Thesis available: RBF Approach to Financial Time Series Analysis

[Jim Hutchinson]

My thesis, "A Radial Basis Function Approach to Financial Time Series
Analysis" is now available from the MIT AI Lab Publications office as
Technical Report 1457, both in hardcopy and in FTP-able compressed
postscript form. Abstract follows. You may want to preview it before
printing: it is 159 pages, and takes about 2MB of disk uncompressed.
Comments and questions to hutch at phz.com are welcome! 

Jim Hutchinson			Email: hutch at phz.com
PHZ Partners			Voice: +1 (617) 494-6000
One Cambridge Center		FAX:   +1 (617) 494-5332
Cambridge, MA 02142 USA

---------------------- Abstract ----------------------------------

A RADIAL BASIS FUNCTION APPROACH TO FINANCIAL TIME SERIES ANALYSIS

			Jim Hutchinson

Billions of dollars flow through the world's financial markets every day,
and market participants are understandably eager to accurately price
financial instruments and understand relationships involving them.
Nonlinear multivariate statistical modeling on fast computers offers the
potential to capture more of the underlying dynamics of these high
dimensional, noisy systems than traditional models while at the same time
making fewer restrictive assumptions about them.  For this style of
exploratory, nonparametric modeling to be useful, however, care must be
taken in fundamental estimation and confidence issues, especially concerns
deriving from limited sample sizes.  This thesis presents a collection of
practical techniques to address these issues for a modeling methodology,
Radial Basis Function networks.  These techniques include efficient methods
for parameter estimation and pruning, including a heuristic for setting
good initial parameter values, a pointwise prediction error estimator for
kernel type RBF networks, and a methodology for controlling the ``data
mining'' problem.  Novel applications in the finance area are described,
including the derivation of customized, adaptive option pricing formulas
that can distill information about the associated time varying systems that
may not be readily captured by theoretical models.  A second application
area is stock price prediction, where models are found with lower
out-of-sample error and better ``paper trading'' profitability than that of
simpler linear and/or univariate models, although their true economic
significance for real life trading is questionable.  Finally, a case is
made for fast computer implementations of these ideas to facilitate the
necessary model searching and confidence testing, and related
implementation issues are discussed.

------------------- FTP Retrieval Instructions -------------------

% ftp publications.ai.mit.edu
Name (publications.ai.mit.edu:hutch): anonymous
Password: (your email address)
..
ftp> cd ai-publications/1993
ftp> binary
ftp> get AITR-1457.ps.Z
ftp> quit
% uncompress AITR-1457.ps.Z
% lpr AITR-1457.ps 


-------------------- For hardcopies contact -----------------------

Sally Richter
MIT AI Laboratory Publications Office 
email: publications at ai.mit.edu 
phone: 617-253-6773 
fax: 617-253-5060

Ask for TR-1457.