Research

Ph.D. Job Market Paper

“Climate Change Expectations: Evidence from Earnings Forecasts”

Click here for draft.

I study the effects of changes in climate change expectations on forecasts of cash flows of public firms. I use data on financial analysts’ forecasts of firm earnings, and local temperatures as shifters of their perception of climate change. Analysts experiencing warmer temperatures tend to issue more pessimistic forecasts. The effect is correlated with firm exposure to both regulatory and physical climate change risks. The sensitivity of forecasts to temperatures is more negative for carbon-intensive industries, while for firms in the renewable sector the effect has an opposite, positive, sign. The negative effect is related to firm exposure to physical climate risks as well, especially for some risks such as hurricanes and storms. This effect is amplified for analysts that directly experience extreme weather events, consistently with a mechanism related to the salience of climate change. Exploiting forecasts issued for different future horizons, I pin down the timing at which climate risks are expected to materialize. The reaction of forecasts to temperatures is concentrated in horizons between eight and ten quarters in the future.

Selected Work in Progress

“Persistence-based Portfolio Choice Along the FOMC Cycle”

with Fulvio Ortu and Federico Severino

The Federal Reserve holds two main sets of monetary policy meetings, the “Federal Open Market Committee” (FOMC) and the “Board Meetings”, which gather with six- week and two-week cadence respectively. Cieslak, Morse, and Vissing-Jorgensen (2019) show that the cadence of these meetings is associated with cycles of corresponding frequencies in stock markets. These can be fruitfully exploited through a portfolio strategy that invests in the whole market at alternate weeks (the even-week strategy). This simple investment rule is based on the cycles identified empirically but, so far, lacks a theoretical foundation. In this paper, we provide a rigorous framework to detect cycles in the stock market, and to determine optimal portfolio choices which profit from such cycles. We use the filtering approach for stationary time series of Ortu, Severino, Tamoni, and Tebaldi (2020) to isolate uncorrelated components of stock returns that are precisely associated with two- and six-week cycles. Then, we replicate these components using tradable assets from the U.S. market, and design an optimal portfolio strategy that maximizes the investor’s wealth and outperforms the even-week strategy.

“Retail Traders Through the Covid Pandemic: Evidence from Robinhood”

I analyze the investment decisions of a peculiar sample of retail investors around the Covid-19 pandemic, employing a novel data-set from a popular brokerage platform. After dividing the sample period into three periods – pre-pandemic, crash and recovery – I investigate which firm characteristics predict the popularity of stocks among retail traders. I find that these characteristics change across the three periods: since the onset of the pandemic, traders became attracted to stocks and sectors that performed the worst in the crisis. This behavior is distant from a flight to quality dynamic and rather consistent with a contrarian strategy. The findings have implications for financial stability as a larger fraction of the population gains access to financial markets through technology.