Stock Market Prediction using Data Mining Classifiers and their Efficiency Analysis
Financial industry has seen tremendous reforms due to increase in technology and use of Artificial intelligence and Data mining. The industry has seen a recent increase of investment in technological domain by the Financial pioneers and has also seen an increase in the trends of investment tips via use of automation. This research aims to use data mining technology to predict Dow Jones stock market index by training classifiers to obtain rule set and patterns from the collected past stock market data. Use of complete automation including algorithmic trading and artificial intelligence has revolutionalized the arena of the Financial industry. This research work compares several different classifiers in their efficiency to detect future market trends in the germinant stage and proposes a cohesive system to be incorporated with the client's investment portfolio and send use a synthetically intelligent module to send investment suggestions.
Keywords - Data Mining, Classifiers, Naïve bayes, KNN, oneR, zeroR, Random forrest,Random Tree, j48, Adaboost, Logical Model tree, ANN, Fuzzy logic