Recognizing and Forecasting the Sign of Financial Local Trends using Hidden Markov Models
AbstractThe 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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Bibliographic InfoPaper provided by Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia in its series Working Paper CRENoS with number 200803.
Date of creation: 2008
Date of revision:
markov models; asymmetries; binary data; short-time forecasts;
Find related papers by JEL classification:
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-03-08 (All new papers)
- NEP-ECM-2008-03-08 (Econometrics)
- NEP-ETS-2008-03-08 (Econometric Time Series)
- NEP-FOR-2008-03-08 (Forecasting)
- NEP-ORE-2008-03-08 (Operations Research)
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