115
Views
1
CrossRef citations to date
0
Altmetric
Research Articles

Sentiment Analysis Within a Deep Learning Probabilistic Framework – New Evidence from Residential Real Estate in the United States

Pages 25-49 | Received 01 Feb 2022, Accepted 02 May 2023, Published online: 17 May 2023

References

  • Alfano, V., & Guarino, M. (2022). A word to the wise analyzing the impact of textual strategies in determining house pricing. Journal of Housing Research, 31 (1), 88–112. https://doi.org/10.1080/10527001.2021.2013058
  • Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59 (3), 1259–1294. https://doi.org/10.1111/j.1540-6261.2004.00662.x
  • Beracha, E., Lang, M., & Hausler, J. (2019). On the relationship between market sentiment and commercial real estate performance—A textual analysis examination. Journal of Real Estate Research, 41 (4), 605–637. https://doi.org/10.22300/0896-5803.41.4.605
  • Bork, L., Møller, S. V., & Pedersen, T. Q. (2020). A new index of housing sentiment. Management Science, 66 (4), 1563–1583. https://doi.org/10.1287/mnsc.2018.3258
  • Borovkova, S., & Dijkstra, M. (2018). Deep learning prediction of the EUROSTOXX 50 with news sentiment. SSRN, 3253043, https://doi.org/10.2139/ssrn.3253043
  • Braun, J., Hausler, J., & Schäfers, W. (2020). Artificial intelligence, news sentiment, and property market liquidity. Journal of Property Investment & Finance, 38(4), 309–325. https://doi.org/10.1108/JPIF-08-2019-0100
  • Braun, N. (2016). Google search volume sentiment and its impact on REIT market movements. Journal of Property Investment & Finance, 34 (3), 249–262. https://doi.org/10.1108/JPIF-12-2015-0083
  • Buduma, N., & Lacascio, N. (2017). Fundamentals of deep learning (1st ed.). O’Reilly.
  • Case, K. E., & Shiller, R. J. (1990). Forecasting prices and excess returns in the housing market. Real Estate Economics, 18 (3), 253–273. https://doi.org/10.1111/1540-6229.00521
  • Chen, H., De, P., Hwang, B.-H. (2013). Customers as advisors: The role of social media in financial markets. Working Paper.
  • Clayton, J., Ling, D. C., & Naranjo, A. (2009). Commercial real estate valuation: fundamentals versus investor sentiment. The Journal of Real Estate Finance and Economics, 38 (1), 5–37. https://doi.org/10.1007/s11146-008-9130-6
  • Cutler, D. M., Poterba, J. M., & Summers, L. H. (1988). What Moves Stock Prices?. National Bureau of Economic Research.
  • Das, S. R., & Chen, M. Y. (2007). Yahoo! for Amazon: Sentiment extraction from small talk on the web. Management Science, 53 (9), 1375–1388. https://doi.org/10.1287/mnsc.1070.0704
  • Dietzel, M. A. (2016). Sentiment-based predictions of housing market turning points with Google trends. International Journal of Housing Markets and Analysis, 9(1), 108–136. https://doi.org/10.1108/IJHMA-12-2014-0058
  • Fahrmeir, L., Kneib, T., Lang, S., & Marx, B. (2013). Regression models. In L. Fahrmeir, T. Kneib, S. Lang, and B. Marx (Eds.), Regression: Models, methods and applications (pp. 21–72). Springer.
  • Ferguson, N. J., Philip, D., Lam, H. Y. T., & Guo, J. M. (2015). Media content and stock returns: The predictive power of press. Multinational Finance Journal, 19 (1), 1–31. https://doi.org/10.17578/19-1-1
  • Freybote, J., & Seagraves, P. (2017). Heterogeneous investor sentiment and institutional real estate investments. Real Estate Economics, 45 (1), 154–176. https://doi.org/10.1111/1540-6229.12132
  • Freybote, J., & Seagraves, P. (2018). The impact of investor sentiment on commercial real estate market liquidity. Journal of Real Estate Research, 40 (4), 597–627. https://doi.org/10.1080/10835547.2018.12091513
  • Goodwin, K., Waller, B., & Weeks, H. S. (2014). The impact of broker vernacular in residential real estate. Journal of Housing Research, 23 (2), 143–161. https://doi.org/10.1080/10835547.2014.12092089
  • Goodwin, K. R. (2019). Measures of real estate market sentiment and their relationship with U.S. home prices. Journal of Housing Research, 28 (2), 208–214. https://doi.org/10.1080/10527001.2020.1776514
  • Goodwin, K. R., Waller, B. D., & Weeks, H. S. (2018). Connotation and textual analysis in real estate listings. Journal of Housing Research, 27 (2), 93–106. https://doi.org/10.1080/10835547.2018.12092149
  • Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33 (5), 2223–2273. https://doi.org/10.1093/rfs/hhaa009
  • Hájek, P., Olej, V., & Myšková, R. (2013). Forecasting stock prices using sentiment information in annual reports – A neural network and support vector regression approach. WSEAS Transactions on Business and Economics, 10 (4), 293–305.
  • Hausler, J., Ruscheinsky, J., & Lang, M. (2018). News-based sentiment analysis in real estate: A machine learning approach. Journal of Property Research, 35 (4), 344–371. https://doi.org/10.1080/09599916.2018.1551923
  • Heinig, S., & Nanda, A. (2018). Measuring sentiment in seal estate – A comparison study. Journal of Property Investment & Finance, 36 (3), 248–258. https://doi.org/10.1108/JPIF-05-2017-0034
  • Hohenstatt, R., Kaesbauer, M., & Schäfers, W. (2011). “Geco” and its potential for real estate research: Evidence from the U.S. housing market. Journal of Real Estate Research, 33 (4), 471–506. https://doi.org/10.1080/10835547.2011.12091318
  • Huang, A. H., Zang, A. Y., & Zheng, R. (2014). Evidence on the information content of text in analyst reports. The Accounting Review, 89 (6), 2151–2180. https://doi.org/10.2308/accr-50833
  • Jegadeesh, N., & Wu, D. (2013). Word power: A new approach for content analysis. Journal of Financial Economics, 110 (3), 712–729. https://doi.org/10.1016/j.jfineco.2013.08.018
  • Kearney, C., & Liu, S. (2014). Textual sentiment in finance: A survey of methods and models. International Review of Financial Analysis, 33, 171–185. https://doi.org/10.1016/j.irfa.2014.02.006
  • Koelbl, M. (2020). Is the MD&A of US REITs informative? A textual sentiment study. Journal of Property Investment & Finance, 38 (3), 181–201. https://doi.org/10.1108/JPIF-12-2019-0149
  • Li, F. (2010). The information content of forward-looking statements in corporate filings-A Naïve Bayesian machine learning approach: The information content of corporate filings. Journal of Accounting Research, 48 (5), 1049–1102. https://doi.org/10.1111/j.1475-679X.2010.00382.x
  • Liu, B., & McConnell, J. J. (2013). The role of the media in corporate governance: Do the media influence managers’ capital allocation decisions? Journal of Financial Economics, 110 (1), 1–17. https://doi.org/10.1016/j.jfineco.2013.06.003
  • Liu, Y., Qin, Z., Li, P., Wan, T., (2017 Stock volatility prediction using recurrent neural networks with sentiment analysis [Paper presentation]. International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems .
  • 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. https://doi.org/10.1111/j.1540-6261.2010.01625.x
  • Marcato, G., & Nanda, A. (2016). Information content and forecasting ability of sentiment indicators: Case of real estate market. Journal of Real Estate Research, 38 (2), 165–203. https://doi.org/10.1080/10835547.2016.12091442
  • Ramadhan, W. P., Astri Novianty, S., Casi Setianingsih, S., (2017 Sentiment analysis using multinomial logistic regression [Paper presentation]. ICCEREC: The 2017 International Conference on Control, Electronics, Renewable Energy and Communications Proceedings, in September 26–28, 2017, Tentrem Hotel, Yogyakarta-Indonesia. Piscataway, NJ: IEEE.
  • Ramos, J. (2003). Using TF-IDF to determine word relevance in document queries . In Proceedings of the First Instructional Conference on Machine Learning ( Vol. 242, pp. 133 – 142 ) .
  • Rochdi, K., & Dietzel, M. (2015). Outperforming the benchmark: Online information demand and REIT market performance. Journal of Property Investment & Finance, 33 (2), 169–195. https://doi.org/10.1108/JPIF-11-2014-0069
  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323 (6088), 533–536. https://doi.org/10.1038/323533a0
  • Ruscheinsky, J., Lang, M., & Schäfers, W. (2018). Real estate media sentiment through textual analysis. Journal of Property Investment & Finance, 36 (5), 410–428. https://doi.org/10.1108/JPIF-07-2017-0050
  • Seo, Y., Im, J., & Mikelbank, B. (2020). Does the written word matter? The role of uncovering and utilizing information from written comments in housing ads. Journal of Housing Research, 29 (2), 133–155. https://doi.org/10.1080/10527001.2020.1849929
  • Shiller, R. J. (2000). Irrational exuberance. Princeton University Press.
  • Sinha, N. R. (2015). Underreaction to news in the US stock market. Quarterly Journal of Finance, 6 (02), 1650005. https://doi.org/10.1142/S2010139216500051
  • Soo, C. K. (2018). Quantifying sentiment with news media across local housing markets. The Review of Financial Studies, 31 (10), 3689–3719. https://doi.org/10.1093/rfs/hhy036
  • Souma, W., Vodenska, I., & Aoyama, H. (2019). Enhanced news sentiment analysis using deep learning methods. Journal of Computational Social Science, 2 (1), 33–46. https://doi.org/10.1007/s42001-019-00035-x
  • 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. https://doi.org/10.1111/j.1540-6261.2007.01232.x
  • 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. https://doi.org/10.1111/j.1540-6261.2008.01362.x
  • Walker, C. B. (2014). Housing booms and media coverage. Applied Economics, 46 (32), 3954–3967. https://doi.org/10.1080/00036846.2014.948675
  • Young, L., & Soroka, S. (2012). Affective news: The automated coding of sentiment in political texts. Political Communication, 29(2), 205–231. https://doi.org/10.1080/10584609.2012.671234

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.