Strategy

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.

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.

Along with its interrelated companion volume, The Content, Impact, and Regulation of Streaming Video, this book covers the next generation of TV—streaming online video, with details about its present and a broad perspective on the future. It reviews the new technical elements that are emerging, both in hardware and software, their long-term trend, and the implications. It discusses the emerging ‘media cloud’ of video and infrastructure platforms, and the organizational form of such TV.

Along with its interrelated companion volume, The Technology, Business, and Economics of Streaming Video, this book examines the next generation of TV—online video. It reviews the elements that lead to online platforms and video clouds and analyzes the software and hardware elements of content creation and interaction, and how these elements lead to different styles of video content.

Utilizing transaction-level financial data, we explore how household consumption responded to the onset of the COVID-19 pandemic. As case numbers grew and cities and states enacted shelter-in-place orders, Americans began to radically alter their typical spending across a number of major categories. In the first half of March 2020, individuals increased total spending by over 40% across a wide range of categories.

The largest economic cost of the COVID-19 pandemic could arise from changes in behavior long after the immediate health crisis is resolved. A potential source of such a long-lived change is scarring of beliefs, a persistent change in the perceived probability of an extreme, negative shock in the future. We show how to quantify the extent of such belief changes and determine their impact on future economic outcomes. We nd that the long-run costs for the U.S. economy from this channel is many times higher than the estimates of the short-run losses in output.

This paper estimates a structural model of unconventional monetary policy transmission through bank balance sheets using cross-sectional instruments for loan and deposit demand. We estimate the demand for banking at a branch-specific level from the response of a bank's quantities at one branch to interest rate changes caused by demand shocks at other branches. Depositors are considerably less sensitive to interest rates than corporate or mortgage borrowers.

The Great Recession was a deep downturn with long-lasting effects on credit, employment and output. While narratives about its causes abound, the persistence of GDP below pre-crisis trends remains puzzling. We propose a simple persistence mechanism that can be quantified and combined with existing models. Our key premise is that agents don't know the true distribution of shocks, but use data to estimate it non-parametrically. Then, transitory events, especially extreme ones, generate persistent changes in beliefs and macro outcomes.

"Big data" financial technology raises concerns about market inefficiency. A common concern is that the technology might induce traders to extract others' information, rather than to produce information themselves. We allow agents to choose how much they learn about future asset values or about others' demands, and we explore how improvements in data processing shape these information choices, trading strategies and market outcomes. Our main insight is that unbiased technological change can explain a market-wide shift in data collection and trading strategies.