Portfolio Management Process

The Portfolio Perspective

In studying the mean-variance analytical foundation created by Harry Markowitz, candidates have come to see how Modern Portfolio Theory emphasizes whole portfolio over its individual pieces.

The goal of portfolio management is to build a portfolio of assets with an appropriate risk/return profile for the individual investor (who could be a person or an entity, such as a foundation).

Steps in the Portfolio Management Process

The following list represents the steps in the portfolio management process.

  1. Planning
  • Identify investor Objectives and Constraints.
  • Formulate the Investor Policy Statement (IPS).
  • Forecast risk and returns for asset classes to derive capital market expectations.
  • Use IPS and capital market expectations to create the investor's strategic asset allocation (SAA).
  1. Execution
  • Select securities for portfolio inclusion based on the Planning step.
  • Purchase the selected securities.
  • Sometimes portfolio managers are temporarily allowed to deviate from the strategic asset allocation based insights related to perceived asset mispricings. These temporary deviations are called Tactical Asset Allocations (TAA).
  1. Feedback
  • Analyze returns through performance measurement.
  • Determine the sources of returns through performance attribution.
  • Use the results of the portfolio measurement and performance attribution to make a performance appraisal of the portfolio manager. This appraisal determines if the portfolio manager should be retained or fired.
  • Over time the portfolio needs to monitor the investor's changes and the capital market expectation changes to rebalance the portfolio as appropriate.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.