Abstract—In this paper, we propose a hybrid machine learning
system based on Genetic Algorithm (GA) and Support Vector
Machines (SVM) for stock market prediction. A variety of indicators
from the technical analysis field of study are used as input features.
We also make use of the correlation between stock prices of different
companies to forecast the price of a stock, making use of technical
indicators of highly correlated stocks, not only the stock to be
predicted. The genetic algorithm is used to select the set of most
informative input features from among all the technical indicators.
The results show that the hybrid GA-SVM system outperforms the
stand alone SVM system.
Keywords—Genetic Algorithms, Support Vector Machines,
Stock Market Forecasting.
TOCK market prediction is regarded as a challenging task
in financial time-series forecasting. This is primarily
because of the uncertainties involved in the movement of the
market. Many factors interact in the stock market including
political events, general economic conditions, and traders’
expectations. Therefore, predicting market price movements is
investigations, movements in market prices are not random.
Rather, they behave in a highly non-linear, dynamic manner.
Also, the ability to predict the direction and not the exact
value of the future stock prices is the most important factor in
making money using financial prediction. All the investor
needs to know to make a buying or selling decision is the
expected direction of the stock. Studies have also shown that
predicting direction as compared to value can generate higher
The rest of this paper is organized as follows: In section 2,
we give an overview of previous studies in this area. In
sections 3 and 4, we give a brief introduction to the basic
concepts behind the theory of technical analysis and SVM
respectively. In section 5, the stock prediction problem is