Structures Related to the Emplacement of Shallow-Level Intrusions

A systematic view of the vast nomenclature used to describe the structures of shallow-level intrusions is presented here. Structures are organised in four main groups, according to logical breaks in the timing of magma emplacement, independent of the scales of features: (1) Intrusion-related structures, formed as the magma is making space and then develops into its intrusion shape; (2) Magmatic flow-related structures, developed as magma moves with suspended crystals that are free to rotate; (3) Solid-state, flow-related structures that formed in portions of the intrusions affected by continuing flow of nearby magma, therefore considered to have a syn-magmatic, non-tectonic origin; (4) Thermal and fragmental structures, related to creation of space and impact on host materials. This scheme appears as a rational organisation, helpful in describing and interpreting the large variety of structures observed in shallow-level intrusions

Classification and regression trees and their use in financial modeling

Classification and regression trees (CART) are nonparametric and nonlinear modeling techniques that do not rely upon the many stringent assumptions required by classical parametric models. Despite the fact that researchers in many fields have regularly found trees to be an attractive way to express underlying relationships, they are relatively unfamiliar to financial modelers where the historical focus of financial modeling has been on parametric regression. Although the linear type of regression analysis is convenient and sometimes intuitive, it may not fully capture the complexity of financial markets. As the quantity and variety of financial information available to data exploration have increased over time, there has been a commensurate need for a more robust and versatile process to analyze these data. CART offers a valuable alternative to traditional methods for modeling financial data

A hybrid approach to combining CART and logistic regression for stock ranking

The benefits of applying tree-based methods to the purpose of modelling financial assets as opposed to linear factor analysis are increasingly being understood by market practitioners. Tree-based models such as CART (classification and regression trees) are particularly well suited to analysing stock market data which is noisy and often contains non-linear relationships and high-order interactions. CART was originally developed in the 1980s by medical researchers disheartened by the stringent assumptions applied by traditional regression analysis (Brieman et al. [1984]). In the intervening years, CART has been successfully applied to many areas of finance such as the classification of financial distress of firms (see Frydman, Altman and Kao [1985]), asset allocation (see Sorensen, Mezrich and Miller [1996]), equity style timing (see Kao and Shumaker [1999]) and stock selection (see Sorensen, Miller and Ooi [2000])..

The benefits of tree-based models for stock selection

The identification of the primary drivers of stock returns has been of great interest to both financial practitioners and academics alike for many decades. Influenced by classical financial theories such as the CAPM (Sharp, 1964; Lintner, 1965) and APT (Ross, 1976), a linear relationship is conventionally assumed between company characteristics as derived from their financial accounts and forward returns. Whilst this assumption may be a fair approximation to the underlying structural relationship, it is often adopted for the purpose of convenience. It is actually quite rare that the assumptions of distributional normality and a linear relationship are explicitly assessed in advance even though this information would help to inform the appropriate choice of modelling technique. Non-linear models have nevertheless been applied successfully to the task of stock selection in the past (Sorensen et al, 2000). However, their take-up by the investment community has been limited despite the fact that researchers in other fields have found them to be a useful way to express knowledge and aid decision-making..