Research

WORKS IN PROGRESS:

"FOMC Minutes Sentiment Surprises and their Impact on Financial Markets"

 I develop a semi-automated method that systematically evaluates the information in FOMC meeting documents. This method highlights the economic conditions as discussed in policy documents to create sentiment indices that proxy for the FOMC's interest rate tilt. I compare the sentiment index of the discussions in the minutes to the sentiment index of the information in their corresponding FOMC statements. Considering these two types of sentiment indices, I calculate the surprise component of the minutes' sentiments. Using high-frequency data, I then examine how these surprises, which I refer to as news shocks, impact financial markets. In particular, I evaluate the impact of news shocks on fed funds futures, broad equity and real estate investment trust indices, and exchange rate valuation of several major currencies against the U.S. Dollar. My findings indicate that financial assets respond significantly to the surprise sentiments of the minutes, especially after the FOMC implemented its calendar-based forward guidance.


Link to the paper



"Monetary Policy Effects on the Chilean Stock Market: A Semi-Automated Content Approach" (Work with Mario Gonzalez)

The Central Bank of Chile determines Chile's Monetary Policy Rate and circulates press releases that effectively explain the decision after each of its monthly policy meeting. The information contained in these press releases includes policy decisions for the current month, the central bank’s economic outlook, and signals about likely future central bank policy decisions. In the current work, we examine the monetary policy rate decisions and the corresponding additional information from the meeting statements. Using Semi-automated Content Analysis, we identify qualitative information from the statement releases of the Central Bank of Chile and create a quantitative measure, which we call the sentiment score,  based on the economic discussions in these policy documents. We then evaluate how the surprise component of the sentiment scores – together with unexpected policy changes - impact Chilean financial assets. We find that the sentiment scores affect financial markets beyond simply altering expectations about the yield curve.

Link to the paper


"Forward – Looking Monetary Policy and the Contributions of Public Expectations"

The staff of the Federal Reserve Board of Governors uses a significant amount of resources to obtain forecasts of macroeconomic conditions. The Federal Open Market Committee (FOMC) members receive these forecasts shortly before their scheduled monetary policy meetings, but only makes these forecasts available to the public after a five year lag. Despite the resources used to create them, the staff forecasts may not necessarily represent all of the relevant economic information considered when making monetary policy decisions. Hence, I analyze publicly available forecast information, which is created independently of staff forecasts and is proxied by the Survey of Professional Forecasters. I examine whether these widely available forecasts represent information that affect FOMC decision-making, or if the policy decisions depend solely on the private information of the FOMC. Moreover, I use Semi-Automated Content Analysis to decipher the sentiments of the information in the FOMC meeting statements and then evaluate the type of forecasts reflected in these statements. I find that a combination of publicly available inflation projections and contemporaneous staff forecasts of unemployment best portrays macroeconomic information that affects policy decisions. In contrast, sentiments obtained from the information in the FOMC statements are largely consistent with the staff projections.