Decision Making & Negotiations

Expectations Investing offers a unique and powerful alternative for identifying value-price gaps. Rappaport and Mauboussin provide everything the reader needs to utilize the discounted cash flow model successfully. And they add an important twist: they suggest that rather than forecasting cash flows, investors should begin by estimating the expectations embedded in a company's stock price. An investor who has a fix on the market's expectations can then assess the likelihood of expectations revisions.

One of the central challenges in online advertising is attribution, namely, assessing the contribution of individual advertiser actions including e-mails, display ads and search ads to eventual conversion. Several heuristics are used for attribution in practice; however, there is no formal justification for them and many of these fail even in simple canonical settings. The main contribution in this work is to develop an axiomatic framework for attribution in online advertising.

We study the following fundamental data-driven pricing problem. How can/should a decision-maker price its product based on observations at a single historical price? The decision-maker optimizes over (potentially randomized) pricing policies to maximize the worst-case ratio of the revenue it can garner compared to an oracle with full knowledge of the distribution of values, when the latter is only assumed to belong to broad non-parametric set. In particular, our framework applies to the widely used regular and monotone non-decreasing hazard rate (mhr) classes of distributions.

We study how investors' preferences for robustness influence corporate investment, financing, and compensation decisions and valuation in a financial contracting model with agency. We characterize the robust contract and show that early liquidation can be optimal when investors are sufficiently ambiguity averse. We implement the robust contract by debt, equity, cash, and a financial derivative asset. The derivative is used to hedge against the investors' concern that the entrepreneur may be overly optimistic.

We examine how choice bracketing affects expected value maximization in experience-based choice. Experience-based choices are a series of individual choices made sequentially, for which feedback follows each choice, and are thus naturally bracketed narrowly. Previous research broadly bracketed multiple experience-based choices for decision makers by aggregating the choices (such that each choice pertained to multiple individual choices) or by reducing feedback frequency.

This research highlights how gender shapes consumer payments in pay-what-you-want contexts. Four studies involving hypothetical and real payments show that men typically pay less than women in pay-what-you-want settings, due to gender differences in agentic versus communal orientation. Men approach the payment decision with an agentic orientation, and women approach it with a communal orientation. These orientations then shape payment motives and ultimately affect payment behavior.

This research provides a first investigation into how interest-free financing promotions influence consumer behavior. Five experiments demonstrate that framing an economically equivalent financing offer in a way that makes salient that it is interest-free increases consumers’ demand for credit to finance experiential, but not material goods.

We evaluate the impacts of tax policy on asset returns using the U.S. municipal bond market. In theory, tax-induced ownership segmentation limits risk-sharing, creating downward-sloping regions of the aggregate demand curve for the asset. In the data, cross-state variation in tax privilege policies predicts differences in in-state ownership of local municipal bonds; the policies create incentives for concentrated local ownership. High tax privilege states have muni-bond yields that are more sensitive to variations in supply and local idiosyncratic risk.

We study the problems of offline and online contextual optimization with feedback information, where instead of observing the loss, we observe, after-the-fact, the optimal action an oracle with full knowledge of the objective function would have taken. We aim to minimize regret, which is defined as the difference between our losses and the ones incurred by an all-knowing oracle.