QuantMastery.com Quantitative Finance Newsletter
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The set of computing techniques collectively called data mining methods can be applied to stock market analysis, predictions, and other financial applications. In this article we will discuss outline these methods for financial modeling and present a survey of current capabilities of these methods in financial analysis. There are two main categories: adaptive linear and non-linear “mining” of financial data. Example of such methods as: ARIMA, neural networks, decision trees, Markov chains, hybrid knowledge-based neural networks, and hybrid relational methods.
The main application are related to examining financial time series, modeling and forecasting these financial time series.Our main purpose is to provide much needed guidance for applying new predictive and decision-enhancing hybrid methods to financial tasks such as capital-market investments, trading, banking services, and many others. The very complex and challenging problem of forecasting financial time series requires specific methods of data mining. We discuss these requirements and show the relations between problem requirements and the capabilities of different methods. Relational data mining as a hybrid learning method combines the strength of inductive logic programming (ILP) and probabilistic inference to meet this challenge.