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Title: | 各類新聞與正負面情緒對房市之影響:文字探勘之應用 Application of Text Mining: The Influence of Media Sentiment on Real Estate Market By Different News Topics and Positive/Negative Sentiment |
Authors: | 郭偉傑 Kuo, Wei-Chieh |
Contributors: | 陳明吉 郭偉傑 Kuo, Wei-Chieh |
Keywords: | 文字探勘 情緒分析 房地產市場 Text Mining Sentiment Analysis Real Estate Market |
Date: | 2020 |
Issue Date: | 2020-08-03 17:34:36 (UTC+8) |
Abstract: | 本研究透過於聯合知識庫蒐集2009年至2018年有關房市、股市、勞動市場與人口統計新聞共計70,533篇,並應用文字探勘與情緒分析技術,利用財金領域辭典作為分析情感的依據,計算各個不同新聞主題每月所隱含的情緒指標,來研究房市參與者會受到哪些主題的新聞所影響進而做出相對應的房市交易決策行為改變房價、房屋交易量、房屋流通天數與房屋議價空間。另外為了分析房市交易資訊是否具有正負面影響力不同的情形,本研究在計算情緒指標上也額外分別建立了正面與負面的情緒指標,來探討房市參與者較容易受到正面亦或是負面情緒所影響;此外,本研究為了探討媒體情緒與房市交易資訊之因果關係,亦採用Granger因果關係檢定來進行驗證。
本研究發現,房市媒體情緒將能顯著影響下一期的房價、房屋交易量、流通天數與議價空間,而將房市主題拆分為更細的主題後,也發現如租屋、房屋供給、房市政策與房市信用狀況新聞媒體情緒皆能顯著影響下一期的房價。除了房市以外的市場中,本研究也發現股市、勞動市場、人口統計媒體情緒也會顯著影響下一期房價,是故本研究證實了不只房市新聞媒體情緒將會顯著影響下一期房市交易資訊,若將房市媒體情緒做更細緻的拆分或是納入不同市場的媒體情緒,也能對未來房市交易資訊具有顯著的影響。
正負面媒體情緒中,本研究發現許多不同主題新聞中正面情緒與負面情緒影響力不同之現象;因果關係驗證上,本研究發現房市媒體情緒波動會造成房價、房屋交易量、房屋流通天數與房屋議價空間之改變,具有顯著因果關係。 Base on the vigorous development of text mining and sentiment analysis in recent years, it has also been gradually applied in various financial markets. This research collect news about the housing market, stock market, labor market and demographics from 2009 to 2018 via Udndata.com and capture 70,533 articles. Through text mining and sentiment analysis techniques, we constructed a series of monthly sentiment for every news topic and examine the relationship between the media sentiment and the housing market. Besides, we also separately established positive and negative sentiment index to explore whether housing market participants are more susceptible to positive or negative sentiment. In the end, we also used causality test to check the relation between sentiment and the houseing market.
The empirical results shows that the housing market sentiment will significantly affect the trading volume and the wiggle room in the next period. Also, after splitting the housing market media sentiment into more detailed themes, it also found that such as rental, housing supply, housing market policy and the credit situation media sentiment can significantly affect the house prices. In markets other than the housing market, we also found that stock market, labor market, demographic media sentiment will also significantly affect the house prices. To conclude this study, we confirmed that not only the housing market news media sentiment but also stock market, labor market and demographics media sentiment significantly affect the housing market. Besides, we found the Positive/Negative sentiment influence the housing market differently. In the end, we also found house media sentiment would Granger cause the housing market. |
Reference: | 英文參考文獻 Baker, M., Wurgler, J., (2006), Investor sentiment and the cross‐section of stock returns, Journal of Finance, 61, 1645–1680. Ball-Rokeach, S., DeFleur, M. L., (1976), A Dependency Model of Mass Media Effects. Communication Research, 3(1), 3-21. Barber, B., & Odean,T., (2008), All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors. The Review of Financial Studies, 21(2), 785-818. Baumeister, R., Bratslavsky, E., Finkenauer, C., Vohs, K. D., (2001), Bad is stronger than good. Review of General Psychology, 5, 323–370. Beracha, E., Wintoki, B., (2013), Forecasting residential real estate price changes from online search activity, Journal of Real Estate Research, 35, 283-312. Boiy, E., Moens, M. F., (2009), A machine learning approach to sentiment analysis in multilingual web texts, Information Retrieval, 12, 526-558. Calomiris, C. W., & Mamaysky, H., (2019), How news and its context drive risk and returns around the world. Journal of Financial Economics, 133, 299–336. Chau, F., Deesomark, R.,& Koutmos, D., (2016), Does investor sentiment really matter? International Review of Financial Analysis, 48, 221-232. Chen, M. C., Patel, K., (2002), An empirical analysis of determination of house prices in the Taipei area, Taiwan Economic Review, 30(4), 563-595. Dong, Z. D., Dong, Q., Hao, C., (2010), HowNet and its computation of meaning, In Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, Beijing, China. Dougal, C., Engelberg, J., García, D.,& Parsons, C., (2012), Journalists and the Stock Market. The Review of Financial Studies, 25(3), 639-679. Dumas, B., Kurshev, A.,& Uppal, R., (2009), Equilibrium portfolio strategies in the presence of sentiment risk and excess volatility, Journal of Finance, 64, 579-629. Fayyad, U., Piatetsky-Shapiro, G.,& Smyth, P., (1996), From Data Mining to Knowledge Discovery: An Overview, Advances in Knowledge Discovery and Data Mining, 495-515. French, K., Schwert, W.,& Stambaugh, R., (1987), Expected stock returns and volatility, Journal of Financial Economics, 19, 3-29. Gandomi, A., Haider, M., (2015), Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management, 35(2), 137-144. Godbole, N., Srinivasaiah, M., & Skiena, S., (2007), Large-scale sentiment analysis for news and blogs, In Proceedings of the International Conference on Weblogs and Social Media (ICWSM), Boulder, CO, USA Hu, M., Liu, B., (2004), Mining opinion features in customer reviews, In Proceedings of AAAI, 755-760. Kahneman, D., & Tversky, A., (1979), Prospect Theory: An Analysis of Decision Under Risk, Econometrica, 47, 263–291. Keshk, W., Wang, J., (2018), Determinants of the relationship between investor sentiment and analysts’ private information production, Journal of Business Finance & Accounting, 45, 9-10. Keynes, J. M., (1936), The General Theory of Employment, Interest and Money, London: Harcourt Brace Jovanovich Ku, L. W., Chen, H. H., (2007), Mining opinions from the web: beyond relevance retrieval, Mining Web Resources for Enhancing Information Retrieval, 58, 1838-1850. Ku, L. W., Lo, Y. S., & Chen, H. H., (2007), Using polarity scores of words for sentence-level opinion extraction, In Proceedings of NTCIR-6 workshop meeting, Tokyo, Japan. Lai, R. N., & Order, R. V., (2010), Momentum and House Price Growth in the United States: Anatomy of a Bubble, Real Estate Economics, 38(4), 753-773. Liu, B., (2012), Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers Loughran, T., Mcdonald, B., (2011), When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks, The Journal of Finance, 66(1), 35-65. Maynard, D., Funk, A., (2011), Automatic detection of political opinions in tweets, In Proceedings of the 8th international conference on the semantic web, ESWC, 11, 88-99. McQuail, D., (1977), The influence and effects of mass media, Mass Communication and Society, London: Edward Arnold Ltd, 70-94. Merton, R., (1973), A Rational Theory of Option Pricing, Bell Journal of Economics and Management Science, 4(1), 141-183 Nelson, D. B., (1991), Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 59, 347-370. Peng, L., Xiong, W., (2006), Investor attention, overconfidence and category learning, Journal of Financial Economics, 80, 563-602. Rozin, P., Royzman, E. B., (2001), Negativity bias, negativity dominance, and contagion, Personality and Social Psychology Review, 5, 296-320. Saydometov, S., Sabherwala, S., Aroul, R. R., (2018), Sentiment and Housing Returns, Dallas Baptist University, working paper Shiller, R. J., (2000), Irrational Exuberance, Philosophy and Public Policy Quarterly, 20(1), 18-23. Shiller, R. J., (2005), Irrational Exuberance, Princeton: NJ: Princeton University Press. Solomon, D. H., (2012), Selective publicity and stock prices, Journal of Finance, 67, 599-637. Soo, C. K., (2018), Quantifying Sentiment with News Media across Local Housing Markets, The Review of Financial Studies, 31(10), 3689-3719. Sullivan, D. (2001), Document Warehousing and Text Mining: Techniques for Improving Business Operations, Marketing, and Sales, John Wiley & Sons, Inc., New York, NY, USA Tetlock, P. C., (2007), Giving Content to Investor Sentiment: The Role of Media in the Stock Market, The Journal of Finance, 62(3), 1139-1168. Tetlock, P. C., (2008), More Than Words: Quantifying Language to Measure Firms` Fundamentals, The Journal of Finance, 63(3), 1437-1467. Tetlock, P. C., Saar-Tsechansky, M., Macskassy, S., (2008), More Than Words: Quantifying Language to Measure Firms` Fundamentals, The Journal of Finance, 63(3), 1437-1467. Walker, C., (2014), Housing booms and media coverage. Applied Economics, 46(32), 3954-3967. Wu, C. H., Lin C. J., (2017), The impact of media coverage on investor trading behavior and stock returns, Pacific‐Basin Finance Journal, 43, 151-172. Zakoian, J. M., (1994), Threshold heteroskedasticity models. Journal of Economic Dynamics and Control, 15, 931-955.
中文參考文獻 呂旻哲, (2018), 房價供需層面變數與信義房價指數、國泰房地產指數及房價綜合趨勢分數之分析, 中華大學資訊管理學研究所 李慶堂, (2014), Text Mining技術淺談, 國立台灣大學計算機及資訊網路中心電子報,31. 林宜萱, (2013), 財經領域情緒辭典之建置與其有效性之驗證-以財經新聞為元件, 臺灣大學會計學研究所 朱芳妮, 楊茜文, 黃御維, & 陳明吉, (2020), 媒體傳播效應與房市變化關聯性之驗證, 管理學報, forthcoming 彭建文, & 張金鶚, (2000), 總體經濟對房地產景氣影響之研究. 國家科學委員會研究彙刊:人文及社會科學, 10(3), 330-343. 蔡怡純, & 陳明吉, (2013), 房價之不對稱均衡調整:門檻誤差修正模型應用. 臺灣土地研究, 16(1), 37-58. |
Description: | 碩士 國立政治大學 財務管理學系 107357030 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107357030 |
Data Type: | thesis |
DOI: | 10.6814/NCCU202000812 |
Appears in Collections: | [財務管理學系] 學位論文
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