A new investment strategy based on data mining and Neural Networks

Abstract

In this paper, we present a new investment strategy for optimal gains on investments in the stock market. Neural Network (NN)-based framework is used for trading prediction and forecasting. To this end, statistical measures based on return and volatility are used to filter out low performing sectors in the stock market. A simple but effective method based on price Simple Moving Averages (SMAs) is used to measure volatility for a given stock. The proposed NN-based system uses the strongest performing indices for stock market forecasting. In addition to predicting investment decisions such as Buy or Sell, the proposed framework also aims at maximizing investment gains (or returns). The proposed NN-based framework rely on historical data and provides investors investing strategies for optimal trading. Training data is extracted extracted from historical weekly data (from the Yahoo Finance). Simulation results indicate that the proposed framework can help investors making investment decisions and increasing their trading profitability.