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Recognizing and Forecasting the Sign of Financial Local Trends using Hidden Markov Models

  • M. Bigeco
  • E. Grosso
  • E. Otranto


The problem of forecasting financial time series has received great attention in the past, from both Econometrics and Pattern Recognition researchers. In this context, most of the efforts were spent to represent and model the volatility of the financial indicators in long time series. In this paper a different problem is faced, the prediction of increases and decreases in short (local) financial trends. This problem, poorly considered by the researchers, needs specific models, able to capture the movement in the short time and the asymmetries between increase and decrease periods. The methodology presented in this paper explicitly considers both aspects, encoding the financial returns in binary values (representing the signs of the returns), which are subsequently modelled using two separate Hidden Markov models, one for increases and one for decreases, respectively. The approach has been tested with different experiments with the Dow Jones index and other shares of the same market of different risk, with encouraging results.

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Paper provided by Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia in its series Working Paper CRENoS with number 200803.

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Date of creation: 2008
Date of revision:
Handle: RePEc:cns:cnscwp:200803
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  1. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
  2. Dueker, Michael J, 1997. "Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 26-34, January.
  3. Edoardo Otranto, 2005. "The multi-chain Markov switching model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(7), pages 523-537.
  4. Holthausen, Robert W. & Larcker, David F., 1992. "The prediction of stock returns using financial statement information," Journal of Accounting and Economics, Elsevier, vol. 15(2-3), pages 373-411, August.
  5. Anthony S. Tay & Peter F. Christoffersen & Francis X. Diebold & Roberto S. Mariano & Yiu Kuen Tse, 2006. "Direction-of-Change Forecasts Based on Conditional Variance, Skewness and Kurtosis Dynamics : International Evidence," Finance Working Papers 22481, East Asian Bureau of Economic Research.
  6. Jeffrey A. Frankel & Andrew K. Rose, 1996. "Currency crashes in emerging markets: an empirical treatment," International Finance Discussion Papers 534, Board of Governors of the Federal Reserve System (U.S.).
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