Portland State University. Electrical and Computer Engineering
Date of Publication
Master of Science (M.S.) in Electrical and Computer Engineering
Electrical and Computer Engineering
Stock exchanges -- Computer simulation, Financial engineering, Stock price forecasting, Machine learning -- Mathematical models, Support vector machines
1 online resource (ix, 110 pages)
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.
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Caley, Jeffrey Allan, "A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers" (2013). Dissertations and Theses. Paper 2001.