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A Survey of Systems for Predicting S...

Caley, Jeffrey Allan.

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A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers./
Author:	Caley, Jeffrey Allan.
Description:	120 p.
Notes:	Source: Masters Abstracts International, Volume: 51-05.
Contained By:	Masters Abstracts International51-05(E).
Subject:	Engineering, Computer. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1535828
ISBN:	9781303028182

A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers.
Caley, Jeffrey Allan.

A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers. - 120 p.

Source: Masters Abstracts International, Volume: 51-05.

Thesis (M.S.)--Portland State University, 2013.

In this work, we propose and investigate a series of methods to predict stock market movements. These methods use stock market technical and macroeconomic indicators as inputs into different machine learning classifiers. The objective is to survey existing domain knowledge, and combine multiple techniques into one method to predict daily market movements for stocks. Approaches using nearest neighbor classification, support vector machine classification, K-means classification, principal component analysis and genetic algorithms for feature reduction and redefining the classification rule were explored. Ten stocks, 9 companies and 1 index, were used to evaluate each iteration of the trading method. The classification rate, modified Sharpe ratio and profit gained over the test period is used to evaluate each strategy. The findings showed nearest neighbor classification using genetic algorithm input feature reduction produced the best results, achieving higher profits than buy-and-hold for a majority of the companies.

ISBN: 9781303028182Subjects--Topical Terms:

1669061
Engineering, Computer.

A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers.
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020 $a 9781303028182
035 $a (MiAaPQ)AAI1535828
035 $a AAI1535828
040 $a MiAaPQ $c MiAaPQ
100 1 $a Caley, Jeffrey Allan. $3 2103619
245 1 2 $a A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers.
300 $a 120 p.
500 $a Source: Masters Abstracts International, Volume: 51-05.
500 $a Includes supplementary digital materials.
500 $a Adviser: Richard Tymerski.
502 $a Thesis (M.S.)--Portland State University, 2013.
520 $a In this work, we propose and investigate a series of methods to predict stock market movements. These methods use stock market technical and macroeconomic indicators as inputs into different machine learning classifiers. The objective is to survey existing domain knowledge, and combine multiple techniques into one method to predict daily market movements for stocks. Approaches using nearest neighbor classification, support vector machine classification, K-means classification, principal component analysis and genetic algorithms for feature reduction and redefining the classification rule were explored. Ten stocks, 9 companies and 1 index, were used to evaluate each iteration of the trading method. The classification rate, modified Sharpe ratio and profit gained over the test period is used to evaluate each strategy. The findings showed nearest neighbor classification using genetic algorithm input feature reduction produced the best results, achieving higher profits than buy-and-hold for a majority of the companies.
590 $a School code: 0180.
650 4 $a Engineering, Computer. $3 1669061
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0464
690 $a 0984
690 $a 0544
710 2 $a Portland State University. $b Electrical and Computer Engineering. $3 1678929
773 0 $t Masters Abstracts International $g 51-05(E).
790 $a 0180
791 $a M.S.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1535828