Fundamental Investment Analysis

The use of order flow information by financial firms has come to the forefront of the regulatory debate. A central question is: Should a dealer who acquires information by taking client orders be allowed to use or share that information? We explore how information sharing affects dealers, clients and issuer revenues in U.S. Treasury auctions. Because one cannot observe alternative information regimes, we build a model, calibrate it to auction results data, and use it to quantify counter-factuals. The model's key force is that sharing information reduces uncertainty about future value.

Using high-frequency transaction-level income, spending, balances, and credit limits data from an online financial service, we show that many consumers fail to stick to their self-set debt paydown plans and argue that this behavior is best explained by a model of present bias. Theoretically, we show that (i) a present-biased agent's sensitivity of consumption spending to paycheck receipt reflects his or her short-run impatience and that (ii) this sensitivity varies with available resources only for agents who are aware (sophisticated) rather than unaware (naive) of their future impatience.

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.

Many research papers in household finance utilize annual snapshots of household wealth from administrative data, such as tax registries, to calculate "imputed consumption." However, trading costs, unobserved intra-year trades, or unobserved security characteristics may cause measurement error. We document how such errors vary across groups of individuals by income, portfolio characteristics, and wealth and how they are correlated with individual income and balance sheets, asset prices, and the business cycle using transaction-level retail brokerage account data.

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.

We document that governments whose local currency debt provides them with greater hedging benefits actually borrow more in foreign currency. We introduce two features into a government's debt portfolio choice problem to explain this finding: risk-averse lenders and lack of monetary policy commitment. A government without commitment chooses excessively countercyclical inflation ex post, which leads risk-averse lenders to require a risk premium ex ante.

The SEC should reform 144A regulation to prevent Wall Street streaking further ahead.

"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.

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.