Portfolio Analytics

Risk and return…

In this practical we explore portfolio analytics, specifically calculating downside risk and performance measures.

• Also note the exercises in calculating weighted portfolio risk contribution.

• Practical 2 (Html)

• Practical 2 Bonus (Html)

DIY Section

See the following description, try it yourself and then go through my practical as a guideline. There’s some nice tricks in there too.

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You’ve been asked to comment on whether active managers in the local ASISA General Equity universe are taking advantage of the opportunities available to them.

This is a difficult question, as one cannot simply directly compare their performance vs that of an index alternative. This follows as we need to assess what the active manager opportunity set has been over time - i.e. ask the question what the alpha opportunities were.

In order to answer this question, you recall the definition of dispersion to measure the average weighted difference in returns between constituents and a parent index.

√∑Wi×(Ri−ˉR))2

Where Ri is each individual stock’s return, and ˉR the relevant benchmark return.

Naturally, if dispersion is low (high) that means the alpha opportunity (ability to differentiate portfolios) is low (high).

You are tasked to calculate dispersion on a rolling monthly basis (using daily constituent returns series) - and calculate how dispersion has changed through time.

Then assess whether active managers have taken advantage of the opportunity sets available to them over time.

Data to Use

library(tidyverse)
# Constituents:

J200 <- fmxdat::J200

# Index Returns
Indexes <- fmxdat::LCL_Index_Returns %>% filter(Tickers == "J200")

Active_Funds <- fmxdat::ASISA %>% filter(!is.na(Returns))

Solution:

• Practical 2 Bonus: Dispersion (Html)