IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v17y2010i9p869-874.html
   My bibliography  Save this article

Predicting stock price movements: regressions versus economists

Author

Listed:
  • Paul Soderlind

Abstract

The forecasting performance of the Livingston survey and traditional prediction models of stock prices is analysed. The survey forecasts look similar to those from a 'too large' prediction model: poor out-of-sample performance and too sensitive to recent and irrelevant information.

Suggested Citation

  • Paul Soderlind, 2010. "Predicting stock price movements: regressions versus economists," Applied Economics Letters, Taylor & Francis Journals, vol. 17(9), pages 869-874.
    • Handle: RePEc:taf:apeclt:v:17:y:2010:i:9:p:869-874
    DOI: 10.1080/17446540802584871
    as

    Download full text from publisher

    File URL: http://www.informaworld.com/openurl?genre=article&doi=10.1080/17446540802584871&magic=repec&7C&7C8674ECAB8BB840C6AD35DC6213A474B5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/17446540802584871?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. John Y. Campbell & Samuel B. Thompson, 2005. "Predicting the Equity Premium Out of Sample: Can Anything Beat the Historical Average?," Harvard Institute of Economic Research Working Papers 2084, Harvard - Institute of Economic Research.
    3. De Bondt, Werner F M & Thaler, Richard, 1985. "Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
    4. Jack W. Wilson, 2002. "An Analysis of the S&P 500 Index and Cowles's Extensions: Price Indexes and Stock Returns, 18701999," The Journal of Business, University of Chicago Press, vol. 75(3), pages 505-534, July.
    5. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    6. Diebold, Francis X, 1988. "Serial Correlation and the Combination of Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(1), pages 105-111, January.
    7. Pearce, Douglas K, 1984. "An Empirical Analysis of Expected Stock Price Movements," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 16(3), pages 317-327, August.
    8. Dokko, Yoon & Edelstein, Robert H, 1989. "How Well Do Economists Forecast Stock Market Prices? A Study of the Livingston Surveys," American Economic Review, American Economic Association, vol. 79(4), pages 865-871, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Silvija Vlah Jerić & Mihovil Anđelinović, 2019. "Evaluating Croatian stock index forecasts," Empirical Economics, Springer, vol. 56(4), pages 1325-1339, April.
    2. A. Belenky & L. Egorova, 2016. "Two approaches to modeling the interaction of small and medium price-taking traders with a stock exchange by mathematical programming techniques," Papers 1610.05703, arXiv.org.
    3. Rangvid, Jesper & Schmeling, Maik & Schrimpf, Andreas, 2013. "What do professional forecasters' stock market expectations tell us about herding, information extraction and beauty contests?," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 109-129.
    4. Pierdzioch, Christian & Rülke, Jan-Christoph, 2012. "Forecasting stock prices: Do forecasters herd?," Economics Letters, Elsevier, vol. 116(3), pages 326-329.
    5. Björn Fastrich & Peter Winker, 2014. "Combining Forecasts with Missing Data: Making Use of Portfolio Theory," Computational Economics, Springer;Society for Computational Economics, vol. 44(2), pages 127-152, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Söderlind, Paul, 2009. "The C-CAPM without ex post data," Journal of Macroeconomics, Elsevier, vol. 31(4), pages 721-729, December.
    2. Prat, Georges, 2013. "Equity risk premium and time horizon: What do the U.S. secular data say?," Economic Modelling, Elsevier, vol. 34(C), pages 76-88.
    3. Giot, Pierre & Petitjean, Mikael, 2007. "The information content of the Bond-Equity Yield Ratio: Better than a random walk?," International Journal of Forecasting, Elsevier, vol. 23(2), pages 289-305.
    4. Aretz, Kevin & Bartram, Söhnke M. & Pope, Peter F., 2011. "Asymmetric loss functions and the rationality of expected stock returns," International Journal of Forecasting, Elsevier, vol. 27(2), pages 413-437.
    5. Kenneth R. Szulczyk & Changyong Zhang, 2020. "Switching-regime regression for modeling and predicting a stock market return," Empirical Economics, Springer, vol. 59(5), pages 2385-2403, November.
    6. Feng, Jiabao & Wang, Yudong & Yin, Libo, 2017. "Oil volatility risk and stock market volatility predictability: Evidence from G7 countries," Energy Economics, Elsevier, vol. 68(C), pages 240-254.
    7. Ekaterini Panopoulou & Sotiria Plastira, 2014. "Fama French factors and US stock return predictability," Journal of Asset Management, Palgrave Macmillan, vol. 15(2), pages 110-128, April.
    8. Marmer, Vadim, 2008. "Nonlinearity, nonstationarity, and spurious forecasts," Journal of Econometrics, Elsevier, vol. 142(1), pages 1-27, January.
    9. Xiaojun Chu & Jianying Qiu, 2021. "Forecasting stock returns using first half an hour order imbalance," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3236-3245, July.
    10. Veress, Aron & Kaiser, Lars, 2017. "Forecasting quality of professionals: Does affiliation matter?," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 159-168.
    11. GIOT, Pierre & PETITJEAN, Mikael, 2006. "International stock return predictability: statistical evidence and economic significance," LIDAM Discussion Papers CORE 2006088, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    12. Erik Hjalmarsson, 2006. "Should we expect significant out-of-sample results when predicting stock returns?," International Finance Discussion Papers 855, Board of Governors of the Federal Reserve System (U.S.).
    13. Bin Chen & Kenwin Maung, 2020. "Time-varying Forecast Combination for High-Dimensional Data," Papers 2010.10435, arXiv.org.
    14. Faria, Gonçalo & Verona, Fabio, 2023. "Forecast combination in the frequency domain," Bank of Finland Research Discussion Papers 1/2023, Bank of Finland.
    15. Davide Pettenuzzo & Francesco Ravazzolo, 2016. "Optimal Portfolio Choice Under Decision‐Based Model Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.
    16. Hou, Yang & Meng, Jiayin, 2018. "The momentum effect in the Chinese market and its relationship with the simultaneous and the lagged investor sentiment," MPRA Paper 94838, University Library of Munich, Germany.
    17. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2019. "Forecasting stock returns with cycle-decomposed predictors," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 250-261.
    18. Lu Wang & Feng Ma & Guoshan Liu, 2020. "Forecasting stock volatility in the presence of extreme shocks: Short‐term and long‐term effects," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 797-810, August.
    19. Raushan Kumar, 2021. "Predicting Wheat Futures Prices in India," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(1), pages 121-140, March.
    20. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.

    More about this item

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    Statistics

    Access and download statistics

    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:taf:apeclt:v:17:y:2010:i:9:p:869-874. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.