Enhanced Indexing Strategy - Primary Risks

In this kind of a strategy, the smaller funds/ individuals try to replicate a relatively fewer number of issues as used in pure bond indexing, without acquiring each issue in the index. Yet, they construct a portfolio by matching primary risk factors without acquiring each issue in the index. However, in such cases, once the portfolio manager tries to overlook more number of issues tracked by the index, the risk factor gets increased for tracking error. The manager tries to maintain major primary issues like duration by leaving the same on index, but tries to take some other factors which he could control actively.

The advantage of following an Enhanced Indexing Strategy is that the same is less costly to implement, and offers a higher return, at the discretion or intelligence of the portfolio manager. Simultaneously, it also offers the leverage of maintaining the primary risk factors determined by the index.

However, the dark side of following this kind of a strategy is that it increases the tracking error possibility, once the manager starts taking them into his hands. Apart from same, the management fee is typically higher, and is worthwhile, only if it offers higher returns.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

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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.