FinTech as a Game Changer: Overview of Research Frontiers

2021 | Information Systems Research | Citations: 25

Authors: Hendershott, Terrence; Zhang, Xiaoquan (Michael); Zhao, J. Leon; Zheng, Zhiqiang (Eric)

Abstract: Technologies have spawned finance innovations since the early days of computer a ... Expand

Abstract: Technologies have spawned finance innovations since the early days of computer applications in businesses, most recently reaching the stage of disruptive innovations, such as mobile payments, cryptocurrencies, and digitization of business assets. This has led to the emerging field called financial technology or simply FinTech. In this editorial review, we first provide an overview on relevant technological, pedagogical, and managerial issues pertaining to FinTech teaching and research, with a focus on market trading, artificial intelligence, and blockchain in finance. And then we introduce the articles appearing in this special section. We hope that our discussions of potential research directions and topics in FinTech will stimulate future research in the fields of information systems and finance toward making their unique marks in the FinTech evolution and the associated business and societal innovations. Collapse

Topics: electronic finance artificial intelligence mobile system fake news blockchain

Methods: linear regression analysis machine learning word embedding classification method ordinary least square

Event Study and Principal Component Analysis Based on Sentiment Analysis – A Combined Methodology to Study the Stock Market with an Empirical Study

2020 | Information Systems Frontiers | Citations: 0

Authors: Xu, Qianwen; Chang, Victor; Hsu, Ching-Hsien

Abstract: This paper provides an improved method by introducing Sentiment Analysis into th ... Expand

Abstract: This paper provides an improved method by introducing Sentiment Analysis into the Event Study and Principal Component Analysis. The model is constructed by using the heuristic mean-end analysis. This method enables us to take into investors’ feelings towards related stocks when we study the stock market’s reaction to a given event. This paper investigates the Chinese A-shared market over 2013–2019 to study the influence of rumors and the offsetting impact of rumor clarifications on the stock price. The results indicate that no matter investor sentiment is bullish or bearish, stock price reacts significantly to rumors before as well as when the rumor goes public. Furthermore, clarification offsets the positive abnormal returns caused by rumors with bullish sentiment substantially at a limited level. Still, after five days, it creates a positive effect like the positive rumor does on the stock price. Under the bearish sentiment, clarification brings an insignificant impact on the stock price. The results indicate that the source of rumor may not come from the media and investment decisions established on rumors would be beneficial to investors before as well as after they are published. Moreover, official clarification causes an offset effect, but it is very limited. Collapse

Topics: missing data database system accounting Python

Methods: data transformation longitudinal research principal component analysis event study Student's t-test

User Satisfaction with Information Systems: A Comprehensive Model of Attribute-level Satisfaction

2019 | Communications of the Association for Information Systems | Citations: 0

Authors: Vaezi, Reza; Mills, Annette; Chin, Wynne

Abstract: In this study, we introduce and test a comprehensive model of attribute-level sa ... Expand

Abstract: In this study, we introduce and test a comprehensive model of attribute-level satisfaction to measure user satisfaction with information systems (IS). Recognizing that, as complex “objects”, IS feature multiple subsystems, components, and attributes, we draw on marketing research and attribute satisfaction theory to assess user satisfaction across three levels of abstraction. We first assess overall satisfaction as the most abstract level then move to satisfaction with each major IS component (i.e., information, system, and service satisfaction). Subsequently, we measure user satisfaction with key attributes of each major IS component (e.g., information format, system reliability). The results provide a parsimonious yet practical model along with associated measures that can assess user satisfaction across various IS aspects (i.e., components and attributes) and different user interactions with IS. Collapse

Topics: user satisfaction PeopleSoft IS satisfaction IT security ease of use

Methods: survey PLS tool literature study statistical power analysis cross sectional research

Theories: two-factor theory information systems success model

Long-term stock index forecasting based on text mining of regulatory disclosures

2018 | Decision Support Systems | Citations: 0

Authors: Feuerriegel, Stefan; Gordon, Julius

Abstract: Share valuations are known to adjust to new information entering the market, suc ... Expand

Abstract: Share valuations are known to adjust to new information entering the market, such as regulatory disclosures. We study whether the language of such news items can improve short-term and especially long-term (24 months) forecasts of stock indices. For this purpose, this work utilizes predictive models suited to high-dimensional data and specifically compares techniques for data-driven and knowledge-driven dimensionality reduction in order to avoid overfitting. Our experiments, based on 75,927 ad hoc announcements from 1996–2016, reveal the following results: in the long run, text-based models succeed in reducing forecast errors below baseline predictions from historic lags at a statistically significant level. Our research provides implications to business applications of decision-support in financial markets, especially given the growing prevalence of index ETFs (exchange traded funds). Collapse

Topics: decision support electronic mail decision making decision support system electronic trading

Methods: experiment machine learning dimensionality reduction data transformation time series analysis

Determinants of software piracy under risk aversion: a model with empirical evidence

2015 | European Journal of Information Systems | Citations: 0

Authors: Driouchi, Ahmed; Wang, Mingzhu; Driouchi, Tarik

Abstract: This paper studies the determinants of software piracy in world economies from a ... Expand

Abstract: This paper studies the determinants of software piracy in world economies from a risk avoidance perspective. A risk aversion model for the commercialization of pirated software is developed to account for behavioral elements of risk and uncertainty avoidance among countries’ software pirates (i.e., counterfeiters and suppliers) and test empirically for the effects of country characteristics on piracy levels. Panel regression analysis is conducted to identify the determinants of software piracy using this model on a data set of 87 countries during 2007–2011. The empirical results confirm those obtained in prior research (e.g., the inverted U-shaped relationship between GDP per capita and piracy rates) but divulge that the behavioral-country component capturing the attitudes towards risk of software pirates improves the explanatory power of the statistical regressions after controlling for country performance and institutional factors. We also show that human development and good country governance reduce piracy rates. Besides providing support for our risk aversion-based piracy model and hinting at the need to consider population behavior in policy-making, these findings underline the relevance of human development and country institutions in explaining software piracy rates. Collapse

Topics: digital piracy decision making intellectual property enterprise information system database system

Methods: data transformation principal component analysis longitudinal research principal component regression descriptive statistic

Theories: theory of economic development

Comparing Out-of-Sample Predictive Ability of PLS, Covariance, and Regression Models

2014 | International Conference on Information Systems | Citations: 0

Authors: Evermann, Joerg; Tate, Mary

Abstract: Partial Least Squares Path Modelling (PLSPM) is a popular technique for estimati ... Expand

Abstract: Partial Least Squares Path Modelling (PLSPM) is a popular technique for estimating structural equation models in the social sciences, and is frequently presented as an alternative to covariance-based analysis as being especially suited for predictive modeling. While existing research on PLSPM has focused on its use in causalexplanatory modeling, this paper follows two recent papers at ICIS 2012 and 2013 in examining how PLSPM performs when used for predictive purposes. Additionally, as a predictive technique, we compare PLSPM to traditional regression methods that are widely used for predictive modelling in other disciplines. Specifically, we employ out-ofsample k-fold cross-validation to compare PLSPM to covariance-SEM and a range of atheoretical regression techniques in a simulation study. Our results show that PLSPM offers advantages over covariance-SEM and other prediction methods. Collapse

Methods: structural equation modeling PLS tool partial least squares path modeling partial least squares regression regression analysis method