The Determinants of Hedging by Derivatives in Hong Kong and Chinese Firms and the Value Effects

This dissertation studies the determinants and the value effects of corporate hedging with derivatives for 230 Hong Kong and Chinese non-financial firms listed in Hong Kong Stock Exchange from 2008 to 2013. With the data from annual reports, the evidence is found that there are positive relations between deciding to hedge with derivatives and foreign currency exposures, expected cost of financial distress, and scale of economies. The liquidity measures are negatively linked with the usage of derivatives. The empirical results of the financial distress costs and liquidity factors for Chinese firms are relatively weaker than those of Hong Kong firms, which may be explained by the state and the government as the major shareholder providing financial supports and the debt guarantee. Finally, the growths of firm values from hedging activities are 1.70% for Hong Kong firms and 0.37% for Chinese firms from the perspective of tax benefits

Concern or Control?: Gender Stereotyping and Hospitality Leaders

Although most managers in the global hospitality industry are still male, an increasing number of women are taking on leadership roles. But how exactly do employees perceive masculine and feminine leadership styles? New research led by UCF Rosen College of Hospitality Management\u27s Associate Professor Bendegul Okumus and the research team she works with looks at gender stereotypes and finds that the most successful managers, in the eyes of their staff, have a management style that combines both masculine and feminine leadership traits

Leveraging Prototype Patient Representations with Feature-Missing-Aware Calibration to Mitigate EHR Data Sparsity

Electronic Health Record (EHR) data frequently exhibits sparse characteristics, posing challenges for predictive modeling. Current direct imputation such as matrix imputation approaches hinge on referencing analogous rows or columns to complete raw missing data and do not differentiate between imputed and actual values. As a result, models may inadvertently incorporate irrelevant or deceptive information with respect to the prediction objective, thereby compromising the efficacy of downstream performance. While some methods strive to recalibrate or augment EHR embeddings after direct imputation, they often mistakenly prioritize imputed features. This misprioritization can introduce biases or inaccuracies into the model. To tackle these issues, our work resorts to indirect imputation, where we leverage prototype representations from similar patients to obtain a denser embedding. Recognizing the limitation that missing features are typically treated the same as present ones when measuring similar patients, our approach designs a feature confidence learner module. This module is sensitive to the missing feature status, enabling the model to better judge the reliability of each feature. Moreover, we propose a novel patient similarity metric that takes feature confidence into account, ensuring that evaluations are not based merely on potentially inaccurate imputed values. Consequently, our work captures dense prototype patient representations with feature-missing-aware calibration process. Comprehensive experiments demonstrate that designed model surpasses established EHR-focused models with a statistically significant improvement on MIMIC-III and MIMIC-IV datasets in-hospital mortality outcome prediction task. The code is publicly available at \url{https://github.com/yhzhu99/SparseEHR} to assure the reproducibility