abstract: High-frequency traders account for a significant part of overall price formation ... Expand

Abstract: High-frequency traders account for a significant part of overall price formation and liquidity provision in modern securities markets. In order to react within microseconds, high-frequency traders depend on specialized low latency infrastructure and fast connections to exchanges, which require significant IT investments. The paper investigates a technical failure of this infrastructure at a major exchange that prevents high-frequency traders from trading at low latency. This event provides a unique opportunity to analyze the impact of high-frequency trading on securities markets. The analysis clearly shows that although the impact on trading volume and the number of trades is marginal, the effects on liquidity and to a lesser extent on price volatility are substantial when high-frequency trading is interrupted. Thus, investments in high-frequency trading technology provide positive economic spillovers to the overall market since they reduce transaction costs not only for those who invest in this technology but for all market participants by enhancing the quality of securities markets. Collapse

Topics: electronic trading IS impact

Methods: experimental group regression analysis method descriptive statistic robustness check cross sectional research

Theories: transaction cost economics

Introduction to Data, Text, and Web Mining for Business Analytics Mini-track

2023 | HICSS | Citations: 0

Authors: Delen, D.; Davazdahemami, B.

Abstract: ... Expand

Abstract: Collapse

Topics: cryptocurrency social media

Methods: computational algorithm experiment transformer model deep learning

FINFLUENCERS: OPINION MAKERS OR OPINION FOLLOWERS?

2023 | European Conference On Information Systems | Citations: 0

Authors: Haase, Frederic; Rath, Oliver; Kurka, Marco; Schoder, Detlef

Abstract: This paper explores the concept of Finfluencers: financial social network actor ... Expand

Abstract: This paper explores the concept of Finfluencers: financial social network actors with high potential social influence. Our research aims to clarify whether Finfluencers drive or are influenced by the broader social network sentiment, thereby establishing their role as either opinion makers or opinion followers. Using a dataset of 71 million tweets focusing on stocks and cryptocurrencies, we grouped actors by their social networking potential (SNP). Next, we derived sentiment time series using state-ofthe-art sentiment models and applied the technique of Granger causality. Our findings suggest that the sentiment of Finfluencer actors on Twitter has short-term predictive power for the sentiment of the larger group of actors. We found stronger support for cryptocurrencies in comparison to stocks. From the perspective of financial market regulation, this study emphasizes the relevance of understanding sentiment on social networks and high social influence actors to anticipate scams and fraud. Collapse

Topics: Twitter social network social influence cryptocurrency social media

Methods: longitudinal research time series analysis Granger causality descriptive statistic sentiment analysis

Detection of Stock Manipulation Influencer Content using Supervised Learning

2023 | International Conference on Information Systems | Citations: 0

Authors: Haase, Frederic; Rath, Oliver; Lauten, Julia; Schoder, Detlef

Abstract: In recent years, social media influencers have emerged as key players in stock ... Expand

Abstract: In recent years, social media influencers have emerged as key players in stock manipulation schemes. Despite their growing impact, methods to detect such activities remain scarcely explored. In this study, we examine the social media content of stock manipulation influencers (SMIs) implicated in a $100 million fraud case by the U.S. Securities and Exchange Commission (SEC) in 2022. Leveraging natural language processing (NLP) techniques, we first investigate the linguistic characteristics present in the social media content published by SMIs. Next, we develop and evaluate supervised learning models to detect manipulative content. Our results have significant implications for investors, regulators, and the broader financial community. They reveal the unique linguistic characteristics of SMI content and demonstrate the potential of machinelearning and deep-learning-based techniques in advancing fraud detection systems. Collapse

Topics: Twitter social media fraud detection database system social media analytics

Methods: machine learning RoBERTa DistilBERT computational algorithm natural language processing

The promise of digital technologies for sustainable entrepreneurship: A systematic literature review and research agenda

2023 | International Journal of Information Management | Citations: 1

Authors: Holzmann, Patrick; Gregori, Patrick

Abstract: Research on sustainable entrepreneurship increasingly recognizes the transformat ... Expand

Abstract: Research on sustainable entrepreneurship increasingly recognizes the transformative potential of digital tech­ nologies to mitigate and counteract grand environmental and social challenges through entrepreneurial action. However, this emerging field of research, referred to as digital sustainable entrepreneurship, is currently dispersed and fragmented and lacks the consolidated foundation to progress further. This article further estab­ lishes this nascent stream by conducting a systematic literature review offering two main contributions. First, common themes are derived from the literature (i.e., enabling value for society and environment, stakeholder inclusion, venture viability, and entrepreneurial individuals) to unravel the field’s current state. Second, previous work is discussed and integrated by applying a business model perspective. Specifically, the article offers a framework that contributes to the role of business models for merging sustainability and digital technologies, reconceptualizes digital technologies as business model actors, and further develops the entrepreneur-business model nexus. Based on this, we present a comprehensive and actionable research agenda and practical implications. Collapse

Topics: business model entrepreneurship big data mobile application green IS

Methods: structured literature research theory development literature study longitudinal research machine learning

Theories: sociomaterialism theory

Social informedness and investor sentiment in the GameStop short squeeze

2023 | Electronic Markets | Citations: 2

Authors: Kim, Kwansoo; Lee, Sang-Yong Tom; Kauffman, Robert J.

Abstract: We examine investor behavior on social media platforms related to the GameStop ( ... Expand

Abstract: We examine investor behavior on social media platforms related to the GameStop (GME) short squeeze in early 2021. Individual investors stimulated the stock market via Reddit social posts in the presence of institutional investors who bet against GME’s success as short sellers. We analyzed r/WallStreetBets subreddit posts related to GME’s trading patterns. We performed text-based sentiment analysis and compared the social informedness of posting users for GME trading on two social media platforms. The short squeeze occurred due to coordinated trading by individual investors, who discussed trading strategies on the platforms and drove collective social informedness-based trading behavior. Our findings suggest that the valence and number of submissions influenced GME’s intraday transaction volumes and precursors for irrational trading behavior patterns to have emerged. We provide a theoretical interpretation of what occurred and call for tighter monitoring of social news platforms. We also encourage effort to create an in-depth understanding of the observed patterns and the linkages between them and the larger equity markets. Collapse

Topics: Reddit social media Twitter analytical information system Python

Methods: Student's t-test sentiment analysis longitudinal research econometric modeling case study

Theories: cognitive dissonance theory