Essays in Empirical Operations Management: Bayesian Learning of Service Quality and Structural Estimation of Complementary Product Pricing and Inventory Management

This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.

The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.

The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.

Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.

Essays in Macro Finance

I study the link between capital markets and sources of macroeconomic risk. In chapter 1 I show that expected inflation risk is priced in the cross section of stock returns even after controlling for cash flow growth and volatility risks. Motivated by this evidence I study a long run risk model with a built-in inflation non-neutrality channel that allows me to decompose the real stochastic discount factor into news about current and expected cash flow growth, news about expected inflation and news about volatility. The model can successfully price a broad menu of assets and provides a setting for analyzing cross sectional variation in expected inflation risk premium. For industries like retail and durable goods inflation risk can account for nearly a third of the overall risk premium while the energy industry and a broad commodity index act like inflation hedges. Nominal bonds are exposed to expected inflation risk and have inflation premiums that increase with bond maturity. The price of expected inflation risk was very high during the 70's and 80's, but has come down a lot since being very close to zero over the past decade. On average, the expected inflation price of risk is negative, consistent with the view that periods of high inflation represent a "bad" state of the world and are associated with low economic growth and poor stock market performance. In chapter 2 I look at the way capital markets react to predetermined macroeconomic announcements. I document significantly higher excess returns on the US stock market on macro release dates as compared to days when no macroeconomic news hit the market. Almost the entire equity premium since 1997 is being realized on days when macroeconomic news are released. At high frequency, there is a pattern of returns increasing in the hours prior to the pre-determined announcement time, peaking around the time of the announcement and dropping thereafter.