Stock Prediction and Automated Trading System

Abstract

Stock market decision making is a very challenging and difficult task of financial data prediction. Prediction about stock market with high accuracy movement yield profit for investors of the stocks. Because of the complexity of stock market financial data, development of efficient models for prediction decision is very difficult, and it must be accurate. This study attempted to develop models for prediction of the stock market and to decide whether to buy/hold the stock using data mining and machine learning techniques. The classification techniques used in these models are naive bayes and random forest classification. Technical indicators are calculated from the stock prices based on time-line data and it is used as inputs of the proposed prediction models. 10 years of stock market data has been used for prediction. Based on the data set, these models are capable to generate buy/hold signal for stock market as a output. The main goal of this paper is to generate decision as per user’s requirement like amount to be invested, time duration for investment, minimum profit, maximum loss using machine learning and data analysis techniques.