报告题目：Reward-Timing Uncertainty and R&D Investment
Abstract:In contrast to the common belief that uncertainty decreases investment, we show that uncertainty in the timing of future reward actually increases R&D investment.This surprising result arises from the convexity of the time discount function, which we show theoretically through an illustrative model.To measure reward-timing uncertainty empirically, we use a special structure of languages–future-time reference (FTR)–which refers to when and how languages mark the timing of future events. We find at the country, firm, and CEO level that weak-FTR language speakers perceive higher reward-timing uncertainty and pursue more R&D investment. The results are robust to various model specifications and endogeneity checks.
报告人简介：Dr. Jianxin Daniel Chi is a tenured Associate Professor at University of Nevada, Las Vegas. He has broad research interest in corporate finance, including corporate governance, corporate liquidity management, product market competition, executive compensation, firm valuation, mergers and acquisitions, and earnings management. He has taught a wide range of finance classes at the undergraduate and graduate level, including corporate finance, investments, financial modeling, and financial markets and institutions. His research has appeared in respected finance journals, such as the Journal of Financial and Quantitative Analysis, Financial Management, and the Journal of Banking and Finance. He regularly presents his research at international conferences and seminars. He has received numerous research grants, including grant from The National Natural Science Foundation of China. He has also received UNLV Department of Finance’s Research Award and Teaching Award. Before joining UNLV in 2011, he has been on the finance faculty at Arizona State University and North Dakota State University. He received his Ph.D. in Finance from Texas A&M University. Before joining academia, Dr. Chi had professional experience in international business, marketing, business analysis, and operations management.