DePaul University

About: DePaul University is a education organization based out in Chicago, Illinois, United States. It is known for research contribution in the topics: Population & Context (language use). The organization has 5658 authors who have published 11562 publications receiving 295257 citations.

Using Psychology Theories in Archival Financial Accounting Research

Abstract: Psychologists have studied human behavior for over a century and, as a result, have developed a robust set of theories regarding how people behave. Most financial accounting issues deal with matters of human behavior, such as the judgments and decisions of managers, investors, analysts, and auditors. Consequently, psychology offers a rich pool of theories from which financial accounting researchers can draw to motivate hypotheses and interpret results. Despite this, archival accounting researchers traditionally have relied almost solely on theories based in financial economics. We argue that two major obstacles to the use of psychology theories by archival researchers has been a lack of awareness about the theories that are available and when their use would be most productive. Our paper attempts to bridge this gap. Specifically, we describe a number of psychology theories that are applicable to financial accounting issues, lay out the circumstances where they may be especially useful to archival researchers, and provide a number of specific examples of how psychology theories provide new insights about financial accounting issues.

Do Investors Learn from Experience? Evidence from Frequent IPO Investors

Abstract: We examine how experience affects the decisions of individual investors and institutions in IPO auctions to bid in subsequent auctions, and their bidding returns. We track bidding histories for all 31,476 individual investors and 1,232 institutional investors across all 84 IPO auctions during the period from 1995 to 2000 in Taiwan. For individual bidders, (1) high returns in previous IPO auctions increase the likelihood of participating in future auctions; (2) bidders' returns decrease as they participate in more auctions; (3) auction selection ability deteriorates with experience; and (4) those with greater experience bid more aggressively. These findings are consistent with naive reinforcement learning wherein individuals become unduly optimistic after receiving good returns. In sharp contrast, there is little sign that institutional investors exhibit such behavior. The Author 2011. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

Automatic Online Fake News Detection Combining Content and Social Signals

Abstract: The proliferation and rapid diffusion of fake news on the Internet highlight the need of automatic hoax detection systems In the context of social networks, machine learning (ML) methods can be used for this purpose Fake news detection strategies are traditionally either based on content analysis (ie analyzing the content of the news) or - more recently - on social context models, such as mapping the news' diffusion pattern In this paper, we first propose a novel ML fake news detection method which, by combining news content and social context features, outperforms existing methods in the literature, increasing their already high accuracy by up to 48% Second, we implement our method within a Facebook Messenger chatbot and validate it with a real-world application, obtaining a fake news detection accuracy of 817%