Financial Applications of Relational Data Mining

Conclusion

Relational data mining based on inductive logic programming, first-order logic and probabilsitic estimates has several important advantages known from theoretical viewpoint Computational experiments presented in this chapter have shown these advantages practically for real financial data.

Relational data mining methods and MMDR method, in particular, are able to discover useful regularities in financial time series for stock market prediction. In the time frames of the current study we obtained positive results using separately, SP500C and history of target itself for target forecast. The best of these regularities had shown about 75 % of correct forecasts on test data (1995–1996). The target variable was predicted using separately SP500 (close) and the target variable’s own history. Comparison of performance with other methods is presented in the next chapter.