Application of Fundamental Analysis and Computational Intelligence in Dry Cargo Freight Market
Athanasios Tsakonas, Emmanouil Nikolaidis, Georgios D. Dounias · Bournemouth University Research Online (Bournemouth University) · 2003
ABSTRACT: The aim of this work is to explore and estimate the short-term prediction efficiency, using two alternative approaches: the fundamental analysis on factors affecting the Baltic Panamax Index (BPI) evolution, and a computational intelligence approach based on neuro-fuzzy technique. In order to accomplish this task, we first analyze the basic routes that compose the Baltic Panamax Index. We estimate the supply and demand on the BPI standard routes, the GDP annual growth rate of main importing countries, and the other fundamental factors and thus we conclude for the up trend or downtrend in a short time basis. Next, we develop a computer based system, consisting of a neural network and a wavelet analysis and filtering system, using as system’s input, BPI time series data for the past three years. The configuration of such system, involves the application of wavelets filtering as a system preprocessor, in order to identify the underlying trend of a BPI signal. Then, applying wavelet analysis enables us to further configure a neural network, which is finally used as the anticipation engine, of this system. Results showed that both techniques are capable to produce a short-term decision support in the maritime or financial sector. Nevertheless, more research on both domains might improve the anticipation power in either the fundamental or the computational intelligence approach. As a concluding remark, we suggest that the whole procedure may be a valuable tool in order to identify the capers freight market, or other maritime time-series data, as well.