The role of investor sentiment in forecasting housing returns in China : a machine learning approach

Show simple item record Cepni, Oguzhan Gupta, Rangan Onay, Yigit 2022-11-01T08:01:21Z 2022-11-01T08:01:21Z 2022-07-11
dc.description.abstract This paper analyzes the predictive ability of aggregate and disaggregate proxies of investor sentiment, over and above standard macroeconomic predictors, in forecasting housing returns in China, using an array of machine learning models. We find that our new aligned investor sentiment index has greater predictive power for housing returns than the principal component analysis (PCA)-based sentiment index, used earlier in the literature. Moreover, shrinkage models utilizing the disaggregate sentiment proxies do not result in forecast improvement indicating that aligned sentiment index optimally exploits information in the disaggregate 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 has important implications for both investors and policymakers. en_US
dc.description.department Economics en_US
dc.description.uri en_US
dc.identifier.citation Cepni, O., Gupta, R., &Onay, Y. (2022). The role of investor sentiment inforecasting housing returns in China: A machinelearning approach. Journal of Forecasting, 41(8), 1725–1740. en_US
dc.identifier.issn 0277-6693 (print)
dc.identifier.issn 1099-131X (online)
dc.identifier.other 10.1002/for.2893
dc.language.iso en en_US
dc.publisher Wiley en_US
dc.rights © 2022 The Authors. Journal of Forecasting published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License. en_US
dc.subject Bayesian shrinkage en_US
dc.subject Housing prices en_US
dc.subject Investor sentiment en_US
dc.subject Time-varying parameter model en_US
dc.subject Principal component analysis (PCA) en_US
dc.title The role of investor sentiment in forecasting housing returns in China : a machine learning approach en_US
dc.type Article en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record