Abstract The fnancial market infuences personal corporate fnancial lives and the economic health of a country. Price change of stock market is not a completely random model. The pattern of fnancial market has been observed by some economists, statisticians and computer scientists. This paper gives a detailed idea about the sequence and state prediction of stock market using Hidden Markov Model and also making inferences regarding stock market trend. The one day difference in close value of stock market value has been used for some period and the corresponding transition probability matrix and emission probability matrix are obtained. Seven optimal hidden states and three sequences are generated using MATLAB and then compared. Keywords: Hidden Markov Model, Transition Probability Matrix, Emission Probability Matrix, Stock Market, States and Sequence Forecasting The Stock Market Values Using Hidden Markov Model R. Sasikumar*, A. Sheik Abdullah* * Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India. Email: sasikumarmsu@gmail.com and sheik.stat@gmail.com Introducton The most of the trading in Indian stock market is classifed in two categories, the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). The BSE has been functioning since 1875. The NSE was founded in 1992 and started trading in 1994. Even though both exchanges have the same trading mechanism, trading hours, settlement process, etc., they are having high demand from people. The two prominent Indian market indices are Sensex and S&P CNX Nifty. Financial market (Stock Market) is a platform for investors to own some shares of a company. Investors will then become a part of the company members and share in both gains and losses of that particular company. This is a better way for the investors to get extra income apart from their regular salary. Changes of share prices on every day make it more volatile and diffcult to predict the future price. When purchasing a stock, it does not guarantees to give anything in return. Thus, it makes stocks risky in investment, but investors can also get high proft return. When investors take wrong decision in choosing the counters, it may end up in capital loss. The behavior of stock market returns has been deeply discussed over some years. In this paper, the hidden states and sequence are generated for stock market values using Hidden Markov Model (HMM) through software. Review of related works There are so many researches going on stock market analysis. Rabiner (1989) used precise HMMs, in which the state sequence estimation problem can be solved very effciently by the Viterbi algorithm whose complexity is linear in the number of nodes, and quadratic in the number of states. However, this algorithm only emits a single optimal (most probable) state sequence, even in cases where there are multiple (equally probable) optimal solutions. Hassan and Baikunth Nath (2005) used HMM to predict next day closing price for some of the airlines. They considered four input attributes for a stock, and they were the opening price, highest price, lowest price and closing price. These four attributes of previous day were used to predict next day’s closing price. Hassan (2009) introduced the new combination of HMM and Fuzzy model to forecast the stock market data. He classifed the data set as daily opening, high, low and closing prices to predict the next day’s closing price. HMM-fuzzy model is more reliable and proftable than the other model. Jyoti Badge (2012) used Macro-Economic factor as a technical indicator, which is used to identify the patterns of the market at a particular time. For selecting technical indicator author was applying principal component Article can be accessed online at http://www.publishingindia.com