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題名 應用大數據分析商用不動產市場、股票市場情緒與收益之交叉關聯
Big Data Analytics: How the Commercial Real Estate and Stock Market’s Sentiment Affects the Commercial Real Estate Investment
作者 黃瀞誼
Huang, Ching-Yi
貢獻者 林左裕
黃瀞誼
Huang, Ching-Yi
關鍵詞 網路聲量情緒
社群媒體
新聞媒體
商用不動產市場
向量自我迴歸
Online Sentiment
Social media
News Media
Commercial Real Estate Market
Vector Auto Regression (VAR)
日期 2021
上傳時間 2-Sep-2021 17:34:03 (UTC+8)
摘要 本研究以網路討論熱度作為市場需求的參考，主要探討商用不動產市場、股票市場之網路聲量情緒對各市場收益之影響，以向量自我迴歸模型（VAR）分析市場情緒與價格之動態關聯。另考慮到商用不動產市場與股票市場情緒變數蒐集之原始聲量來源眾多，蒐集網站如Facebook、Instagram、PTT、Mobile 01、Dcard、ETtoday、Linetoday…等，惟各網站之屬性不同，像是Facebook、Instagram、PTT、Mobile 01與Dcard多半是自發性討論或針對特定議題進行回覆與留言，為較開放且隨機之言論；ETtoday與Linetoday則是電子新聞報導，其中可能夾雜廣告、宣傳或教育之性質，故本研究將網路聲量進行分類，將來源細分為多半是自發性發言之「網路社群」與具有宣傳、廣告性質之「新聞媒體」等二來源集，分別建構情緒指標，嘗試以兩種不同性質的聲量屬性角度探討商用不動產市場與股票市場情緒與價格之關聯性。
實證結果發現，前兩個月至前四個月的商用不動產市場「網路社群」聲量情緒將正向影響當期的商用不動產市場收益；而前兩個月與前三個月的商用不動產市場「新聞媒體」聲量情緒負向影響當期的商用不動產市場收益，顯示網路社群與新聞媒體之討論內容與熱度確實對於商用不動產市場具解釋效果。此外，本研究亦發現前兩個月的股票產市場「新聞媒體」聲量情緒將正向影響當期的商用不動產市場收益，換句話說，股票市場之聲量情緒可以用於預測未來商用不動產市場之發展趨勢，證實股票市場與商用不動產市場間存在情緒外溢效應。透過網路社群或新聞媒體之討論情緒不僅可補足過去單靠總體經濟變數所無法解釋之市場意向，亦提供商用不動產市場一新穎的預測指標。本研究之實證結果可提供政府、投資者或不動產相關從業人員在觀察市場、進行投資決策或政策制定之參考依據。
This research uses the popularity of internet discussions as a reference for market demand, and explores the impact of online sentiment on the price of the commercial real estate market and the stock market, as well as analyzes the dynamic relationship between the sentiment of the market and the revenue of the market with the Vector Auto Regression model. In addition, this research divides the online sentiment into two source sets: " social media" that involve spontaneous discussions and "news media" that potentially involve propaganda and advertising.
The empirical results show that the sentiment of the " social media " in the commercial real estate market during last two months to last four months positively affects the current commercial real estate market revenue; while the sentiment of the "news media" in the real estate market during last two months and last three months negatively affects the current commercial real estate market revenue, which also indicates that the content and the popularity of discussions between the internet and the news media do have an explanatory effect on the commercial real estate market. Moreover, this study shows that online sentiment of the "news media" in the stock market during last two months positively affects the current commercial real estate market earnings. In other words, the development trend of the market confirms that there is a spillover effect between the stock market and the commercial real estate market.
參考文獻 一、中文參考文獻
王信達，2010，「從兩岸總體經濟環境探討臺北市上海市辦公市場租金影
響之實證分析」，淡江大學中國大陸研究所碩士論文。
林左裕，2019，「應用網路搜尋行為預測房地產市場」，應用經濟論叢，
第105期。
林左裕、程于芳，2014，「影響不動產市場之從眾行為與總體經濟因素之
研究」，應用經濟論叢，第95期。
張曉慈，2010，「影響不動產報酬波動性之總體經濟因素分析」，國立政
治大學地政研究所碩士論文。
楊奕農，2009，「時間序列分析 經濟與財務上之應用」。頁 99-123、
205-235、331-392、395-446，台北，雙葉書廊有限公司。
鄧筱蓉，2017，房市泡沫與總體經濟關係，JOURNAL OF HOUSING:26(2).
薄有為、鍾懿萍，2011，「辦公大樓租金影響因素之研究-以上海市甲級辦
公大樓為例」，『物業管理學會論文集』, 13-22.
 
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sentiment and market behavior”, Empirical Economics, 57(1):105-127.
Agyemang, A., Chowdhury, I., and Balli, F., 2021,“Quantifying Return Spillovers in Global Real Estate
Markets”, Journal of Housing Economics, 101781.
Akinsomi, O., Mkhabela, N., and Taderera, M., 2018, “The
role of macro-economic indicators in explaining direct commercial real estate returns: evidence from South Africa”, Journal of Property Research, 35(1):28-52.
Baker, M., and Wurgler, J., 2006, “Investor sentiment and the cross‐section of stock returns”, The journal of Finance, 61(4):1645-1680.
Baker, M., Wurgler, J., and Yuan, Y., 2012, “Global, local, and contagious investor sentiment”, Journal of financial economics, 104(2):272-287.
Beauchamp, N.,2017, “Predicting and interpolating state‐level polls using Twitter textual data”, American Journal of Political Science, 61(2):490-503.
Bhuriya, D., Kaushal, G., Sharma, A., and Singh, U., 2017, “Stock market predication using a linear regression”, In 2017 international conference of electronics, communication and aerospace technology (ICECA), (Vol. 2, pp. 510-513). IEEE.
Bhuiyan, E. M., and Chowdhury, M., 2020, “Macroeconomic variables and stock market indices: Asymmetric dynamics in the US and Canada”, The Quarterly Review of Economics and Finance, 77:62-74.
Carosia, A. E. O., Coelho, G. P., and Silva, A. E. A., 2020, “Analyzing the Brazilian financial market through Portuguese sentiment analysis in social media”, Applied Artificial Intelligence, 34(1):1-19.
Checkley, M. S., Higón, D. A., and Alles, H., 2017, “The hasty wisdom of the mob: How market sentiment predicts stock market behavior”, Expert Systems with applications, 77:256-263.
Chen, M. C., and K. Patel , 2002 , "The Empirical Analysis of Determination of Housing Prices in the Taipei Area", Taiwan Economic Review, 30(4):563-594.
De Long, J. B., Shleifer, A., Summers, L. H., and Waldmann, R. J.,1990,” Noise trader risk in financial markets”, Journal of political Economy, 98(4): 703-738.
Devos, E., Ong, S. E., Spieler, A. C., and Tsang, D., 2013,” REIT institutional ownership dynamics and the financial crisis”, The journal of real estate finance and economics, 47(2): 266-288.
Dietzel, M. A., 2016, “Sentiment-based predictions of housing market turning points with Google trends”, International Journal of Housing Markets and Analysis.
Freybote, J., and Seagraves, P.A., 2017, “Heterogeneous investor sentiment and institutional real estate investments”, Real Estate Economics, 45(1):154-176.
Freybote, J., and Seagraves, P., 2018, “The impact of investor sentiment on commercial real estate market liquidity”, Journal of Real Estate Research, 40(4):597-628.
Filis, G.,2010, ”Macro economy, stock market and oil prices: do meaningful relationships exist among their cyclical fluctuations? ” Energy Economics, 32(4): 877-886.
Gallimore, P., and Gray, A., 2002, “The role of investor sentiment in property investment decisions”, Journal of Property Research, 19(2):111-120.
Giordano, G., and Primerano, I., 2018, “The use of network analysis to handle semantic differential data”, Quality & Quantity, 52(3):1173-1192.
González-Sánchez, M., and Morales de Vega, M. E., 2021, “Influence of Bloomberg’s Investor Sentiment Index: Evidence from European Union Financial Sector. Mathematics”,9(4):297.
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Hausler, J., Ruscheinsky, J., and Lang, M., 2018, “News-based sentiment analysis in real estate: a machine learning approach”, Journal of Property Research, 35(4):344-371.
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描述 碩士
國立政治大學
地政學系
108257024
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108257024
資料類型 thesis
dc.contributor.advisor 林左裕zh_TW
dc.contributor.author (Authors) 黃瀞誼zh_TW
dc.contributor.author (Authors) Huang, Ching-Yien_US
dc.creator (作者) 黃瀞誼zh_TW
dc.creator (作者) Huang, Ching-Yien_US
dc.date (日期) 2021en_US
dc.date.accessioned 2-Sep-2021 17:34:03 (UTC+8)-
dc.date.available 2-Sep-2021 17:34:03 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2021 17:34:03 (UTC+8)-
dc.identifier (Other Identifiers) G0108257024en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/137042-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 地政學系zh_TW
dc.description (描述) 108257024zh_TW
dc.description.abstract (摘要) 本研究以網路討論熱度作為市場需求的參考，主要探討商用不動產市場、股票市場之網路聲量情緒對各市場收益之影響，以向量自我迴歸模型（VAR）分析市場情緒與價格之動態關聯。另考慮到商用不動產市場與股票市場情緒變數蒐集之原始聲量來源眾多，蒐集網站如Facebook、Instagram、PTT、Mobile 01、Dcard、ETtoday、Linetoday…等，惟各網站之屬性不同，像是Facebook、Instagram、PTT、Mobile 01與Dcard多半是自發性討論或針對特定議題進行回覆與留言，為較開放且隨機之言論；ETtoday與Linetoday則是電子新聞報導，其中可能夾雜廣告、宣傳或教育之性質，故本研究將網路聲量進行分類，將來源細分為多半是自發性發言之「網路社群」與具有宣傳、廣告性質之「新聞媒體」等二來源集，分別建構情緒指標，嘗試以兩種不同性質的聲量屬性角度探討商用不動產市場與股票市場情緒與價格之關聯性。
實證結果發現，前兩個月至前四個月的商用不動產市場「網路社群」聲量情緒將正向影響當期的商用不動產市場收益；而前兩個月與前三個月的商用不動產市場「新聞媒體」聲量情緒負向影響當期的商用不動產市場收益，顯示網路社群與新聞媒體之討論內容與熱度確實對於商用不動產市場具解釋效果。此外，本研究亦發現前兩個月的股票產市場「新聞媒體」聲量情緒將正向影響當期的商用不動產市場收益，換句話說，股票市場之聲量情緒可以用於預測未來商用不動產市場之發展趨勢，證實股票市場與商用不動產市場間存在情緒外溢效應。透過網路社群或新聞媒體之討論情緒不僅可補足過去單靠總體經濟變數所無法解釋之市場意向，亦提供商用不動產市場一新穎的預測指標。本研究之實證結果可提供政府、投資者或不動產相關從業人員在觀察市場、進行投資決策或政策制定之參考依據。		zh_TW
dc.description.abstract (摘要) This research uses the popularity of internet discussions as a reference for market demand, and explores the impact of online sentiment on the price of the commercial real estate market and the stock market, as well as analyzes the dynamic relationship between the sentiment of the market and the revenue of the market with the Vector Auto Regression model. In addition, this research divides the online sentiment into two source sets: " social media" that involve spontaneous discussions and "news media" that potentially involve propaganda and advertising.
The empirical results show that the sentiment of the " social media " in the commercial real estate market during last two months to last four months positively affects the current commercial real estate market revenue; while the sentiment of the "news media" in the real estate market during last two months and last three months negatively affects the current commercial real estate market revenue, which also indicates that the content and the popularity of discussions between the internet and the news media do have an explanatory effect on the commercial real estate market. Moreover, this study shows that online sentiment of the "news media" in the stock market during last two months positively affects the current commercial real estate market earnings. In other words, the development trend of the market confirms that there is a spillover effect between the stock market and the commercial real estate market.		en_US
dc.description.tableofcontents 第一章　緒論 1
第一節　研究動機與目的 1
第二節 研究方法與範圍 4
第三節 研究架構 7
第二章 文獻回顧 9
第一節 網路聲量與大數據分析 9
第二節 投資者情緒與市場收益 12
第三節 情緒溢出效應 15
第四節 影響不動產市場之總體經濟因素 17
第五節 小結 19
第三章 研究設計 21
第一節 研究設計與模型建立 21
第二節 資料說明與變數選取 27
第四章 實證分析 41
第一節 結構性轉變 41
第二節 單根檢定 42
第三節 皮爾森相關係數檢定 44
第四節 向量自我迴歸模型 45
第五章 結論與建議 53
第一節 結論 53
第二節 建議 55
參考文獻 57

表 1 情緒指數關鍵字設定表 32
表 2 聲量來源分類表 32
表 3 變數統整表 35
表 4 變數敘述統計表 36
表 5 各變數單根檢定結果 43
表 6 皮爾森相關係數檢定結果 44
表 7 模型一之最適落後期數篩選 46
表 8 模型二之最適落後期數篩選 46
表 9 商用不動產市場情緒之向量自我迴歸模型結果 51
表 10 股票市場情緒之向量自我迴歸模型結果 52

圖 1 研究流程圖 7
圖 2 Y_A時間趨勢圖 37
圖 3 X_COMN時間趨勢圖 37
圖 4 X_COMS時間趨勢圖 37
圖 5 X_STOCKN時間趨勢圖 38
圖 6 X_STOCKS時間趨勢圖 38
圖 7 NTD時間趨勢圖 38
圖 8 I時間趨勢圖 39
圖 9 M2時間趨勢圖 39
圖 10 CPI時間趨 39
圖 11 Y_A結構性轉變 41		zh_TW
dc.format.extent 2233987 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108257024en_US
dc.subject (關鍵詞) 網路聲量情緒zh_TW
dc.subject (關鍵詞) 社群媒體zh_TW
dc.subject (關鍵詞) 新聞媒體zh_TW
dc.subject (關鍵詞) 商用不動產市場zh_TW
dc.subject (關鍵詞) 向量自我迴歸zh_TW
dc.subject (關鍵詞) Online Sentimenten_US
dc.subject (關鍵詞) Social mediaen_US
dc.subject (關鍵詞) News Mediaen_US
dc.subject (關鍵詞) Commercial Real Estate Marketen_US
dc.subject (關鍵詞) Vector Auto Regression (VAR)en_US
dc.title (題名) 應用大數據分析商用不動產市場、股票市場情緒與收益之交叉關聯zh_TW
dc.title (題名) Big Data Analytics: How the Commercial Real Estate and Stock Market’s Sentiment Affects the Commercial Real Estate Investmenten_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 一、中文參考文獻
王信達，2010，「從兩岸總體經濟環境探討臺北市上海市辦公市場租金影
響之實證分析」，淡江大學中國大陸研究所碩士論文。
林左裕，2019，「應用網路搜尋行為預測房地產市場」，應用經濟論叢，
第105期。
林左裕、程于芳，2014，「影響不動產市場之從眾行為與總體經濟因素之
研究」，應用經濟論叢，第95期。
張曉慈，2010，「影響不動產報酬波動性之總體經濟因素分析」，國立政
治大學地政研究所碩士論文。
楊奕農，2009，「時間序列分析 經濟與財務上之應用」。頁 99-123、
205-235、331-392、395-446，台北，雙葉書廊有限公司。
鄧筱蓉，2017，房市泡沫與總體經濟關係，JOURNAL OF HOUSING:26(2).
薄有為、鍾懿萍，2011，「辦公大樓租金影響因素之研究-以上海市甲級辦
公大樓為例」，『物業管理學會論文集』, 13-22.
 
二、英文參考文獻
Affuso, E., and Lahtinen, K. D., 2019, “Social media
sentiment and market behavior”, Empirical Economics, 57(1):105-127.
Agyemang, A., Chowdhury, I., and Balli, F., 2021,“Quantifying Return Spillovers in Global Real Estate
Markets”, Journal of Housing Economics, 101781.
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dc.identifier.doi (DOI) 10.6814/NCCU202101478en_US