This paper presents two approaches in helping investors make better decisions. First, we discuss conventional methods, such as using the Efficient Market Hypothesis and technical indicators, for forecasting stock prices and movements. We will show that these methods are inadequate, and thus, we need to rethink the issue. Afterwards, we will discuss using artificial intelligence, such as Hidden Markov Models and Support Vector Machines, to help investors gather and compute enormous amount of data that will enable them to make informed decisions. We will leverage the Simlio engine to train both the HMM and SVM on past datasets and use it to predict future stock movements. The results are encouraging and they warrant future research on using AI for market forecasts.
Title
Analysis of Hidden Markov Models and Support Vector Machines in Financial Applications
Published
2010-05-12
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2010-63
Type
Text
Extent
27 p
Archive
The Engineering Library
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