MantaCore Methodology Overview

Introduction

MantaCore is a comprehensive financial analysis and portfolio management system designed to provide robust tools for data cleaning, risk modeling, portfolio optimization, and performance attribution. This document provides a high-level overview of the methodologies implemented in the system, serving as an introduction to the more detailed documentation available for each component.

For detailed information on specific methodologies, please refer to the dedicated documentation for each component.

Key Methodologies

Data Cleaning

The data cleaning process ensures high-quality, consistent data for financial analysis by:

• Removing outliers and handling missing values
• Aligning time series data across different instruments
• Rescaling bond prices according to time-to-maturity
• Providing different strategies for handling data anomalies

For more details, see Data Cleaning Process for Linear Instruments.

Risk Factor Modeling

The risk factor engine analyzes the underlying factors that drive financial instrument returns:

• Linear risk factor models for standard instruments
• Regression-based analysis to identify factor exposures
• Risk decomposition to understand sources of risk
• Factor-based scenario analysis

For more details, see Risk Factor Modeling Methodology.

Portfolio Construction / Optimization

The optimization engine constructs optimal portfolios based on:

• Mean-CVaR optimization
• Risk-based allocation methods
• Constraint-based optimization with various objective functions
• Efficient frontier generation for risk-return analysis

For more details, see Portfolio Construction

Performance Attribution

The attribution system analyzes portfolio performance:

• Brinson-Fachler attribution based on a holdings-based attribution type
• Hierarchical performance attribution based on a returns-based attribution type
• Risk & performance decomposition across different dimensions

For more details, please see Performance Attribution