International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438 Volume 4 Issue 4, April 2015 www.ijsr.net Licensed Under Creative Commons Attribution CC BY A Survey on Stock Market Prediction Techniques Shyam Kute 1 , Sunil Tamhankar 2 1 Department of Information Technology Walchand College of Engineering, Sangli, India 2 Department of Electronics Engineering, Walchand College of Engineering, Sangli, India Abstract: Different techniques are available for the prediction of stock market. Very popular some of these are Neural Network, Data Mining, Hidden Markov Model(HMM) And Neuro-Fuzzy system. From these Neural Network and Neuro-Fuzzy Systems are the most leading machine learning techniques in stock market index prediction area. Other traditional methods do not cover all possible relation of stock price movements. Neural Network and Markov Model can be used exclusively in the financial markets and forecasting of stock price. Neural Networks discovers the non linear relationship in the input data set without knowing the relation between input and output. For the sample data which contain noisy information with least principle ANN can generalize and correctly infer the unseen part of data. Hence ANN suits well than any other models in the prediction of stock markets. Keywords: Artificial Neural Network(ANN), Data Mining, Hidden Markov Model(HMM), Neuro-Fuzzy system 1. Introduction In general the prediction is to know about the future. So, for the investment of equity or money the prediction of stock market is very important. The similar terms for prediction markets are decision markets, future idea, virtual markets, informative markets and predictive markets. We know that the market is changing, ahead, difficult to predict and disorganize in nature. Hence by using normal analytical methods the prediction of stock is difficult like time series analysis. For beginning conditions confusing systems are sensitive. So the relatively neural networks are effective to deal with such a non-linear system [1]. Financial market every time undergoes to changing behaviour. The area of selection for investors to development of powerful trading facilities and communication has enlarged. Due to this traditional capital market theory has also changed and methods of financial analysis have greatly improved. From many years researchers trying to predict stock return or stock index which is an important financial subject. From this one assumptions is made that the fundamental information such as high price, low price, previous close, open price, close price, last price, average price of any equity past values publicly available which is related with future stock indices or returns[3]. Industry specific information like industrial production and growth rate of consumer price, economic variables such as interest rates and exchange rates and divided yields of company is necessary for the prediction of stock. The survey of recent techniques such as Artificial Neural Network Hidden Markov Model, Data Mining and Neuro- Fuzzy system offer useful tools for forecasting noisy environments like stock market. This study aims to provide intelligent techniques to forecast stock market indexes and stock market prices. A stock market index represents movement of comany stock which shows prices of stock going up or down. For forecasting process Firm characteristics are not taken into consideratio.The researchers could trying to develop a model to forecast individual stock prices to overcome this limitation. A. Stock Market Organized and regulated financial market where securities such as bonds, notes and shares are bought and sold at price governed by the forces of demand and supply. Stock Market basically serves as. 1) Primary market where corporations, governments, municipolities and other incorporated bodies can raise capital by channeling saving of the investors into productive venture. 2) Secondary market where investors can sell their securities to other investors for cash, thus reducing the risk of investment and maintaining liquidity in the system. The Indian stock market is worlds third largest stock market on the basis of investors base and has collection pool of about 20 million investors. Stock is basically a share in the ownership of a company. Stocks are partial ownership of businesses instead of stock tickers piece of paper, which can be traded in stock market. If company ownership is divides in 100 parts and we are the investor purchase one part which is equal to one share then we own one percent of that company. Stock exchange uses a trading system which is order driven automated matching system. Stock prices are defined on the basis of at any time how many buyers and sellers available for same stock in the market. If no. of buyers are more than sellers then stock price becomes high and if no. of sellers higher than buyers then stock price becomes low. If order does not find the match then it remains in the system and waiting for the fresh orders or updation of the previous orders which are already present. The buy order and sell order are actually categorized in to best buy order and best sell order. These best buy and sell order looked in to counter party angle. The best buy order is which is highest price and best sell order is which is lowest price. Logic behind this is the best buy order with highest price meaning the seller counterparty will like to sell it in highest possible price thats why it is highest price. And best sell order with lowest price meaning the counterparty buyer Will buy that with lowest security price. With this logic system will match the orders and executes in to the traders system. Stock market is Paper ID: SUB152824 303