Operations & Supply Chain Management

Deciding between the use of market orders and limit orders is an important question in practical optimal trading problems. A key ingredient in making this decision is understanding the uncertainty of the execution of a limit order, that is, the fill probability or the probability that an order will be executed within a certain time horizon. Equivalently, one can estimate the distribution of the time-to-fill. We propose a data-driven approach based on a recurrent neural network to estimate the distribution of time-to-fill for a limit order conditional on the current market conditions.

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.

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.

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.

Auctions are widely used in practice. While also extensively studied in the literature, most of the developments rely on the significant common prior assumption. We study the design of optimal prior-independent selling mechanisms: buyers do not have any information about their competitors and the seller does not know the distribution of values, but only a general class it belongs to.

Technology is often embodied in expensive and indivisible capital goods. As a result, the small scale of firms in developing countries could hinder investment and productivity. This paper argues that market interactions between small firms can alleviate this concern. We design and implement a survey of manufacturing firms in Uganda, which uncovers an active rental market for large machines among small firms. We then build an equilibrium model of firm behavior and estimate it with our data.

Matt Frisch, VP of Corporate Development for Canadian pharmaceutical wholesaler Kohl & Frisch, had successfully led the charge to buy out US-based rival AmerisourceBergen Canada (ABC). ABC's aggressive price cuts had disrupted the industry -- before squeezing its own revenues to the point where leaders at its Pennsylvania headquarters decided to divest the Canadian unit rather than subsidize an unprofitable operation.

Objectives:

To examine whether and how step-down unit admission after ICU discharge affects patient outcomes.

Design:

Retrospective study using an instrumental variable approach to remove potential biases from unobserved differences in illness severity for patients admitted to the step-down unit after ICU discharge.

Setting:

Ten hospitals in an integrated healthcare delivery system in Northern California.

Patients: