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Stock Price Predictors Based on Bayesian Method

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  • Dušan Marček

Abstract

Many stock Price Predictors transformating input (historical data, theory) to output (forecast) have been publishing. For example papers [1], [2] deal with ARMA and exponential smoothing methods. Proposed contribution present an approach based on Bayesian method. Bayesian method, applied to stock price forecast, enables to predict stock prices when much historical data are unavailable or where the users of such information processing systems might not be able to accumulate them. This article shows basic approach to Bayesian estimation of constant process and demonstrates its methodology. Finally, we present example illustrating the application of this approach.

Suggested Citation

  • Dušan Marček, 1999. "Stock Price Predictors Based on Bayesian Method," Bulletin of the Czech Econometric Society, The Czech Econometric Society, vol. 6(9).
  • Handle: RePEc:czx:journl:v:6:y:1999:i:9:id:70
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    File URL: http://ces.utia.cas.cz/bulletin/index.php/bulletin/article/view/70
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    References listed on IDEAS

    as
    1. Dirk Tasche, 2004. "The single risk factor approach to capital charges in case of correlated loss given default rates," Papers cond-mat/0402390, arXiv.org, revised Feb 2004.
    2. Konstantin Belyaev & Aelita Belyaeva & Tomas Konecny & Jakub Seidler & Martin Vojtek, 2012. "Macroeconomic Factors as Drivers of LGD Prediction: Empirical Evidence from the Czech Republic," Working Papers 2012/12, Czech National Bank, Research Department.
    3. Acharya, Viral V. & Bharath, Sreedhar T. & Srinivasan, Anand, 2007. "Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries," Journal of Financial Economics, Elsevier, vol. 85(3), pages 787-821, September.
    4. Jiri Witzany, 2011. "A Two Factor Model for PD and LGD Correlation," Bulletin of the Czech Econometric Society, The Czech Econometric Society, vol. 18(28).
    5. Jon Frye, 2000. "Depressing recoveries," Emerging Issues, Federal Reserve Bank of Chicago, issue Oct.
    6. Stefano Caselli & Stefano Gatti & Francesca Querci, 2008. "The Sensitivity of the Loss Given Default Rate to Systematic Risk: New Empirical Evidence on Bank Loans," Journal of Financial Services Research, Springer;Western Finance Association, vol. 34(1), pages 1-34, August.
    7. Seidler, Jakub & Horvath, Roman & Jakubík, Petr, 2009. "Estimating expected loss given default in an emerging market: the case of Czech Republic," Journal of Financial Transformation, Capco Institute, vol. 27, pages 103-107.
    8. De Graeve, F. & Kick, T. & Koetter, M., 2008. "Monetary policy and financial (in)stability: An integrated micro-macro approach," Journal of Financial Stability, Elsevier, vol. 4(3), pages 205-231, September.
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    More about this item

    JEL classification:

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

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