Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
SOPS: Stock Prediction Using Web Sentiment
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
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We propose a stock market portfolio recommender system based on association rule mining (ARM) that analyzes stock data and suggests a ranked basket of stocks. The objective of this recommender system is to support stock market traders, individual investors and fund managers in their decisions by suggesting investment in a group of equity stocks when strong evidence of possible profit from these transactions is available. Our system is different compared to existing systems because it finds the correlation between stocks and recommends a portfolio. Existing techniques recommend buying or selling a single stock and do not recommend a portfolio. We have used the support confidence framework for generating association rules. The use of traditional ARM is infeasible because the number of association rules is exponential and finding relevant rules from this set is difficult. Therefore ARM techniques have been augmented with domain specific techniques like formation of thematical sectors, use of cross-sector and intra-sector rules to overcome the disadvantages of traditional ARM. We have implemented novel methods like using fuzzy logic and the concept of time lags to generate datasets from actual data of stock prices. Thorough experimentation has been performed on a variety of datasets like the BSE-30 sensitive Index, the S&P CNX Nifty or NSE-50, S&P CNX-100 and DOW-30 Industrial Average. We have compared the returns of our recommender system with the returns obtained from the top-5 mutual funds in India. The results of our system have surpassed the results from the mutual funds for all the datasets. Our approach demonstrates the application of soft computing techniques like ARM and fuzzy classification in the design of recommender systems.