Promise not fulfilled: FinTech, data privacy, and the GDPR

2023 | Electronic Markets | Citations: 0

Authors: Dorfleitner, Gregor; Hornuf, Lars; Kreppmeier, Julia

Abstract: This article analyzes how the General Data Protection Regulation (GDPR) has affe ... Expand

Abstract: This article analyzes how the General Data Protection Regulation (GDPR) has affected the privacy practices of FinTech firms. We study the content of 276 privacy statements respectively before and after the GDPR became binding. Using text analysis methods, we find that the readability of the privacy statements has decreased. The texts of privacy statements have become longer and use more standardized language, resulting in worse user comprehension. This calls into question whether the GDPR has achieved its original goal—the protection of natural persons regarding the transparent processing of personal data. We also link the content of the privacy statements to FinTech-specific determinants. Before the GDPR became binding, more external investors and a higher legal capital were related to a higher quantity of data processed and more transparency, but not thereafter. Finally, we document mimicking behavior among FinTech industry peers with regard to the data processed and transparency. Collapse

Topics: legal context General Data Protection Regulation privacy electronic finance website

Methods: parametric test paired t-test descriptive statistic Student's t-test qualitative content analysis

Theories: privacy calculus 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

What determines FinTech success?—A taxonomy-based analysis of FinTech success factors

2023 | Electronic Markets | Citations: 0

Authors: Werth, Oliver; Cardona, Davinia Rodríguez; Torno, Albert; Breitner, Michael H.; Muntermann, Jan

Abstract: Value creation in the financial services sector has been fundamentally transform ... Expand

Abstract: Value creation in the financial services sector has been fundamentally transformed by digitally born financial technology (FinTech) companies. FinTech companies synthesize information systems with financial services. Given its disruptive power, the FinTech phenomenon has received great attention in academic research, practice, and media. Still, limited systematic research provides a structure and holistic view of FinTechs’ success. Aiming to enhance understanding of the factors enabling FinTech success, we classify success factors across extant scientific literature on distinct FinTech business model archetypes. Our analysis reveals that the “cost–benefit dynamic of the innovation,” “technology adoption,” “security, privacy, and transparency,” “user trust,” “user-perceived quality,” and “industry rivalry” are crucial factors for FinTech success and can be seen as “grand challenges” for the FinTech ecosystem. In addition, we validate and discuss our findings with real-world examples from the FinTech industry and two interviews with stakeholders from the FinTech ecosystem. Our study contributes to the knowledge of FinTechs by providing a classification system of success factors for practitioners and researchers. Collapse

Topics: electronic finance business model privacy robo advisor technology adoption

Methods: qualitative content analysis qualitative interview case study literature study Google news

Information Transparency and Market Efficiency in Blockchain-enabled Marketplaces: Role of Traders’ Analytical Ability

2023 | International Conference on Information Systems | Citations: 0

Authors: Zhang, Hong; Zheng, Zhiqiang; Mehra, Amit

Abstract: Classic economic theory asserts that full information transparency entails info ... Expand

Abstract: Classic economic theory asserts that full information transparency entails information symmetry and, thus, market efficiency. We test if this theory still holds in a blockchainenabled marketplace where full information transparency is accomplished. We leverage the data from EnjinX, a non-fungible-token (NFT) marketplace, where the entire historical NFT transactions are symmetrically accessible to all buyers and sellers. We surprisingly observe substantial market inefficiencies. To explain this paradox that inefficiencies persist even in a fully information-transparent environment, we propose that traders' limited analytical ability, rather than information asymmetry, ultimately drives market inefficiencies. We quantify analytical ability by examining whether traders' performance can be augmented by machine-learning algorithms. And we find that having ten more historical transactions increases market efficiency by 1.10%. However, market efficiency could decrease by 69.02% when traders cannot effectively consume the available information. Our findings contribute to the literature by quantifying analytical ability and highlighting the analytical-ability divide phenomenon. Collapse

Topics: blockchain digital divide Ethereum non-fungible token application programming interface

Methods: computational algorithm machine learning experimental group experiment ordinary least square

Theories: lemon market theory

Gamified System Effectiveness on Social Trading Platforms

2022 | International Conference on Information Systems | Citations: 0

Authors: Chen, Yingying; Leung, Alvin; Yue, Wei Thoo

Abstract: To motivate user engagement and generate desired engagement outcomes, some soci ... Expand

Abstract: To motivate user engagement and generate desired engagement outcomes, some social trading platforms have introduced gamified systems with hierarchical badges and financial incentives. However, previous studies have not examined the effectiveness of such an application. Based on data collected from a popular social trading platform, eToro, we empirically examine the effectiveness of the gamified system and its mechanism. Results indicate that the gamified system under social trading is effective in inducing user-to-user and user-to-system interactions, thus leading to some desired engagement outcomes on social media. However, the gamified system does not contribute to the engagement outcomes on financial investment. Our paper contributes to the literature on both gamification and social trading, highlighting gamification's important practical implications for platform managers, cautioning against the possible ineffectiveness of the dual-outcome gamified system and shedding light on the design of gamified systems. Collapse

Topics: social trading platform gamification social media eToro media richness

Methods: longitudinal research propensity score matching

When Trading Becomes Social: How Social Trading Platforms Affect the Disposition Effect

2022 | International Conference on Information Systems | Citations: 0

Authors: Jabr, Wael; Rai, Arun

Abstract: Social trading platforms have, over the last decade or so, been gaining a stron ... Expand

Abstract: Social trading platforms have, over the last decade or so, been gaining a strong foothold in individual investment markets. Users on these platforms can observe ("view") traders' detailed transactions over time. They can also ''follow'' anyone of those traders, just like with other social media platforms, investing their money in accordance with the strategies of their trader of choice. We study whether and how the disposition effect bias of individual traders is affected by two social features of the platform, "Views" and "Followers." We find a differentiated impact on this bias from those two social features, which is conditional on the level of market turbulence. We attribute this to how traders assess the signal originating from Views and Followers in relation to how committal it is. Collapse

Topics: social trading platform decision making electronic finance logistics management financial performance

Methods: theory development Z-test survival analysis cluster analysis hazard function

Theories: disposition effect behavioral economics

Effects of Personality on Trading Performance in Social Trading Platforms

2022 | Americas Conference on Information Systems | Citations: 0

Authors: Liu, Quanchen; Xiong, Bingqing; Lim, Eric; Zhu, Jiantao

Abstract: Social trading platforms offer opportunities for amateur investors to copy prof ... Expand

Abstract: Social trading platforms offer opportunities for amateur investors to copy professional traders' behavior. However, past studies on behavioral finance have largely neglected the role of personality in shaping traders' behavior. To this end, we aim to scrutinize the effects of leader traders' personality on their trading behaviors and subsequent performance on social trading platforms. Particularly, we employ the Myers-Briggs Type Indicator (MBTI) personality classification scheme to delineate leader traders' personality into the four dimensions of Extraversion-Introversion (E-I), Sensing-Intuition (S-N), Thinking-Feeling (T-F), and Judging-Perceiving (J-P). Next, we draw on machine learning techniques to advance a novel text-based approach for extracting the personality dimensions of leader traders automatically. Analytical results attest to the impact of personality dimensions on trading behavior and that of trading behavior on performance. Findings from this study yield insights for both social trading platforms and followers by identifying profitable leader traders based on their personality. Collapse

Topics: personality social trading platform

Methods: machine learning descriptive statistic statistical hypothesis test multidimensional modeling partial least squares regression

Theories: Myers–Briggs type indicator big five model behavioral economics

Introduction to the Mini-Track “The Sharing Economy”

2022 | HICSS | Citations: 0

Authors: Xiao, Bo; Abhari, Kaveh

Abstract: ... Expand

Abstract: Collapse

Topics: crowdfunding sharing platform crowdsourcing usability social media

Theories: uncertainty reduction theory

From Copy to Practice: Followers’ Learning Behavior in Forex Social Trading

2019 | International Conference on Information Systems | Citations: 0

Authors: Yu, Mengli; Li, Yijing; Cai, Zhao; Liu, Fei; Tan, Chee-Wee

Abstract: Forex social trading platforms endows novice investors with opportunities to tra ... Expand

Abstract: Forex social trading platforms endows novice investors with opportunities to trade on foreign exchange markets by mimicking the investment strategies of sophisticated traders. But concurrently, the copy-trading mechanism underlying these platforms foster a conducive learning environment whereby inexperience followers could evolve into independent traders by observing and learning from the trading behaviors of prominent traders. Drawing on observational learning theory, we advance learning efficiency and effectiveness as focal yardsticks to encapsulate followers’ learning performance and explore their effects on the profitability of followers’ first independent trades. Preliminary analysis conducted on a leading forex social trading platform reveals that traders’ trading consistency amplifies followers’ learning efficiency whereas traders’ profitability bolsters followers’ learning effectiveness. Furthermore, while our empirical findings attest to the criticality of learning effectiveness on followers’ ability to profit from their initial independent trades, speeding up the learning process may not guarantee better performance. Collapse

Topics: social trading platform online learning system task complexity

Methods: partial least squares regression PLS tool descriptive statistic

Theories: social learning theory

Effects of transparency: analyzing social biases on trader performance in social trading

2018 | Journal of Information Technology | Citations: 11

Authors: Glaser, Florian; Risius, Marten

Abstract: Social Trading platforms combine the trading functionalities of classical online ... Expand

Abstract: Social Trading platforms combine the trading functionalities of classical online broker services with the communication and interaction features of social networks. Next to following other users’ profiles, a main characteristic of social trading platforms is the possibility to follow other users by automatically copying their trades. By being a technologically based financial intermediary that enables individual profit maximization, social trading platforms constitute a contemporary example of financialization. Our empirical analysis of the behavior of traders on a social trading platform provides new insights on financialization related questions regarding the influence of transparency and interaction in delegated investment environments. The disposition effect is a well-studied behavioral bias of investors and traders. Human investors tend to realize returns of their winning positions too early and let unfavorable positions accumulate losses for too long. We find that on social trading platforms the traders’ sensitivity to the disposition effect is influenced by the amount of attention they receive from their followers who invested capital into the traders’ strategy. These novel insights propose a link between principal-agent theory and the disposition effect induced by transparency mechanisms. We extend the literature on trader-investor interaction channels in social trading networks. The results obtained in a social network environment are of high relevance for regulators who have a strong focus on customer protection and financial services regulation. They also provide guidelines for platform designers, traders, investors and social trading platform operators. Collapse

Topics: social trading platform moral hazard decision making electronic trading social influence

Methods: longitudinal research autocorrelation analysis econometric modeling regression analysis method statistical hypothesis test

Theories: disposition effect behavioral economics agency theory