A Sequential Monte Carlo Approach for Online Stock Market Prediction Using Hidden Markov Model
by Ahani, E., and Abass O.
This paper attempts an application of a sequential Monte Carlo (SMC) algorithm to perform online prediction based on joint probability distribution in hidden Markov Model (HMM). SMC methods, a general class of Monte Carlo methods, are mostly used for sampling from sequences of distributions. Simple examples of these algorithms are extensively used in the tracking and signal processing literature. Recent developments indicate that these techniques have much more general applicability, and can be applied to statistical inference problems. Firstly, due to the problem involved in estimating the parameter of HMM, the HMM is now represented in a state space model. Secondly, we make the prediction using SMC method by developing the corresponding on-line algorithm. At last, the data of daily stock prices in the banking sector of the Nigerian Stock Exchange (NSE) (price index between the years 1st January 2005 to 31st December 2008) are analyzed, and experimental results reveal that the method proposed in this manner is effective.
Sequential Monte Carlo, Hidden Markov Model, State-Space model, Stock Market
Tsionas, Efthymios G.,
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