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ICONIP'96: TUTORIAL ON FINANCIAL ENGINEERING
============================================
Three tutorials on financial application of Neural Networks will
be presented on Tuesday, September 24, 1996, which is one of the
events of the 1996 International Conference on Neural Information
Processing (ICONIP'96) to be held from 24-27 September, 1996. Each tutorial
lasts for 3 hours, including a 15 minutes tea break and will be
held in the Hong Kong Convention and Exhibition Centre.

Schedule:
8:45am -- 11:45am 	Tutorial 1: "Time Series Models: Classical and
			   Nonlinear Approaches", by Prof. John E Moody,
        		   Oregon Graduate Institute.
11:45am -- 12:45pm 	Lunch Break
12:45pm -- 3:45pm 	Tutorial 2: "Neural Networks in Financial
			   Engineering", by Prof. Paul Refenes,
			   London Business School.
3:45pm -- 4:00pm 	Tea Break
4:00pm -- 7:00pm 	Tutorial 3: "Option Pricing in Modern Finance
			   Theory and the Relevance of Artificial Neural
        		   Network", by Prof. Halbert White,
        		   Univ. of Calif., San Deigo.

You can find detail in 
	URL: http://www.cs.hku.hk/~kpchan/iconip96-tutorial.html

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Tutorial 1: Time Series Models: Classical and Nonlinear Approaches.
	    Professor John E. Moody<BR>
	    Computer Science Department, Oregon Graduate Institute, U.S.A.
This tutorial will present an overview of both classical and nonlinear
approaches to analyzing and predicting time series. The presentation will
begin with a review of linear time series models (eg. AR, MA, ARIMAX),
followed by simple nonlinear models (eg. bilinear, regime switching, ARCH).
Relevant results of chaos theory, including the limits of predictability,
strange attractors, and "embeddology" will be summarized.  Next, general
nonlinear models, including both feed forward and recurrent neural networks
will be discussed.  Finally, I will present several time series modeling
case studies from economics, physics, and medicine will be presented.
--------------------------------------------------------------------
Tutorial 2: Neural Networks in Financial Engineering
	    Professor A-P. N. Refenes
	    Neuro Forecasting Unit, London Business School, U.K.
The potential for computational intelligence applications in financial
engineering has yet to fully take off. For neural networks to become
common place in the financial engineering community, it requires the
synergetic combination of expertise in financial economics and the
mathematical/natural sciences. The objectives of this short course
are as follows:
      *	to create an awareness in the neural network and engineering
        communities of the basic needs and approaches of financial
        engineering.
      *	to formulate neural learning in terms of econometric and
        statistical model which will expose the fundamental advantages
        and computational properties of the technology
      *	to create an awareness of the breadth of applicability of neural
        techniques in diverse areas of financial engineering.
      * to expose some of the basic weaknesses of neural modelling
        techniques when applied to finance problems in which the
        stochastic component in the data generating processes is by
        far the most prevalent, in comparison to  any deterministic
        component.
      *	to identify the main areas of the research agenda if neural
        networks are to become commonplace in finance and econometrics
        alongside their statistical counterparts.
The main elements in the programme reflect these objectives. This is an
introductory tutorial, a collection of case studies, and a session on the
main research topics that need to be dealt with to satisfy the last
objective (amongst others).                   
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Tutorial 3: Option Pricing in Modern Finance Theory and the Relevance
	    of Artificial Neural Networks
	    Professor Halbert White, Department of Economics,
	    University of California, San Diego, U.S.A.
This tutorial will review the theory of option pricing in the modern
mathematical finance literature, including the popular Black-Scholes
model and some of its extensions. With this as foundation, we will
investigate the role that artificial neural networks can play in pricing
options and in fashioning appropriate option trading strategies. The
tutorial will assume a basic knowledge of probability theory and analysis.
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Registration Fee: 
		One Tutorial 	All 3 tutorials
    Member* 	  HK$ 1,000 	   HK$ 1,500 
    Non-member 	  HK$ 1,200 	   HK$ 1,800
    Students** 	  HK$ 1,000	   HK$ 1,500

 * You are eligible to the member rate if you are a member of IEEE, ACM,
        HKCS, CNNC, ENNS, INNS or JNNS. Photocopies of your membership
        card must be provided with the application form.
 ** Photocopies of student cards must be provided together with the application
        form 

Registration fee include tutorial, tutorial notes and coffee breaks. Lunch
is NOT inlcuded.

Completed form should be sent to:
	ICONIP'96 Secretariat,
	Department of Computer Science & Engineering,
	The Chinese University of Hong Kong,
	Shatin, N.T. Hong Kong.

All Cheques should be made payable to ICONIP'96

For further information, please contact:
	Dr K P Chan,
	Department of Computer Science,
	The University of Hong Kong.
	e-mail: kpchan@cs.hku.hk
	Fax: (+852) 2559 8447

------------------------ Registration Form ------------------------
TUTORIAL ON FINANCIAL ENGINEERING
Registration Form

Name:(Prof/Dr/Mr/Mrs/Ms*) ________________________________________________
Organization: ____________________________________________________________
Address: _________________________________________________________________
__________________________________________________________________________
Telephone No.: _______________________   Fax No.: ________________________
e-mail address: ______________________   Student: (Yes/No*)
Membership Number: (Yes/No*)(IEEE/ACM/HKCS/CNNC/ENNS/INNS/JNNS*)__________
Payment:
I enclose a cheque of the amount _____________ from ________________(Bank)

All Cheques should be made payable to ICONIP'96

Completed form should be sent to ICONIP'96 Secretariat, Department of Computer
Science & Engineering, The Chinese University of Hong Kong, Shatin, N.T. Hong
Kong.

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