Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Analysis of Hidden Markov Models and Support Vector Machines in Financial Applications

Satish Rao and Jerry Hong

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2010-63
May 12, 2010

http://www.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-63.pdf

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. *Simlio LLC is a startup co-founded by Jerry Hong. It is currently a stock research platform on the web that enables users to draw graphs at ease as well as perform intensive formula calculations to see how well an idea would profit over time.

Advisor: Satish Rao


BibTeX citation:

@mastersthesis{Rao:EECS-2010-63,
    Author = {Rao, Satish and Hong, Jerry},
    Editor = {Bartlett, Peter},
    Title = {Analysis of Hidden Markov Models and Support Vector Machines in Financial Applications},
    School = {EECS Department, University of California, Berkeley},
    Year = {2010},
    Month = {May},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-63.html},
    Number = {UCB/EECS-2010-63},
    Abstract = {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.

*Simlio LLC is a startup co-founded by Jerry Hong. It is currently a stock research platform on the web that enables users to draw graphs at ease as well as perform intensive formula calculations to see how well an idea would profit over time.}
}

EndNote citation:

%0 Thesis
%A Rao, Satish
%A Hong, Jerry
%E Bartlett, Peter
%T Analysis of Hidden Markov Models and Support Vector Machines in Financial Applications
%I EECS Department, University of California, Berkeley
%D 2010
%8 May 12
%@ UCB/EECS-2010-63
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-63.html
%F Rao:EECS-2010-63