House price and the stock market prices dynamics: evidence from China using a wavelet approach

Hong, Yun; Li, Yang

doi:10.1080/13504851.2019.1649359

Cited by 10 publications

(8 citation statements)

References 12 publications

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“…The data that support the findings of this study are available from the study of Hong and Li (2019a) which can be downloaded https://data.mendeley.com/datasets/4b9vdxwdyb/3.…”

Section: Data Availability Statementsupporting

confidence: 57%

“…The data that support the findings of this study are available from the study of Hong and Li (2019a) which can be downloaded https://data.mendeley.com/datasets/4b9vdxwdyb/3. ORCID Oguzhan Cepni https://orcid.org/0000-0003-0711-8880 Rangan Gupta https://orcid.org/0000-0001-5002-3428…”

Section: Acknowledgmentssupporting

confidence: 56%

“…For instance, the suspension of welfare housing provision in 1998 as a response to the 1997 Asian financial crisis (Logan et al, 2010), and more recently, the housing boom triggered by the injection of 4 trillion yuan into infrastructure and urban development after the 2008 global financial crisis (Deng & Chen, 2019). 1 Put alternatively, financialization of the housing market in China (and around the world) basically implies that price comovement between real and financial assets, that is, the equity market (as well as between different assets), is likely to increase (Hong & Li, 2019a), with the underlying driver being investor sentiment (Tang & Xiong, 2012). Moreover, while documenting the relatively large amount of existing evidence of investor sentiment in affecting Chinese stock returns, Su et al (2020) point out that, because individual investors account for a large proportion of Chinese stock investors, they not only tend to make irrational trades in the stock market but are also more likely to carry their emotions to other markets.…”

Section: Introductionmentioning

confidence: 99%

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

Çepni

Gupta

Onay

2022

Journal of Forecasting

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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.

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“…The data that support the findings of this study are available from the study of Hong and Li (2019a) which can be downloaded https://data.mendeley.com/datasets/4b9vdxwdyb/3.…”

Section: Data Availability Statementsupporting

confidence: 57%

Section: Acknowledgmentssupporting

confidence: 56%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Çepni

Gupta

Onay

2022

Journal of Forecasting

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“…Our approach rests on the large impact of house prices on the business cycle (Leamer, 2015) and on waves of optimism and pessimism in the economy (Chahrour & Gaballo, 2021). Such a significant impact of housing on the economy can affect stock prices (Hong & Li, 2020;Bahmani-Oskooee & Ghodsi, 2018;Ali & Zaman, 2017). Indeed, the links between different macroeconomic variables may become stronger during booming house prices (Ryczkowski, 2019;Goodhart & Hofmann, 2008).…”

Section: The Mechanismmentioning

confidence: 99%

The Interconnectedness of Stock Prices, Money, and Credit Across Time and Frequency From 1970 to 2016

Ryczkowski

Zinecker

2022

Technological and Economic Development of Economy

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This article applies continuous wavelet and maximal overlap discrete wavelet transforms to study the co-movements and the direction of causality between money (M3 and M2), bank credit, credit from all sectors vs. stock price dynamics across time and frequency for 12 developed countries. The time-varying linkages were significant typically for credit rather than money developments. Particularly, these linkages appeared during phases of house and stock price boom and bust at a business cycle frequency. The discovered lead-lag patterns give relatively much time for monetary policy authorities to support their financial stability objectives by imposing restrictions on credit. Closing the channel through which credit and stock prices interact may counteract the rising financial imbalances. This finding holds also for the euro area where the monetary pillar is based on broad money. The new application of wavelets allowed discovering the varying linkages that were not necessarily evident in the standard methods of analysing data.

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“…According to researches by Li and Chiang (2012); Owusu-Ansah et al (2020); Hanck and Prüser (2020) housing prices are also affected by economic and institutional factors such as inflation, GDP growth and land price regulation. Land tender, auction and price listing factors also have impact on housing prices (Hong and Li, 2020; Tsai, 2019; Wang et al , 2018). According to research studies by Rahadi et al (2013), Kaklauskas et al (2015), the factors that clearly affect housing prices include facilities, reputation of developers, price policy, housing speculation.…”

Section: Introductionmentioning

confidence: 99%

Factors affecting commercial housing prices in Vietnam

Nam¹,

Phuong²

2021

IJHMA

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Purpose The study aims to identify the affecting factors and their impact rates on the commercial housing prices. The study also aims to suggest implications related to commercial housing prices to develop the commercial housing market. Design/methodology/approach The study investigates housing investors, real estate agents and buyers to identify factors that might affect commercial housing prices. The proposed research model has 7 latent factors and is tested by Cronbach' alpha and exploratory factor analysis by SPSS20.0 software. Findings There are 7 groups with 24 factors affecting commercial housing prices. The neighboring factor group has the greatest impact rate (18.54%); the housing service group has the lowest impact rate (11.48%). Research limitations/implications The study has only determined the affecting factors and their impact rates on commercial housing prices in Bac Ninh city. Therefore, it is necessary to conduct research on factors affecting commercial housing prices in other provinces and cities of Vietnam in the coming time. In addition, the proposed research method can also be consulted when it is necessary to determine the factors affecting commercial housing prices in other countries around the world. Practical implications The study proposes some implications related to commercial housing prices such as commercial housing valuation; housing selection with suitable prices for people intending to buy houses; state support policies for commercial housing investors to develop commercial housing with reasonable prices. Social implications The implementing the implications proposed in the study will facilitate people's easier access to commercial housing; real estate investors do business more efficiently. Originality/value To the best of the authors’ knowledge, this paper presents for the first time a method to determine the affecting factors and their impact rates on commercial housing prices in Vietnam. The paper also points out a number of specific factors affecting commercial housing prices that are different from those shown in previous studies.

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House price and the stock market prices dynamics: evidence from China using a wavelet approach

Cited by 10 publications

References 12 publications

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

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

The Interconnectedness of Stock Prices, Money, and Credit Across Time and Frequency From 1970 to 2016

Factors affecting commercial housing prices in Vietnam

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