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

Repairing a Cracked Mirror: The Heterogeneous Effect of Personalized Digital Nudges Driven by Misperception

( Jung, Miyeon | Cho, Daegon | Shin, Euncheol )



Our study aims to deepen the understanding of personalized digital nudges by evaluating their effects on energy?saving behavior. We conducted a field experiment with a leading smart metering company in South Korea to investigate whether customers save more energy when a personalized goal and feedback are provided, and how the impacts of nudges vary according to the types of misperception. Specifically, we focused on the behavior of customers who underestimate or overestimate their past electricity usage compared to their actual consumption. We merged daily energy consumption with a pre?experiment survey for the customers. We found that goal?setting and feedback mechanisms have a markedly different impact on each type of misperception. Underestimating customers reduced energy consumption only under the “goal setting with feedback treatment”. Conversely, overestimating customers reduced energy consumption even under the “goal setting without feedback” condition. The underlying mechanism is suggested as updating biased beliefs towards goal achievement. Overall, the results demonstrate that personalized nudges lead to heterogeneous behavioral responses and that service providers and policymakers can use these signals to enrich their planning of behavioral nudges.

Policy Uncertainty and Accounting Quality

( El Ghoul, Sadok | Guedhami, Omrane | Kim, Yongtae | Yoon, Hyo Jin )



Using data from 19 countries over the 1990-2015 period, we examine how economic policy uncertainty (EPU) affects accounting quality. We find that accounting quality, measured based on Nikolaev's (2018) model, increases during periods of high policy uncertainty. This relation is confirmed by the negative association between EPU and performance-adjusted discretionary accruals in a multivariate setting, and it extends to various alternative measures of earnings properties. We also find that the positive relation between EPU and accounting quality is more pronounced for government-dependent firms and firms with higher political risk. Additional analyses based on institutional investors' trading behavior, media freedom, and press circulation suggest that market participants' attention is a mechanism through which EPU affects accounting quality. Further, we find evidence that high accounting quality can mitigate the negative effects of EPU on corporate investment and valuation.

Arbitrage Portfolios

( Kim, Soohun | Korajczyk, Robert A | Neuhierl, Andreas )



We propose a new methodology for forming arbitrage portfolios that utilizes the information contained in firm characteristics for both abnormal returns and factor loadings. The methodology gives maximal weight to risk-based interpretations of characteristics’ predictive power before any attribution is made to abnormal returns. We apply the methodology to simulated economies and to a large panel of U.S. stock returns. The methodology works well in our simulation and when applied to stocks. Empirically, we find the arbitrage portfolio has (statistically and economically) significant alphas relative to several popular asset pricing models and annualized Sharpe ratios ranging from 1.31 to 1.66.

The Deterrent Effect of Ride-Sharing on Sexual Assault and Investigation of Situational Contingencies

( Park, Jiyong | Pang, Min-Seok | Kim, Junetae | Lee, Byungtae )



Sexual assault is one of the most repellant and costly crimes, which inflicts irrecoverable harms on victims and society. This study examines the effect of information technology (IT)-enabled ride-sharing platforms on sexual assaults. Drawing upon routine activity theory from the criminology literature, we posit that ride-sharing can reduce a passenger's risk of being a suitable target of sexual assault by providing a more reliable and timely transportation option for traveling to a safer place. By exploiting the nationwide quasi-experimental setting of Uber's city-by-city roilouts in the United States during 2005-2017, we demonstrate that Uber's entry into a city is negatively associated with the number of rape incidents. To zoom into the effects of ride-sharing at a more granular level, we employ precinct-hour-level data on Uber pickups and rape occurrences in New York City in 2015 and conduct spatiotemporal analyses. Our results from the spatiotemporal analyses corroborate those of the quasi-experiment and further reveal situational contingencies in the deterrent effect of ride-sharing. Specifically, ride-sharing contributes to a more significant reduction in the likelihood of rape occurrences in neighborhoods with limited transportation accessibility, and ride-sharing is more effective in deterring sexual crime in riskier circumstances, such as around alcohol-serving places on weekend nights or when the probability of crime occurrences increases. This study sheds new light on the potential of IT-enabled platforms to improve social well-being beyond their economic contributions and offers a new theoretical insight on the distinct role of digital platforms in public safety.

Volatility analysis with realized GARCH-Ito models

( Song, Xinyu | Kim, Donggyu | Yuan, Huiling | Cui, Xiangyu | Lu, Zhiping | Zhou, Yong | Wang, Yazhen )



This paper introduces a unified approach for modeling high-frequency financial data that can accommodate both the continuous-time jump?diffusion and discrete-time realized GARCH model by embedding the discrete realized GARCH structure in the continuous instantaneous volatility process. The key feature of the proposed model is that the corresponding conditional daily integrated volatility adopts an autoregressive structure, where both integrated volatility and jump variation serve as innovations. We name it as the realized GARCH-Ito model. Given the autoregressive structure in the conditional daily integrated volatility, we propose a quasi-likelihood function for parameter estimation and establish its asymptotic properties. To improve the parameter estimation, we propose a joint quasi-likelihood function that is built on the marriage of daily integrated volatility estimated by high-frequency data and nonparametric volatility estimator obtained from option data. We conduct a simulation study to check the finite sample performance of the proposed methodologies and an empirical study with the S&P500 stock index and option data.

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

Faculty & Research