AN EXPERIMENTAL STUDY OF WARE HOUSE MARKET AND INVESTMENT CONTROL IN DMT

The data mining and its tool has played a vital role in exploring the data from different ware houses. Stock market is considered too uncertain to be predictable. Many individuals have developed methodologies or models to increase the probability of making a profit in their stock investment. The overall hit rates of these methodologies and models are generally too low to be practical for real-world application. One of the major reasons is the huge fluctuation of the market. The market with huge volume of investor with good enough knowledge and have a prediction as well as control over their investments. The stock market some time fails to attract new investors. The reason states that non-aware and also people do not want to come forward to fall in to the risk. An approach with adequate expertise is designed to help investors to ascertain veiled patterns from the historic data that have feasible predictive ability in their investment decisions. 

The  ANN  (Artificial  Neural  Network)  is  broadly  accepted  Data  mining  technique  by  finance  area to detect the affiliation among the non-linear variables. LM Vs SCG for NIFTY-MIDCAP50 Actual Closing Price Vs Neural network, LM Vs SCG for RELIANCE Actual Closing price Vs Neural network closing price. This thesis discusses the concept and implementation of prediction of the Stock Market Behavior and Investment Decision Making using Benchmark Algorithms for Naive Investors. It explains Portfolio determination using PSO adopted Clustering technique. It also concentrates on the theory and the enhancement of the portfolio determination using Multi objective Optimization.

However,  real-life  financial  market  imposes  some  nonlinear constraints  such  as  cardinality  constraints,  which  limit  the  number  of assets  held  in  the  portfolio,  minimum  transaction  lots  constraints,  which require  holding  discrete  units  in  assets,  multiples  of  minimum  lots,  or transaction costs, which tend to eliminate small holding. This thesis discusses the concept and implementation of prediction of the Stock Market Behavior and Investment Decision Making using Aprior Algorithms for   Naive   Investors.