Ii. Application of Neural Networks in Stocks
نویسندگان
چکیده
Identification of useful patterns in price movement of a stock in stock market needs tremendous analytical skills and effort. Careful analysis of the available technical indicators will help finding the right timing of trading of a stock to maximize the gains. To help investors manage their portfolios, we propose a tool for clustering and classification of stock market data using an unsupervised learning algorithm, Self-Organizing Map (SOM).Our research is intended to assist users in identifying stocks and guide them with the correct timings of buying or selling of these stocks. It will help the investors to maximize their gains. We found that the Self-Organizing Map algorithm can analyze and cluster the stock data reasonably well. This paper proposes an unsupervised clustering approach based method for stocks selection for our portfolio. The portfolio formed with selected stocks gives a maximum of 40.08% and an average of 20.6%.
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