Modeling with pattern recognition decision rules by Ted C. Earle
Empirical decision rules can help you recognize patterns in price actions. Earle provides detailed buy and sell rules along with examples using data on gold price changes over time.
Most of the interest in using mathematical models in finance and economics involves models based
on hypothetical causal relationships described by quantitative mathematical functions. When these
models work they may reflect substantial understanding of the system being simulated and provide a
wealth of information about the internal relationships of the various factors included in the model. Many
of these models have mathematical characteristics which allow them to be solved for optimal results. In
the areas of finance and economics, however, the quality of data is often so poor and the complexities of
the relationships so difficult to express that such models frequently seem to be unreliable
oversimplifications of the real world.
Another scientific tradition, empirical modeling, should then be considered for wider utilization in the
areas of finance and economics. Empirical models may be of value when the more formal theoretical
models are deemed inadequate because of high uncertainties or an inability to adequately express the
complexities. Empirical models are developed by analyzing experimental data (in the case of finance and
economics, historical data).