Education
1958 Sc.D., Goettingen, Germany

Honors and Awards
William Hume-Rothery Award, The Minerals, Metals and Materials Society, American Institute of Mining, Metallurgical, and Petroleum Engineers, 1990; Klein University Lecturer, Northeastern University, 1990.

Research Interests
Following a previous research career in materials chemistry, Prof. Giessen and his graduate research group are currently engaged in an interdisciplinary program, joint with the Economics Department, on the analysis of usage, supply and pricing of chemicals, materials such as copper, silver and gold and other commodities and futures using chemometric techniques such as pattern recognition (PR) and other advanced computer methods.

 

This research program is underpinned by empirical observations of non-random event sequences in markets, in contradistinction to random walk behavior expected for efficient markets.  Beyond a systematic search for such event patterns, the group's research includes model building to understand the observed regularities in a physical/dynamical way.  Our model considers that markets represent the collective behavior of the market participants which is, to a certain degree, repetitive and thus amenable to analysis in the same sense as the physical phenomena ordinarily studied by chemists.

The task of our analysts is to use PR to find patterns in past market data.  This search has proved very fruitful.  Group members have discovered and characterized market periodities and their overtones by data smoothing, frequency analysis, correlation studies and PR using k-nearest-neighbor (KNN) and area discriminant analysis methods for the commodities and futures listed above.

Selected Publications

K. Xu, B.C. Giessen, J. Chen, J. Yao, Z. Zhao, T. Yu and K. Dadkhah, "Short Time Segment Price Forecasts Using Spline Fit Interactions Which Simulate Cycle Analysis", in: "Practical Fruits of Econophysics, Business Models in the 21st Century - Risk Management and Expectations for Econophysics", Proc. 3rd Nikkei Econophysics Symp., H. Takayasu, ed., Springer, Tokyo (2006) p. 111.

B. C. Giessen, Z. Zhao, J. Chen, J. Yao, K. Xu, T. Yu, and K. Dadkhah, "Successful Price Cycle Forecasts in S&P Futures Using "TFX", a Family of Pattern Recognition Algorithms Based on the KNN Method", ibid., p. 116.

J. Yao, J. Chen, K. Xu, Z. Zhao, T. Yu, B. C. Giessen, and K. Dadkhah, "Market Cycle Turning Point Forecasts by an Interactively Learning Algorithm as a Trading Tool for S&P Futures", ibid., p. 131.

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