Predicting Stock Prices Using Technical Analysis and ...

Predicting Stock Prices Using Technical Analysis and Machine Learning

Jan Ivar Larsen

Master of Science in Computer Science

Submission date: June 2010

Supervisor:

Helge Langseth, IDI

Norwegian University of Science and Technology Department of Computer and Information Science

Problem Description

In this thesis, a stock price prediction model will be created using concepts and techniques in technical analysis and machine learning. The resulting prediction model should be employed as an artificial trader that can be used to select stocks to trade on any given stock exchange. The performance of the model will be evaluated on stocks listed on the Oslo Stock Exchange.

Assignment given: 15. January 2010 Supervisor: Helge Langseth, IDI

Abstract

Historical stock prices are used to predict the direction of future stock prices. The developed stock price prediction model uses a novel two-layer reasoning approach that employs domain knowledge from technical analysis in the first layer of reasoning to guide a second layer of reasoning based on machine learning. The model is supplemented by a money management strategy that use the historical success of predictions made by the model to determine the amount of capital to invest on future predictions. Based on a number of portfolio simulations with trade signals generated by the model, we conclude that the prediction model successfully outperforms the Oslo Benchmark Index (OSEBX).

 ................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download