Alan Moreira

We study disruptions in debt markets during the COVID-19 crisis. The safer end of the credit spectrum experienced significant losses that are hard to fully reconcile with standard default or risk premium channels. Corporate bonds traded at a large discount to their corresponding CDS, and this basis widened most for safer bonds. Liquid bond ETFs traded at a large discount to their NAV, more so for Treasuries, municipal bonds, and investment-grade corporate than high-yield corporate. These facts suggest investors tried to sell safer, more liquid securities to raise cash. These disruptions disappeared nearly as fast as they appeared. We trace this recovery back to the unprecedented actions the Fed took to purchase corporate bonds rather than its interventions in extending credit. The March 23...

Text data is ultra-high dimensional, which makes machine learning techniques indispensable for textual analysis. Text is often selected—journalists, speechwriters, and others craft messages to target their audiences’ limited attention. We develop an economically motivated high dimensional selection model that improves learning from text (and from sparse counts data more generally). Our model is especially useful when the choice to include a phrase is more interesting than the choice of how frequently to repeat it. It allows for parallel estimation, making it computationally scalable. A first application revisits the partisanship of US congressional speech. We find that earlier spikes in partisanship manifested in increased repetition of different phrases, whereas the upward trend starting ...

Managed portfolios that take less risk when volatility is high produce large alphas, substantially increase factor Sharpe ratios, and produce large utility gains for mean-variance investors. We document this for the market, value, momentum, profitability, return on equity, and investment factors in equities, as well as the currency carry trade. Volatility timing increases Sharpe ratios because changes in factor volatilities are not offset by proportional changes in expected returns. Our strategy is contrary to conventional wisdom because it takes relatively less risk in recessions and crises yet still earns high average returns. This rules out typical risk-based explanations and is a challenge to structural models of time-varying expected returns.

Published: ALAN MOREIRA & TYLER MUIR, 2017. "Volatility-Managed Portfolios," The Journal of Finance, vol 72(4), pages 1611-1644.

We build a macroeconomic model that centers on liquidity transformation in the financial sector. Intermediaries maximize liquidity creation by issuing securities that are money-like in normal times but become illiquid in a crash when collateral is scarce. We call this process shadow banking. A rise in uncertainty raises demand for crash-proof liquidity, forcing intermediaries to delever and substitute toward safe, collateral- intensive liabilities. Shadow banking shrinks, causing the liquidity supply to contract, discount rates and collateral premia spike, prices and investment fall. The model produces slow recoveries, collateral runs, and flight to quality and it provides a framework for analyzing unconventional monetary policy and regulatory reform proposals.

Published: ALAN MOREIRA & ALEXI SAVOV, 2017. "The Macroeconomics of Shadow Banking," The Journal of Finance, vol 72(6), pages 2381-2432.