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Application Of Improved Feature Selection Algorithm In Svm Based Market Trend Prediction Model, Qi Li
Application Of Improved Feature Selection Algorithm In Svm Based Market Trend Prediction Model, Qi Li
Dissertations and Theses
In this study, a Prediction Accuracy Based Hill Climbing Feature Selection Algorithm (AHCFS) is created and compared with an Error Rate Based Sequential Feature Selection Algorithm (ERFS) which is an existing Matlab algorithm. The goal of the study is to create a new piece of an algorithm that has potential to outperform the existing Matlab sequential feature selection algorithm in predicting the movement of S&P 500 (^GSPC) prices under certain circumstances. The two algorithms are tested based on historical data of ^GSPC, and Support Vector Machine (SVM) is employed by both as the classifier. A prediction without feature selection algorithm …
A Survey Of Systems For Predicting Stock Market Movements, Combining Market Indicators And Machine Learning Classifiers, Jeffrey Allan Caley
A Survey Of Systems For Predicting Stock Market Movements, Combining Market Indicators And Machine Learning Classifiers, Jeffrey Allan Caley
Dissertations and Theses
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 …