"FOMC Sentiment Extraction and its Transmission to Financial Markets"

 Since 2005, the Federal Open Market Committee (FOMC) has regularly released its minutes three weeks after its meetings. Previous research has found that the volatility of different financial market returns reacts to these releases, and the nature of these reactions may depend on the information the minutes contain. In this paper, I use Automated Content Analysis, adopted from computational linguistics and political science, to derive sentiments acquired from these FOMC meeting documents. I assign an index to the minutes in order to determine if the sentiments obtained from the information therein can be classified as hawkish (analogous to improving economic conditions and stronger inflationary pressures) or dovish (related to deteriorating economic outlook and subdued price changes). I compare the sentiments of the discussions in the minutes to the sentiments of information in corresponding FOMC statements released immediately after the meetings and calculate the surprise \linebreak component of the relative sentiments. I then evaluate how this news shock in the minutes impacts broad equity and real estate investment trust indices, as well as the exchange rate valuation of different world currencies against the U.S. Dollar.  My findings indicate that financial assets respond to the minutes based on the type of news shock they contain and that financial markets react more significantly during the FOMC's date-based policy guidance period.

Link to the paper

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

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 policy meetings in a document called the Greenbook, which is made available to the public only after a five year lag. Despite the resources used to create them, the Greenbook forecasts may not necessarily reflect the complete set of relevant economic information considered when making monetary policy decisions. These forecasts may also not be as relevant to the FOMC if policy determination does not account for projections of future economic indicators. Hence, I first investigate if policy determination considers forward looking information. Second, I examine whether publicly available forecast information, proxied by the Survey of Professional Forecasters, affect FOMC decision-making, or if the decisions depend on the private information of the FOMC. Lastly, I use Automated Content Analysis to decipher the sentiments of the information in the FOMC meeting statements and then evaluate whether publicly available forecasts are reflected in these statements. I find that a combination of publicly available inflation projections as well as contemporaneous Greenbook forecasts of unemployment play important roles in policy decisions. In contrast, sentiments obtained from the information in the FOMC statements are largely consistent with the projections obtained from the Greenbook.

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

The latest financial crisis has increased the interest in understanding how monetary policy  announcements impact financial markets. For the US there are several studies that cover this area of research, however, for emerging markets the number of studies is scarce. This paper studies how the Chilean stock market is affected by monetary policy announcements made by the Central Bank of Chile. In their monthly monetary policy meetings the Central Bank of Chile decides the monetary policy rate and circulates press releases that effectively explains their decision. The information contained in this document includes policy decisions for the current month, the central bank’s economic outlook, and the signals about likely future central bank policy decisions. We therefore examine these monetary policy changes and the corresponding additional information from the meeting statements. Using Automated Content Analysis, we identify the qualitative information from the statement releases of the Central Bank of Chile and create a quantitative measure for the signals indicating likely future monetary policy. This quantitative measure, which we call sentiment score - proxies for the monetary policy tilt. We then evaluate how the surprise component of the sentiment scores – together with unexpected policy changes - impact Chilean financial assets.