KAIST College of Business Selected Publications > Faculty Research > Faculty & Research >KAIST COLLEGE OF BUSINESS
본문 바로가기 사이트 메뉴 바로가기 주메뉴 바로가기

Selected recent publications in the top management and economics journals

Probability of price crashes, rational speculative bubbles, and the cross-section of stock returns

( Jang, Jeewon | Kang, Jangkoo )



We estimate an ex ante probability of extreme negative returns (crashes) of individual stocks as a measure of potential overpricing and find that stocks with a high probability of crashes earn abnormally low returns. Stocks with high crash probability are overpriced regardless of the level of institutional ownership or variations in investor sentiment, and moreover, they exhibit increasing institutional demand until their prices reach the peak of overvaluation. We also find that institutional investors who overweight high crash probability stocks outperform the others, indicating that they have skill in timing bubbles and crashes of individual stocks. Our findings imply that sophisticated investors may not always trade against mispricing but time the correction of overpricing, and suggest that the crash effect we find could arise at least partially from rational speculative bubbles, not entirely from sentiment-driven overpricing. (C) 2018 Elsevier B.V. All rights reserved.

Entrepreneurship, innovation, and political competition: How the public sector helps the sharing economy create value

( Paik, Yongwook | Kang, Sukhun | Seamans, Robert )



Research Summary: With the recent growth of the sharing economy, regulators must frequently strike the right balance between private and public interests to maximize value creation. In this article, we argue that political competition is a critical ingredient that explains whether cities accommodate or ban ridesharing platforms and that this relationship is moderated in more populous cities and in cities with higher unemployment rates. We test our arguments using archival data covering ridesharing bans in various U.S. cities during the 2011-2015 period. We supplement these data with semistructured interviews. We find broad support for our arguments while mitigating potential endogeneity concerns. Our study has important implications for nonmarket strategy, entrepreneurship and innovation, and public-private partnership literatures. In addition, our fmdings inform policy debates on the sharing economy. Managerial Summary: Entrepreneurs and businesses oftentimes face severe regulatory barriers when commercializing innovative products and services even if the innovations are generally beneficial for consumers and the broader society. This research focuses on the political determinants of regulation to provide a better understanding of why some markets are more receptive to innovative products while other markets are more hostile to them. Using the banning of ridesharing companies (e.g., Uber and Lyft) in various U.S. cities during the 2011-2015 period, we find that elected politicians facing less political competition (i.e., not easily replaceable, serving multiple terms, longer tenure in office) were more likely to ban ridesharing companies and favor, potentially displaceable, local taxicab companies. Our research has implications for navigating the political barriers to entry.

Structured volatility matrix estimation for non-synchronized high-frequency financial data

( Fan, Jianqing | Kim, Donggyu )



Several large volatility matrix estimation procedures have been recently developed for factor-based Ito processes whose integrated volatility matrix consists of low-rank and sparse matrices. Their performance depends on the accuracy of input volatility matrix estimators. When estimating co-volatilities based on high-frequency data, one of the crucial challenges is non-synchronization for illiquid assets, which makes their co-volatility estimators inaccurate. In this paper, we study how to estimate the large integrated volatility matrix without using co-volatilities of illiquid assets. Specifically, we pretend that the co-volatilities for illiquid assets are missing, and estimate the low-rank matrix using a matrix completion scheme with a structured missing pattern. To further regularize the sparse volatility matrix, we employ the principal orthogonal complement thresholding method (POET). We also investigate the asymptotic properties of the proposed estimation procedure and demonstrate its advantages over using co-volatilities of illiquid assets. The advantages of our methods are also verified by an extensive simulation study and illustrated by high-frequency data for NYSE stocks. (C) 2018 Elsevier B.V. All rights reserved.

Seizing the Commuting Moment: Contextual Targeting Based on Mobile Transportation Apps

( Ghose, Anindya | Kwon, Hyeokkoo Eric | Lee, Dongwon | Oh, Wonseok )



Despite the average daily commuting time of commuters increasing by the day, the way marketers can benefit from our commuting behaviors has not yet been examined. In collaboration with one of the largest global mobile telecom providers, this study investigates how contextual targeting with commuting impacts user redemptions of mobile coupons. The analysis is based on a rich field study in which 14,741 mobile coupons were sent to 9,928 public transit app users consisting of commuters and noncommuters. The key findings indicate that commuters are about 3x as likely to redeem their mobile coupon compared with noncommuters. However, a multiple-coupon distribution strategy is more effective in increasing redemption among noncommuters than commuters. Moreover, the redemption rate of commuters is higher for coupons with shorter expiration periods, whereas that of noncommuters is higher for coupons with longer expiration periods. On the basis of theories from psychology and physiology, we argue that stress, which is exacerbated by commuting, increases commuters' coupon redemption rate. We provide empirical support for this argument and show that marketers can increase response rates by focusing on specific periods of the day when commuting stress is relatively high (e. g., rush hours). By carefully exploiting commuting, which is easily identifiable and occurs throughout the world, managers may improve their mobile marketing effectiveness.

(Rescaled) Multi-Attempt Approximation of Choice Model and Its Application to Assortment Optimization

( Chung, Hakjin | Ahn, Hyun-Soo | Jasin, Stefanus )



When designing an assortment, a fundamental trade-off exists between estimating consumer behavior more precisely and solving the corresponding optimization problem efficiently. For instance, a mixture of logit model can closely approximate any random-utility choice model; however, it is not suitable for optimization when the number of customer segments and the number of products considered are large. As many companies are using big data and marketing analytics toward microsegmenting consumers, a choice model that is amenable in the problem size becomes necessary for assortment design. In this work, we provide a new approach to approximate any random-utility choice model by characterizing the estimation errors in a classical exogenous demand model and significantly improving its performance with a rescaling method. We show that the resulting approximation is exact for Multinomial Logit (MNL). If, however, the underlying true choice model is not MNL, we show numerically that the approximation under our so-called rescaled two-attempt model outperforms the widely used MNL approximation, and provides performance close to the Markov chain approximation (in some cases, it performs better than the Markov chain approximation). Our proposed approximation can be used to solve a general assortment optimization problem with a variety of (linear) real-world constraints. In contrast to the more direct Mixed Integer Optimization (MIO) approach that utilizes Latent Class Multinomial Logit (LC-MNL), whose running time increases exponentially in the number of mixtures, our approximation yields an alternative MIO formulation whose empirical running time is independent of the number of mixtures.

Contact : Joo, Sunhee ( shjoo2006@kaist.ac.kr )

Faculty & Research