Spring 2023 Seminars

Date Title/Speaker Abstract

Empirical evidence on the ownership and liquidity of real estate tokens.

Laurens Swinkle, Assistant Professor, Finance, Erasmus University, Rotterdam

To better understand the potential and limitations of the tokenization of real asset markets, empirical studies need to examine this radically new organization of financial markets. In our study, we examine the financial and economic consequences of tokenizing 58 residential rental properties in the US, particularly those in Detroit. Tokenization aims at fragmented ownership. We found that the residential properties examined have 254 owners on average. Investors with a greater than USD 5,000 investment in real estate tokens, diversify their real estate ownership across properties within and across the cities. Property ownership changes about once yearly, with more changes for properties on decentralized exchanges. We report that real estate token prices move according to the house price index; hence, investing in real estate tokens provides economic exposure to residential house prices.


Voice Skills and Product Control

Yazhen (Sophie) Xiao, Assistant Professor of Marketing, School of Business, Portland State University


Complex Network Models for Asset Pricing and Financial Decision Making: FinTech Via a Network Lens

Dantong Yu, Associate Professor, Martin Tuchman School of Management, NJIT

Financial systems exhibit a high degree of interconnections and linkages that form various networks: overnight bank lending networks, stock correlation networks, investor networks with similar portfolios, and board of directors networks. Recent studies suggest that inter-dependency networks among firms (sectors) are essential in asset valuation. It is incredibly challenging to capture and investigate the implications incurred by those networks because of the continuous evolution of networks in response to market micro and macro changes. To address this challenge, we take the global and local network models on asset pricing. From the global perspective, we propose a network factor “Z-score” based on the Laplacian spectrum of the equity market network to capture the evolution of the equity market over time and warrant a significant negative risk premium. From the local perspective, we investigate the interdependency between a portfolio manager’s network centrality (Google Personalized Page Rank, Katz centrality, alpha centrality) and extra performance (alpha) on his/her managed portfolio. The local and global network approaches gain impressive results and inspire us to design state-of-the-art graph neural networks that simultaneously consider endogenous features and external interactions to predict stock return and bond price. Experiment results show that our graph neural network models are superior in return prediction and portfolio performance.


Managerial Strategic Disclosure via Social Media-Evidence from 18 Million Corporate Tweets

Shaoqing Zhang, Ph.D. Candidate, Martin Tuchman School of Management, NJIT

Using a broad sample of earnings announcement tweets (EATs) constructed from 18 million corporate tweets, we document that there exists a substitute relationship between EATs and management earnings forecasts (MEFs). EATs reduce the use and improve the accuracy of MEFs and analysts’ forecast for contemporaneous and subsequent quarters. Such improvement is more pronounced in the presence of negative earnings news. Furthermore, firms with EATs are associated with higher profitability, better information environment, and lower liquidity. Using textual analysis, we show that firms with a positive tone of EATs are more likely to have positive earnings surprises. Overall, our findings provide novel evidence on the significant association between corporate Twitter activities and managers’ strategic disclosure as well as firms’ information environment and future performance. 

To Pivot or Not to Pivot: On the Relationship between Pivots and Revenue among Startups

Cesar Bandera, Martin Tuchman School of Management, NJIT

The concept of the pivot, whereby a venture alters its business model, is common practice among startups seeking to validate their value proposition in uncertain markets. Whereas a startup’s initial business model is based on the entrepreneur’s perception of the opportunity, pivots driven by empirical market feedback align the business model with market need. Some studies suggest pivots have positive effects on startup performance while others suggest executing too many pivots adversely affect performance. We argue that executing too many pivots can adversely affect firm performance by postponing the maturation of the firm. Using change in a venture’s NAICS code as a proxy for pivoting, we find an inverted-U relationship between revenue and the number of pivots among Kauffman Firm Survey participants. Counterintuitively, the penalty for over-pivoting is more severe among high-tech firms, which are commonly associated with greater business model uncertainty than low-tech firms. This longitudinal empirical study is one of the first on the relationship between pivoting and performance. It aims to attract attention to this important topic of entrepreneurship strategy, and help the entrepreneur facing the difficult decision of whether or not she should pivot.
04/19 Joe Micale, Assistant Professor, Martin Tuchman School of Management, NJIT  

Research Workshop Day

Zhibo Ye, Ph.D. student, Martin Tuchman School of Management, NJIT