Using economic and financial information for stock selection

A major inconvenience of the traditional approach in portfolio choice, based upon historical information, is its inability to anticipate sudden changes of price tendencies. Introducing information about future behavior of the assets fundamentals may help to make more appropriate choices. However, the specification and parameterization of a model linking this exogenous information to the asset prices is not straightforward. Classification trees can be used to construct partitions of assets of forecasted similar behavior. We analyze the performance of this approach and apply it to different sectors of the S&P50

Monotone Models for Prediction in Data Mining.

This dissertation studies the incorporation of monotonicity constraints as a type of domain knowledge into a data mining process. Monotonicity constraints are enforced at two stages¿data preparation and data modeling. The main contributions of the research are a novel procedure to test the degree of monotonicity of a real data set, a greedy algorithm to transform non-monotone into monotone data, and extended and novel approaches for building monotone decision models. The results from simulation and real case studies show that enforcing monotonicity can considerably improve knowledge discovery and facilitate the decision-making process for end-users by deriving more accurate, stable and plausible decision models.

Decision trees for monotone price models

In economic decision problems such as credit loan approval or risk analysis, models are required to be monotone with respect to the decision variables involved. Also in hedonic price models it is natural to impose monotonicity constraints on the price rule or function. If a model is obtained by a “unbiased” search through the data, it mostly does not have this property even if the underlying database is monotone. In this paper, we present methods to enforce monotonicity of decision trees for price prediction. Measures for the degree of monotonicity of data are defined and an algorithm is constructed to make non-monotone data sets monotone. It is shown that monotone data truncated with noise can be restored almost to the original data by applying this algorithm. Furthermore, we demonstrate in a case study on house prices that monotone decision trees derived from cleaned data have significantly smaller prediction errors than trees generated using raw data. Copyright Springer-Verlag Berlin/Heidelberg 2004Data mining, monotone decision trees, price models,