政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/137170

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/137170

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113656/144643 (79%)
Visitors : 51725342 Online Users : 594

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope

please add "double quotation mark" for query phrases to get precise results

please goto advance search for comprehansive author search

Adv. Search

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

Goto mobile version

政大典藏 > College of Commerce > MBA Program > Theses > Item 140.119/137170

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/137170

Title:	聯準會會議記要的文字分析 Text Mining on FOMC Minutes
Authors:	郭育丞 Kuo, Yu-Cheng
Contributors:	余清祥 Yue, Ching-Syang 郭育丞 Kuo, Yu-Cheng
Keywords:	文字分析聯準會會議紀要寫作風格探索式資料分析維度縮減 Text mining FOMC minutes Writing style Exploratory data analysis Dimension reduction
Date:	2021
Issue Date:	2021-09-02 18:18:40 (UTC+8)
Abstract:	本文探討聯邦公開市場委員會（Federal Open Market Committee，簡稱 FOMC）會議紀要（minutes）的文字風格，尋找不同的利率調整結果（升息、降息、利率不變）時的 FOMC會議紀要的用詞有哪些差異。本文研究資料為1993～2020年的FOMC會議紀要，以及同時期的目標聯邦基金利率（targeted federal funds rate）。透過探索式資料分析（exploratory data analysis）、文字分析（或稱文字探勘）的技術，比較FOMC會議紀要在升息、降息、利率不變時的會議紀要風格。分析發現，目標聯邦基金利率的調整並非隨機（亦即具有自相關性），經常出現連續幾期的升息、降息；文字使用，在不同利率調整時有不少差異，其中升息、降息的報告大多強調美國四大族裔的失業率。另外，由於分析會議紀要的原始文檔為高維度的文檔-詞頻矩陣（document-term matrix），考量多達 4102 個變數，除了具有稀疏矩陣（sparse matrix）的特質外，變數過多也會影響資料分析的效率。因此本文使用倍數指標篩選器、線性降維、非線性降維等方法，透過縮減特徵空間維度以提高執行效率，研究發現倍率指標的為度縮減效果最佳，配合羅吉斯迴歸得出之三分類準確率最高。 In this study, our goal is to explore the writing style of FOMC (Federal Open Market Committee) minutes. In particular, we want to know if the style of minutes shows significant differences when the FOMC decided to raise, lower, or hold interest rates. We applied exploratory data analysis and text mining techniques to the FOMC 1993~2020. We found that the adjustments of targeted federal funds rates are not randomly distributed and they show signs of correlation. For example, among the 39 times of raising interest rate, there was one 17 consecutive intertest increase. Also, the minutes tend to emphasis on the unemployment of four major ethnicities when FOMC decided to raise or lower interest rates. On the other hand, there are 4102 variables involved in exploring the writing study of FOMC minutes. This means that the document-term matrix is a sparse matrix and high dimensionality requires a lot of computation time. Thus, adopted dimensionality reduction techniques: multiplication index, linear reduction and non-linear reduction methods. We found that the multiplication index has the best performance and, together with logistic regression, it has the highest accuracy in classifying the writing style of FOMC minutes in the cases of raising, lowering and holding interest rates.
Reference:	一、中文文獻柏南克（2013）。《柏南克的四堂課：聯準會與金融危機》。臺北：財信。孫亮、黃倩（2017）。《實用機器學習》。北京：人民電郵。黃于珊（2017）。「文字探勘在總體經濟上之應用—以美國聯準會會議紀錄為例」，政治大學金融學系碩士論文。二、英文文獻 Abel, A. B., Bernanke, B., Croushore, D. (2013). Macroeconomics (8nd ed.). New Jersey, NJ: Pearson. Aggarwal, C. C. (2018). Machine Learning for Text. Cham, CH: Springer International Publishing. https://doi.org/10.1007/978-3-319-73531-3 Bernanke, B. (2012). “The Federal Reserve and the Financial Crisis: The Aftermath of the Crisis, Lecture 4.” George Washington University School of Business. https://www.federalreserve.gov/mediacenter/files/chairman-bernanke-lecture4-20120329.pdf Blinder, A. S., Ehrmann, M., de Haan, J., Fratzscher, M., & Jansen, D.-J. (2008). “Central bank communication and monetary policy: A survey of theory and evidence,” Journal of Economic Literature, 46, 910–945. https://doi.org/10.1257/jel.46.4.910 Board of Governors of the Federal Reserve System. (2021, January 14). “Federal Open Market Committee: Transcripts and other historical materials.” Board of Governors of the Federal Reserve System. https://www.federalreserve.gov/monetarypolicy/fomc_historical.htm Boukus, E., & Rosenberg, J. V. (2006). “The information content of FOMC minutes.” Federal Reserve Bank of New York. https://doi.org/10.2139/ssrn.922312 Cannon, S. (2015). “Sentiment of the FOMC: Unscripted,” Economic Review [Federal Reserve Bank of Kansas City], Fall 2015, pp. 55. Chollet, F. (2018). Deep learning with Python. Shelter Island, NY: Manning Publications. Doh, T., Song, D., & Yang, S. K. (2020). “Deciphering Federal Reserve Communication via Text Analysis of Alternative FOMC Statements.” Federal Reserve Bank of Kansas City, Research Working Paper no. 20-14, October. https://doi.org/10.18651/RWP2020-14 Ericsson, N. R. (2016). “Eliciting GDP forecasts from the FOMC’s minutes around the financial crisis,” International Journal of Forecasting, 32, 571–583. https://doi.org/10.1016/j.ijforecast.2015.09.007 Ganegedara, T. (2018). Natural Language Processing with TensorFlow. Birmingham, UK: Packt Publishing. Hayoa, B., & Neuenkirch, M. (2013). “Do Federal Reserve Presidents Communicate with a Regional Bias?” Journal of Macroeconomics, 35(4), 62–72. https://doi.org/10.1016/j.jmacro.2012.10.002 Huang, Y. L., & Kuan, C. M. (2021). “Economic Prediction with the FOMC Minutes: An Application of Text Mining,” International Review of Economics & Finance, 71, 751-761. https://doi.org/10.1016/j.iref.2020.09.020 Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice (2nd ed.). Melbourne, AU: OTexts. https://otexts.com/fpp2/ Joshi, P. (2016). Python Machine Learning Cookbook. Birmingham, UK: Packt Publishing. Jubinskia, D., & Tomljanovich, M. (2013). “Do FOMC Minutes Matter to Markets? An Intraday Analysis of FOMC Minutes Releases on Individual Equity Volatility and Returns,” Review of Financial Economics, 22(3), 86–97. https://doi.org/10.1016/j.rfe.2013.01.002 Kliesen, K. L., Levine, B., & Waller, C. J. (2019). “Gauging Market Responses to Monetary Policy Communication,” Federal Reserve Bank of St. Louis Review, pp. 69-91. https://doi.org/10.20955/r.101.69-91 Lucca, D. O., & Trebbi F. (2009). “Measuring Central Bank Communication: An Automated Approach with Application to FOMC Statements.” NBER Working Paper, No. 15367. https://doi.org/10.2139/ssrn.1470443 Mikolov, T., Sutskever, I., Chen, K., Corrado, G. and Dean, J. (2013). “Distributed Representations of Words and Phrases and their Compositionality.” Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS’13, Curran Associates, Red Hook, NY, Vol. 2, pp. 3111-3119. Rosa, C. (2013). “The Financial Market Effect of FOMC Minutes,” FRBNY Economic Policy Review, 19(2), 67–81. https://ssrn.com/abstract=2378398 Sarkar, D. (2019). Text Analytics with Python (2nd ed.). Bangalore, India: Apress. Shapiro, A.H., & Wilson, D.J. (2019). “Taking the Fed at its Word: A New Approach to Estimating Central Bank Objectives using Text Analysis.” Federal Reserve Bank of San Francisco Working Paper 2019-02. https://doi.org/10.24148/wp2019-02 Stekler, H., & Symington, H. (2016). “Evaluating qualitative forecasts: The FOMC minutes, 2006–2010,” International Journal of Forecasting, 32, 559–570. https://doi.org/10.1016/j.ijforecast.2015.02.003 VanderPlas, J. (2017). Python Data Science Handbook. California, CA: O’Reilly Media.
Description:	碩士國立政治大學企業管理研究所(MBA學位學程) 107363015
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0107363015
Data Type:	thesis
DOI:	10.6814/NCCU202101452
Appears in Collections:	[MBA Program] Theses

Files in This Item:

File	Description	Size	Format
301501.pdf		11564Kb	Adobe PDF2	117	View/Open

All items in 政大典藏 are protected by copyright, with all rights reserved.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - Feedback