Understanding Interest Volatility from FOMC Meeting Proceedings

This study measures the effects of Federal Open Market Committee (FOMC) communications on interest rates. We find that, in recent years, the typical FOMC statement moved mid-term interest rates by 1.1 to 2.8 basis points (bps) up or down. If a press conference is held the day of the meeting, this movement is 0.9 bps greater. Releasing meeting minutes three weeks later only moves interest rates if the FOMC meeting was not followed by a press conference, indicating that the press conference and meeting minutes contain duplicate information. We propose this measure of the volatility effect of the communication itself as a check on the accuracy of text-mining methods that measure the market effects of specific words or sentiments. Next, we identify keywords describing sentiments conveyed and topics discussed in FOMC meetings. Frequencies of these words appear to predict interest rate movements in a regression context. However, two pieces of evidence suggest that these regressions overfit the interest rate data. First, the regression-predicted interest rate movements far exceed the average market effects of an entire statement or press conference. Second, if the frequencies of our keywords summarize the market-moving content in the data— as the regression results suggest—and if the press conferences and minutes contain duplicate information—as our first results indicate—then we would expect the keyword frequencies to be correlated between the press conferences and minutes.

Publications

  • Christopher Rohlfs, Sunandan Chakraborty, Lakshminarayanan Subramanian, (2016). " The Effects of the Content of FOMC Communications on US Treasury Rates ", EMNLP 2016, Austin, USA [PDF]
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Extraction of (Key,Value) Pairs from Unstructured Ads

This work is about the problem of extract- ing structured labeled data from short unstructured ad- postings from online sources like Craigslist, where ads are posted on various topics, such as job post- ings, rentals, car sales etc. A fundamental challenge in addressing this problem is that most ad-postings are highly unstructured, short-text postings written in an informal manner with no inherent grammar or well- defined dictionary. We used unsuper- vised and supervised algorithms for extracting struc- tured data from unstructured ads in the form of (key, value) pairs where the keys naturally represent topic- specific features in the ads. The unsupervised algorithm is centered around building an affinity graph, using the words from a topic-specific corpus of such ads where the edge weights represent affinities between words; the (key, value) extraction algorithm identifies specific groups of words in the affinity graph corresponding to different classes of key attributes. The supervised al- gorithm uses a Conditional Random Field based train- ing algorithm to identify specific structured (key, value) pairs based on pre-defined topic-specific structural data representations of ads.

Publications

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Location Specific Summarization of Climatic and Agricultural Trends


 

Agriculture forms the backbone of several emerging economies. In the past few years, several agrarian regions have been severely affected, due to a combination of several factors including climate, lack of water availability, soil infertility etc. However, in reality, many policymakers and the general public are often unaware of the status of agricultural conditions across different localities within their countries. We have designed a system that automatically constructs a location-specific climatic and agricultural information aggregation and summarization portal based on disparate information sources from the Web. Given a location, the system searches the Web for information concerning different parameters influencing agriculture and climate and presents a summary of relevant information.

Our system is built around three key ideas. First, we (manually) identify target topics of interest within climate and agriculture (such as soil, water) and construct a list of appropriate search queries that comprehensively describe the different aspects of the target topic. Second, for each target topic (such as soil or water), we download the top search result pages and perform information extraction on the textual content of these pages. The information extraction process aims to extract the critical textual snippets that can capture the key trends within the target area. Finally, we perform information summarization where the goal is to identify key trends corresponding to each target topic. We have tailored standard information retrieval techniques to address these problems. This summarized information on the location can be utilized to detect different problems and infer possible remedies from it. Hence, the aim is to bring to fore the important as well as lesser known facts, thereby increasing the availability of knowledge.

Publications

  • Sunandan Chakraborty, Lakshminarayanan Subramanian, (2011). "Location Specific Summarization of Climatic and Agricultural Trends", WWW 2011, Hyderabad, India
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