The "Fed Model" and the Predictability of Stock Returns
Author
Abstract
Suggested Citation
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Faria, Gonçalo & Verona, Fabio, 2020. "The yield curve and the stock market: Mind the long run," Journal of Financial Markets, Elsevier, vol. 50(C).
- Wilton Bernardino & João B. Amaral & Nelson L. Paes & Raydonal Ospina & José L. Távora, 2022. "A statistical investigation of a stock valuation model," SN Business & Economics, Springer, vol. 2(8), pages 1-25, August.
- Maio, Paulo, 2013. "Return decomposition and the Intertemporal CAPM," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4958-4972.
- Lleo, Sebastien & Ziemba, William T., 2014. "Does the bond-stock earning yield differential model predict equity market corrections better than high P/E models?," LSE Research Online Documents on Economics 59290, London School of Economics and Political Science, LSE Library.
- McMillan, David G., 2019. "Predicting firm level stock returns: Implications for asset pricing and economic links," The British Accounting Review, Elsevier, vol. 51(4), pages 333-351.
- Dladla, Pholile & Malikane, Christopher, 2019. "Stock return predictability: Evidence from a structural model," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 412-424.
- Maio, Paulo & Xu, Danielle, 2020. "Cash-flow or return predictability at long horizons? The case of earnings yield," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 172-192.
- Maio, Paulo & Philip, Dennis, 2015. "Macro variables and the components of stock returns," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 287-308.
- Kadilli, Anjeza, 2015. "Predictability of stock returns of financial companies and the role of investor sentiment: A multi-country analysis," Journal of Financial Stability, Elsevier, vol. 21(C), pages 26-45.
- David G. McMillan, 2018. "The Behaviour of the Equity Yield and Its Relation with the Bond Yield: The Role of Inflation," IJFS, MDPI, vol. 6(4), pages 1-18, December.
- Andreas Humpe & David G. McMillan, 2018. "Equity/bond yield correlation and the FED model: evidence of switching behaviour from the G7 markets," Journal of Asset Management, Palgrave Macmillan, vol. 19(6), pages 413-428, October.
- Lleo, Sébastien & Ziemba, William T., 2015.
"Some historical perspectives on the Bond-Stock Earnings Yield Model for crash prediction around the world,"
International Journal of Forecasting, Elsevier, vol. 31(2), pages 399-425.
- Lleo, Sebastien & Ziemba, Bill, 2014. "Some historical perspectives on the Bond-Stock Earnings Yield Model for crash prediction around the world," LSE Research Online Documents on Economics 60960, London School of Economics and Political Science, LSE Library.
- David G. McMillan, 2021. "Forecasting sector stock market returns," Journal of Asset Management, Palgrave Macmillan, vol. 22(4), pages 291-300, July.
- Chronopoulos, Dimitris K. & Papadimitriou, Fotios I. & Vlastakis, Nikolaos, 2018. "Information demand and stock return predictability," Journal of International Money and Finance, Elsevier, vol. 80(C), pages 59-74.
- repec:zbw:bofrdp:2018_007 is not listed on IDEAS
- Gozluklu, Arie & Morin, Annaïg, 2019. "Stock vs. Bond yields and demographic fluctuations," Journal of Banking & Finance, Elsevier, vol. 109(C).
- Laborda, Ricardo & Laborda, Juan, 2017. "Can tree-structured classifiers add value to the investor?," Finance Research Letters, Elsevier, vol. 22(C), pages 211-226.
- Maio, Paulo, 2016. "Cross-sectional return dispersion and the equity premium," Journal of Financial Markets, Elsevier, vol. 29(C), pages 87-109.
- Faria, Gonçalo & Verona, Fabio, 2018. "The equity risk premium and the low frequency of the term spread," Research Discussion Papers 7/2018, Bank of Finland.
- Zakamulin, Valeriy & Hunnes, John A., 2021. "Stock earnings and bond yields in the US 1871–2017: The story of a changing relationship," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 182-197.
- Ji, Hongyun & Zhang, Han, 2024. "Application of the LPPL model in the identification and measurement of structural bubbles in the Chinese stock market," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
- McMillan, David G., 2019. "Stock return predictability: Using the cyclical component of the price ratio," Research in International Business and Finance, Elsevier, vol. 48(C), pages 228-242.
- Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:revfin:v:17:y:2013:i:4:p:1489-1533. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/eufaaea.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.