EconPapers    
Economics at your fingertips  
 

The Role of Investor Sentiment in Forecasting Housing Returns in China: A Machine Learning Approach

Oguzhan Cepni (), Rangan Gupta () and Yigit Onay ()
Additional contact information
Oguzhan Cepni: Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara, Turkey
Yigit Onay: Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara, Turkey

No 202055, Working Papers from University of Pretoria, Department of Economics

Abstract: This paper analyzes the predictive ability of aggregate and dis-aggregate proxies of investor sentiment, over and above standard macroeconomic predictors, in forecasting housing returns in China, using an array of machine learning models. Using a monthly out-of-sample period of 2011:01 to 2018:12, given an in-sample of 2006:01-2010:12, we find that indeed the new aligned investor sentiment index proposed in this paper has greater predictive power for housing returns than the a principal component analysis (PCA)-based sentiment index, used earlier in the literature. Moreover, shrinkage models utilizing the dis-aggregate sentiment proxies do not result in forecast improvement indicating that aligned sentiment index optimally exploits information in the dis-aggregate proxies of investor sentiment. Furthermore, when we let the machine learning models to choose from all key control variables and the aligned sentiment index, the forecasting accuracy is improved at all forecasting horizons, rather than just the short-run as witnessed under standard predictive regressions. This result suggests that machine learning methods are flexible enough to capture both structural change and time-varying information in a set of predictors simultaneously to forecast housing returns of China in a precise manner. Given the role of the real estate market in China’s economic growth, our result of accurate forecasting of housing returns, based on investor sentiment and macroeconomic variables using state-of-the-art machine learning methods, has important implications for both investors and policymakers.

Keywords: Housing prices; Investor sentiment; Bayesian shrinkage; Time-varying parameter model (search for similar items in EconPapers)
JEL-codes: C22 C32 C52 G12 R31 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2020-06
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-ore and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://www.up.ac.za/media/shared/61/WP/wp_2020_55.zp190725.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:202055

Access Statistics for this paper

More papers in Working Papers from University of Pretoria, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Rangan Gupta ().

 
Page updated 2020-11-26
Handle: RePEc:pre:wpaper:202055