Leading indicators for US house prices: New evidence and implications for EU financial risk managers
| Autor: | Miguel Rodriguez Gonzalez, Frederik Kunze, Tobias Basse, Danilo Saft |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Finance
business.industry Financial risk leading indicators financial risk management transfer entropy US house prices Financial risk management machine learning Dewey Decimal Classification::300 | Sozialwissenschaften Soziologie Anthropologie::330 | Wirtschaft Economic indicator Accounting ddc:330 Transfer entropy business General Economics Econometrics and Finance |
| Zdroj: | European Financial Management 28 (2022), Nr. 3 European Financial Management |
| ISSN: | 1468-036X 1354-7798 |
| DOI: | 10.1111/eufm.12325 |
| Popis: | This study draws on machine learning as a means to causal inference for econometric investigation. We utilize the concept of transfer entropy to examine the relationship between the US National Association of Home Builders Index and the S&P CoreLogic Case-Shiller 20 City Composite Home Price Index (SPCS20). The empirical evidence implies that the survey data can help to predict US house prices. This finding extends the results of Granger causality tests performed by Rodriguez Gonzalez et al. in 2018 using a new machine learning approach that methodologically differs from traditional methods in empirical financial research. © 2021 The Authors. European Financial Management published by John Wiley & Sons Ltd. |
| Databáze: | OpenAIRE |
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