My email: Jianfeng.Huo@dartmouth.edu
Motivation:
Recently, a large amount of amazing work has been done in the
area of analyzing and predicting stock prices and index changes using Machine
Learning Algorithms. Intelligent Trading Systems has been used for most of the
stock traders to help them in predicting prices based on various situations and
conditions, thereby helping them in making instantaneous investment decisions.
Stock market prediction is regarded as one of the most challenging task in
financial time-series forecasting. This is primarily because the underlying
nature of the uncertain financial domain and in part because of the mix of
known parameters (Previous Day’s Closing Price, P/E Ratio etc.) and unknown
factors (Election Results, Rumors etc.).
In my project, I will implement a hybrid machine learning system which is
proposed by Rohit Choudhry, and Kumkum Garg [1] 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. The data used
for this study were obtained from the Yahoo Finance website [2].
Before milestone, I expect to implement
the hybrid machine learning system in Matlab. After milestone, I will focus on
the comparison the experience results and to add new features to make it more
efficient and accurate.
Timeline:
l Read papers about using GA and SVM for
stock market prediction
l Before milestone implement the hybrid
machine learning system in Matlab.
l Before final debug the algorithm, test
data and improve accuracy of the system.
l Analyze experiment results and write
the final report.
Reference
[1] R. Choudhry and K. Garg, "A
hybrid machine learning system for stock market forecasting," Proceedings
of World Academy of Science, Engineering and Technology, vol.29, pp. 315-318,
2008.
[2] http://in.finance.yahoo.com/
Other Resources
[1] L. J. Cao and F. E. H. Tay,
"Financial forecasting using support vector machines", Neural
Comput. Applicat., vol. 10, no. 2, pp. 184-192,
2001.
[2] Vatsal H.
Shah, “Machine Learning Techniques for Stock Prediction”, http://www.vatsals.com/Essays/MachineLearningTechniquesforStockPrediction.pdf