TY  - JOUR
T1  - A Sequential Monte Carlo Approach for Online Stock Market Prediction Using Hidden Markov Model
AU - Ahani, E. AU - Abass, O. 
JO  - Journal of Modern Mathematics and Statistics
VL  - 4
IS  - 2
SP  - 73
EP  - 77
PY  - 2010
DA  - 2001/08/19
SN  - 1994-5388
DO  - jmmstat.2010.73.77
UR  - https://makhillpublications.co/view-article.php?doi=jmmstat.2010.73.77
KW  - Sequential monte carlo
KW  -hidden markov model
KW  -state-space model
KW  -stock market
KW  -techniques
KW  -algorithm
AB  - This study attempts a development of a Sequential Monte Carlo (SMC) algorithm approach of 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 very effectively 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 and the Sequential Monte Carlo (SMC) method is used. Secondly, the researchers make the prediction using SMC method in HMM and then develop 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.
ER  -