The Effects Of Acquisition On Restaurant Firms’ Performance: Different-Sector Versus Same-sector Acquisitions

This study examines the postacquisition accounting performance of acquiring firms in the restaurant industry between 1992 and 2012. Specifically, this study investigates the effects of different-sector and same-sector restaurants acquisitions between full-service and limited-service restaurants on restaurant firms\u27 performance. Additionally, the Wilcoxon signed-rank test and regression model are used to examine return on assets (ROA) and return on equity (ROE) for the accounting performance of the acquiring restaurants. The ROA and ROE reveal that the profitability is significantly negative up to 5 years after firms are acquired. However, negative effects are strongest within the first year after acquisition and decrease until 4 years after compared with previous years. After 4 years, the negative effects turn to positive compared to the previous year for ROA and ROE changes. Further, the study reveals that the difference between different-sector and same-sector acquisitions indicates no significant relationship between ROA and ROE changes during all 5-year periods. Overall, this study shows that the effects of acquisitions between different sectors and the same sector are negative and there is no significant difference between them

Basic Enhancement Strategies When Using Bayesian Optimization for Hyperparameter Tuning of Deep Neural Networks

Compared to the traditional machine learning models, deep neural networks (DNN) are known to be highly sensitive to the choice of hyperparameters. While the required time and effort for manual tuning has been rapidly decreasing for the well developed and commonly used DNN architectures, undoubtedly DNN hyperparameter optimization will continue to be a major burden whenever a new DNN architecture needs to be designed, a new task needs to be solved, a new dataset needs to be addressed, or an existing DNN needs to be improved further. For hyperparameter optimization of general machine learning problems, numerous automated solutions have been developed where some of the most popular solutions are based on Bayesian Optimization (BO). In this work, we analyze four fundamental strategies for enhancing BO when it is used for DNN hyperparameter optimization. Specifically, diversification, early termination, parallelization, and cost function transformation are investigated. Based on the analysis, we provide a simple yet robust algorithm for DNN hyperparameter optimization - DEEP-BO (Diversified, Early-termination-Enabled, and Parallel Bayesian Optimization). When evaluated over six DNN benchmarks, DEEP-BO mostly outperformed well-known solutions including GP-Hedge, BOHB, and the speed-up variants that use Median Stopping Rule or Learning Curve Extrapolation. In fact, DEEP-BO consistently provided the top, or at least close to the top, performance over all the benchmark types that we have tested. This indicates that DEEP-BO is a robust solution compared to the existing solutions. The DEEP-BO code is publicly available at <uri>https://github.com/snu-adsl/DEEP-BO</uri>

Early-Bird or Last-Minute? The Impact of Mobile Channel Adoption on Purchasing Behavior

With the introduction of mobile technology, user behavior has been changed. One of the most representative features of mobile channels is that it enables users to access services regardless of time and place. The mobile channel is expected to enhance the flexibility of users. We examine whether there is a difference in purchase behavior between users who adopted mobile channels and those who did not, in a context where purchase time is limited and early purchase gives potential financial merit, using a large dataset from high-speed railway service in Korea. An interesting issue is whether mobile channel makes users purchase earlier and increase the chance to get discounts. Our results using difference-in-differences estimation with propensity score matching show that people who adopted mobile channel purchase tickets later on average and at a higher price than those who did not adopt mobile channel