MIS Distinguished Speaker Series

Temple University

You are here: Home / Archives for financial technology

financial technology

Apr 8 – Sunil Mithas to present “Mobile Apps, Portfolio Diversification, and Portfolio Performance: Evidence from a Quasi-Experiment in China”

By

Mobile Apps, Portfolio Diversification, and Portfolio Performance:
Evidence from a Quasi-Experiment in China

by

sunil mithas

 

Sunil Mithas

World Class Scholar and Professor
Muma College of Business
University of South Florida

Friday, Apr 8
11:00 am – 12:30 pm
In-person: 1810 Liacouras Walk, Room 420

Abstract:

Mobile apps are among the most important and widely used innovations in the brokerage industry. Surprisingly, despite their increasing economic importance and theoretical significance, few studies have examined the effects of mobile app use on individual investors’ financial decisions and performance. This study seeks to understand how mobile apps influence investors’ trading behaviors through portfolio diversification and portfolio performance in a quasi-experimental setting. We leverage a proprietary longitudinal dataset from a leading securities company, and adopt the staggered difference-in-differences specification as our main identification strategy. The findings suggest that mobile app adoption by retail investors leads to a 3.5% increase in portfolio diversification without deteriorating investors’ portfolio performance. Our exploratory analyses of underlying mechanisms suggest that mobile app adoption is especially beneficial for those who have high time constraints (by reducing transaction friction) and is less useful for those who are likely to be overconfident or who have high trend-chasing tendency (by boosting investors’ biases). Further analyses of adopters’ post-adoption behaviors show that mobile app usage intensity had an Inverted-U relationship with portfolio diversification and performance. In other words, balanced use of both PC and mobile channels permits desirable outcomes in terms of portfolio diversification and portfolio performance. We discuss the implications for research and practice.

Bio:

Sunil Mithas is a World Class Scholar and Professor at the Muma College of Business at the University of South Florida. Mithas has taught at the Robert H. Smith School of Business at the University of Maryland, and has held visiting positions in Australia, Germany and Hong Kong. He earned his PhD from the Ross School of Business at the University of Michigan and an engineering degree from IIT, Roorkee. Identified as an MSI Young Scholar by the Marketing Science Institute, Mithas is among top information systems scholars in the world. He has consulted and conducted research with a range of organizations including A. T. Kearney, Ernst & Young, Johnson & Johnson, the Social Security Administration, and the Tata Group. He is the author of two books, and his research has won best-paper awards, and featured in practice-oriented publications such as MIT Sloan Management Review, Bloomberg, and CIO.com.

Feb 18 – Xiao Fang to present “A Deep Learning Approach to Industry Classification”

By

A Deep Learning Approach to Industry Classification

by

Xiao Fang

Professor of Management Information Systems
JPMorgan Chase Senior Fellow
Lerner College of Business and Economics &
Institute for Financial Services Analytics
University of Delaware

Friday, Feb 18

11:00 am – 12:30 pm

In-person: 1810 Liacouras Walk 420 

Abstract:

Industry classification systems (ICSs), which identify economically related firms as peer firms, play a fundamental role in business research and practice. Traditional expert-driven approaches manually design ICSs and thus have limitations, including high maintenance costs and coarse granularity of the identified firm relatedness. To circumvent these limitations, recent research takes an algorithm-driven approach, employing a bag-of-words method to represent firms’ 10-K reports and leveraging these representations for identifying economically related firms. While firms’ 10-K reports are highly informative for identifying economically related firms, the bag-of-words method is inadequate for representing these documents, as it ignores the rich semantic information encoded in word contexts and order, resulting in a less effective ICS. Recent developments in deep-learning-based document embedding provide powerful tools for document representation. However, existing document embedding models (DEMs) are not well suited to capture the rich semantics of 10-K reports due to their challenging nature: they are long documents featuring heterogeneous and shifting concepts. We propose a novel DEM to address these challenges; it solves them through an innovative design of an adaptive gating mechanism and its associated gating function. In addition, we develop a new ICS that takes firms’ 10-K reports as input, employs the proposed DEM to represent the semantics of these reports, and identifies economically related firms based on similarities between their 10-K representations. We demonstrate through extensive empirical evaluations that our proposed ICS is superior to representative existing ICSs as well as ICSs constructed using state-of-the-art DEMs. This study contributes to business research and practice with a novel ICS that can effectively identify economically related firms. It also contributes to the field of deep-learning-based document embedding with an innovative DEM that can capture the semantics of a broad variety of long documents with shifting concepts, such as 10-K reports, legal documents, and patent documents.

Bio:

Xiao Fang is Professor of MIS and JPMorgan Chase Senior Fellow at Lerner College of Business & Economics and Institute for Financial Services Analytics, University of Delaware. He also holds appointments at Department of Computer Science as well as Department of Electrical and Computer Engineering, University of Delaware. His current research focuses on financial technology, social network analytics, and health care analytics, with methods and tools drawn from reference disciplines including Computer Science (e.g., Machine Learning) and Management Science (e.g., Optimization). He has published in business journals including Management Science, Operations Research, MIS Quarterly, and Information Systems Research as well as computer science outlets such as ACM Transactions on Information Systems and IEEE Transactions on Knowledge and Data Engineering. Professor Fang co-founded INFORMS Workshop on Data Science in 2017. He served as an Associate Editor for MIS Quarterly and currently on the editorial board of Service Science (INFORMS).