Information Extraction from Federal Open Market Committee Statements (2020)

Abstract: We present a novel approach to unsupervised information extraction by identifying and extracting relevant concept-value pairs from textual data. The system’s building blocks are domain agnostic, making it universally applicable. In this paper, we describe each component of the system and how it extracts relevant economic information from U.S. Federal Open Market Committee (FOMC) statements. Our methodology achieves an impressive 96% accuracy for identifying relevant information for a set of seven economic indicators: household spending, inflation, unemployment, economic activity, fixed in-vestment, federal funds rate, and labor market.

## introduction while there are many information extraction approaches described in the literature @xcite , there are few that can extract targeted concept-value pairs with sentiment scores and are universally applicable to various data types. in addition, many systems require training data or additional corpora to draw meaningful statistics from. we built a framework that does not make use of predefined language patterns or any additional training data to extract targeted concept-value pairs with sentiment scores, across various data types. in addition, the framework provides a means to derive a semantic representation of unstructured data along with supporting sentiment analysis. we applied it to extract a set of economic concepts and their associated values in fomc statements. financial events and information relevant to stakeholders are disseminated at constantly, yielding massive amounts of textual data. in recent years, unstructured textual data has become an important source of information for various financial systems, @xcite . while some textual data is created at high frequencies, e.g., news headlines, others as-necessary, e.g. sec foot_1 filings, others are generated at pre-defined time intervals, e.g. fomc statements. the statements appear roughly every 6 weeks but if special circumstances arise, like the covid-19 pandemic at the beginning of 2020, additional statements are released accordingly. each fomc statement lays out the direction of u.s. monetary policy. these statements include the federal funds rate, one of the most important economic indicators. in addition, they contain information about inflation, unemployment, economic growth, and monetary supply. the language used in these statements is carefully selected; every word included is intentional due to the market moving effects of these statements. changes in wordings have shown to bring major effects on markets @xcite and directions of market movements have shown to be correlated with statement sentiment scores @xcite . in essence, the most important economic aspects in the statement are federal funds rate, household spending, inflation, unemployment, economic activity, fixed investment, and labor market conditions. identifying and extracting these concepts along with what is said about them would not only provide a semantic representation of the statement but give rise to a fast integration, as soon as release time, of crucial economic data. results can be easily and timely integrated into decision making processes, without any human effort. they can be used to derive short-and long-term interest rates, foreign exchange rates, employment rates, economic output, and the supply of credit and demand for investment. furthermore, providing sentiment analysis for each economic concept will make the extracted information more valuable to both humans and financial systems. in the next sections we are going to present our information extraction system, each of its components, and how we applied it to extract economic concept-value pairs from fomc statements. ## problem statement in this study we propose an unsupervised information extraction framework that is able to extract concept-value pairs from textual data. in addition, our approach differentiates itself from other methods in that it also assigns a sentiment score to each extracted pair. unsupervised extraction refers to the fact that no training set was provided to the system at the time it was built. we built this system to extract seven economic concepts and their values from fomc statements. while the framework is universal and can be applied to any other unstructured textual data without further tuning, we anchor the system's description around fomc statements. to show that we can apply the framework to other types of data, in the results section we present system outputs on a sample of short news articles. ## related work while there is a rich body of literature on information extraction, @xcite there are not many studies that address a concept-value pair extraction task with sentiment analysis for financial text. sentiment analysis for financial data has been a hot research interest for both natural language processing and finance fields. there is a large body of literature on how to perform this task with solutions falling in two main categories: lexical resource-based, @xcite and machine-learning based, @xcite . the financial domain tends to prefer the former mainly due to ease of use and efficiency, @xcite . our methodology, unlike related research, makes use of the extraction layer to derive better sentiment scores. we have tried off-the shelf solutions for adding sentiment scores but none of the options were acceptable, @xcite . a large majority of off-the-shelf solutions are built on product reviews, making them unsuitable for financial data, mostly due to poor coverage and resulting neutral sentiment scores. the literature, mostly on identification of features of product reviews @xcite ) makes use of similar building blocks, but our methodology does not heavily rely on problem constraints e.g., use of opinion or sentiment words for aspect identification or it does not yield as comprehensive output as ours. also, systems that return sentiment scores for very short texts, even for financial data are not widely supported. while we make use of a pre-existing lexical resource, we enhance it with a graphbased methodology to weigh in the importance of each word a technique we have not seen previously. therefore, the end-to-end architecture that we propose becomes a complete coherent solution. in terms of fomc sentiment analysis our method stands out in comparison to other research for the fact that we assign a sentiment score to individual economic concepts rather than to the statement as a whole (cannon. 2015). we provide individual sentiment scores for the most salient economic aspects mentioned in the statement, giving users more powerful decision tools. while there is a considerable body of literature around fomc analysis, a substantial amount is focused on mining meeting notes rather than the statement itself, e.g., identify covered topics in minutes @xcite and predict economic indicators like equity and interest rate volatilities @xcite . compared to other studies on fomc statement analysis, our solution brings improvements in terms of both methodology and granularity of the information it extracts. ## methodology to this point, we have laid the groundwork of the solution proposed and the problem tackled. in this section, we are going to detail our proposed methodology and each building block it contains. our concept-value pair information extraction system is rooted in dependency parsing outcome, with each subsequent step making use of the previous one, all leading up to a sentiment analysis and a concept-value pair filtering step. in a nutshell, the main components our framework makes use of are as follows: ## results for each given statement and for each economic factor, the system will return a single best conceptvalue pair with a sentiment score. when measuring the system's performance on how well it is able to identify and extract the correct pairs, both precision and recall stand at 96%. these results are manually obtained on a dataset of 30 statements of 5 years, 2016 to 2020, inclusive. the cases where the method is not identifying or extracting the correct information are mostly due to one of the following reasons: the quality of the parsing output, search match, e.g. while the method extracts the pair "investment -advanced more quickly in recent months" the filtering step fails to select it due to missing string match with "fixed investment", and occasionally large variations in the language of the statement that do not allow the method to extract the complete relevant value. the default system output is an .xlsx file that contains the extracted concept-value pairs and their sentiment scores. this format can be easily integrated with automatic systems. in addition, to show how the extracted information can be used for human consumption, figure 3 presents results obtained over a period of 8 years, 2013 to 2020, for two of the economic factors. we used bars to represent federal-funds rates, one of the most important economic information, and labels to depict economic factors values. we color coded the bars to reflect the sentiment scores, red color encodes a positive sentiment while blue a negative one. sentiment intensity is reflected in the darkness of the color. in addition, the lower part of the plot captures the sentiment score throughout the years providing users with a historic view of how the sentiment of an economic factor changes and how it is correlated with the federal funds rate. as we can observe, our method is not only able to identify and surface the right information but also provides a sentiment analysis history for the economic factors. this aspect in itself represents a valuable source of information for financial systems that try to predict the type of economy we could expect. more so, the extracted concept-value pairs can provide a semantic representation of an fomc statement given that it captures the most salient information. revealing such a semantically charged representation allows both humans and financial systems to easily compare and contrast how various economic aspects change throughout the years. for a more complete analysis we run our methodology on news articles. figure 4 presents results on the following news body "the u.s. dollar declined against most major foreign currencies yesterday, although the drop was softened when bond prices failed to advance tuesday's rally. the dollar began weakening in europe as interest rates fell there for dollar deposits. the decline continued in new york trading, which was thin, although the dollar recovered slightly when bond prices began falling. lower bond prices translate into higher long-term interest yields, which make dollar denominated investments more attractive. the bond market later closed littlechanged from tuesday. 'this is the first time in a while that we've gone back to trading off interest rates, and my feeling is it will continue between now and the (u.s.) election,' said daniel holland, an assistant vice president at discount corp., new york. in late new york trading, the dollar fell to 3.0210 west german marks from 3.0318 marks on tuesday. the british pound rose to @xmath01.2155. in early tokyo trading thursday, the dollar strengthened against the japanese currency, to 245.45 yen from 245.13 yen late yesterday in new york." foot_3 the extracted key-value pairs show that we can easily obtain a semantic summary of what has been mentioned in the body along with the sentiment it conveys. results support the fact that our methodology extracts important information from a variety of textual data. ## conclusions in this study we presented a novel unsupervised information extraction system with sentiment analysis. our proposed architecture is robust, as it can be extended to support new targeted concepts, it can easily adapt to user-defined constraints, and it can be tuned to vary the amount of information it returns. we achieve 96% accuracy at extracting relevant concept pairs from fomc statements and we show that we can use the same methodology on completely different data than the one used for tuning.