BOND UNIVERSITY School of Information Technology

INFM 333 Introduction to Computational Finance

Lecturer and Course Coordinator: Dr Clarence N W Tan,
BS ElecEngr (Computers), MS Ind&SysEngr, MBA (USC), Ph.D.(Bond)
ASIA, AIBF(Sr), ATAA, MASC (Australia), F. InstBA, FBSC (UK)
Assistant Professor

Office: School of Information Technology, ARCH Level 5
Tel (Office): (07) 5595 3366
Fax: (07) 5595 3320
Tel. (A/H): (0414) 988-986
e-mail: ctan@computer.org
Course URL: http://www.wave.its.bond.edu.au/INFM733
Personal URL: http://www.oocities.org/WallStreet/8823
Office Hours: TBA Other times: By Appointment
Best form of contact: e-mail

Introduction to Computational Finance Topics

This course will introduce students to various contemporary soft-computing applications in finance. Soft-computing methods are advanced technology Artificial Intelligence (AI) methods such as Artificial Neural Networks (ANNs), Genetic Algorithms (GA), Rule-based Expert systems, and Hybrid systems. Some areas of financial applications that will be studied include financial markets forecasting, trading systems, financial analysis modeling and financial distress predictions/classifications.

The demand for financial engineers with these skills are high and this course will provided both Commerce and IT students the opportunity to enhance their marketability in the finance sector by exposing them to cutting edge financial technology. There will be hands-on laboratory sessions for students to have the opportunity to apply the methods taught. Students are expected have competency in basic computer skills such as working with spreadsheets. Basic knowledge of financial theories and markets is desirable but not required as the financial knowledge required to apply these methods will be taught.

Prerequisites: Competency in basic computer skills eg. working with spreasheets equivalent to CORE 110, basic finance knowledge.

Recommended: FINC 200, CORE 110

Text:

1. Excerpts from forthcoming book by Dr C N W Tan 'Artificial Neural Networks As Applied in an OECD Market'.
   NOTE: Students will be provided with the handout for the length of the course and will have to return the notes at the end of the course. All information with regards to the handout are to be treated as confidential.
2. Lecture Notes

Recommended Text:

1. Trippi R., and Turban, E., Neural Networks in Finance and Investing 2n. Edition, Irwin, USA, ISBN 1-55738-919-6, 1996.
2. Deboeck, G. J., Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets, ISBN 0-471-31100-6, John Wiley and Sons Inc., USA, 1994.
3. Carew, E., Fast Money 3

Check the INFM 333 web page for online books.

Assessment Details

Important: Students are strongly discouraged from missing lectures as substantial examinable material not covered by the text will be delivered in the lectures.

• Research Project 25%

This project will involve a research paper on a topic computational finance. Students are encouraged to do research on artificial intelligence in an area of their interest and to discuss it with the lecturer before starting. Students are expected to do a fifteen minute presentation on their research towards the end of the semester. Please note late project will not be accepted.

• 1 Close-Book Mid-semester Examination 30%

• 1 Final 2-hour Close-book Examination 35%

• Assignments and Participation 10%

Students will be rewarded for regularly attending sessions, participating in class discussions and completing in-class/lab assignments.

Late homework will not be tolerated

Contact Times

Attendance at formal session times are compulsory.

Course Outline