Decision Support System for Investment in Stock Market using OAA-SVM


Affiliations

  • NDMVPS's KBTCOE, Department of Computer Engineering, Nashik, Maharashtra, 422013, India

Abstract

The investment in stock market could be high-risk. Stock price depends on several factors,such as currency exchange rate, economics situation, and flow of funds. In stock market, depending on a stock selection anda suitable time on trading successful investors can earn maximum profits. Because of the financial crisis and scoring profits,it is mandatory to have a secure prediction of the values of the stocks. Generally, investors use two Statistical techniques formaking a decision, which are the fundamental analysis and the technical analysis and many no of machine learning models have been investigated for stock prediction such as Genetic Algorithm (GA), Support Vector Machine (SVM) and Neural Network (NN). In proposed system, we have used the binary tree SVM algorithm which is one of the mainstream algorithms for multi-class classification in the fields of pattern recognition and machine learning. In order to reduce the training and testing time of system, One-Against-All SVM (OAA-SVM) algorithms will be proposed for multi-class classification.

Keywords

Hyper-Plane, Optimization, EMA, MACD, OAA, RSI, SVM

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References

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