Deep learning for sentiment and event-driven REIT price dynamics

This research aims to figure out how textual information in the real estate news can be applied to predicting the price dynamics of REIT (real estate investment trust), a publicly traded security in the exchange whose income is backed up by real estate. Due to the information gap in the market and the sentiment-induced irrational trading behaviors, the market often witnesses the departure of REIT price from its fundamental NAV (net asset value). Traditional REIT pricing models fail to incorporate these behavioral factors and the real time market information, leading to a gap in current empirical studies. With the development of deep learning and natural language processing (NLP) techniques, we are curious about how to properly represent and extract textual information in the real estate news, in a way that allows us to capture the up-to-date market events and irrational sentiment, and incorporate them in REIT pricing. To achieve this goal, I conduct a two-stage analysis.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Urban Studies and Planning

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Zhao, Yao,M.C.P.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• David Geltner.

• Urban Studies and Planning.
• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/129870

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/129870