Visualising securities correlation

If you were won­der­ing how the se­cur­it­ies in the world move again­st each oth­er, the pic­ture be­low is the an­swer.


This pic­ture shows the cor­rel­a­tion vari­ous cur­ren­cies, in­dices and com­mod­ity prices by link­ing to­geth­er three power­ful types of visu­al­isa­tions.


The first is a col­oured cor­rel­a­tion mat­rix. In this pic­ture, we have three se­cur­it­ies: the British Pound (GBP), Gold Price (XAU) and the Dow Jones Index (^DJI). The price of GBP and Gold tend to move slightly to­geth­er, and have a cor­rel­a­tion of 0.36 (36%). So the cell that’s between GBP and XAU is marked with 36. Similarly, the cell that’s between XAU and ^DJI has a –64 be­cause Gold and the Dow Jones in­dex are slightly neg­at­ively cor­rel­ated.

The col­our cod­ing is based on the cor­rel­a­tion. Red is -1, Green is +1 and Yellow is 0.

securities-correlation-scatterplotThe second is a scat­ter­plot mat­rix. The cells that mir­ror the cor­rel­a­tions have a series of dots. Each dot rep­res­ents the price on a par­tic­u­lar day.

For ex­ample, com­pare Gold and the Dow Jones. It isn’t a straight-forward neg­at­ive cor­rel­a­tion. In fact, it al­most looks like there were two peri­ods: one in which gold was high when the Dow Jones was low, and vice ver­sa. But with­in those peri­ods, there ap­pears to have been a mild pos­it­ive cor­rel­a­tion.


The third is the hier­arch­ic­al cluster. The se­cur­it­ies is grouped in­to sim­il­ar ones based on their cor­rel­a­tion. For ex­ample, GBP and Silver (XAG) and reas­on­ably close to each oth­er, and form one group. This group is most closely re­lated to the Euro (EUR), and the three of them are closest to the Australian Dollar.

Arranging the se­cur­it­ies by the hier­archy makes it easy to spot groups of se­cur­it­ies that tend to move to­geth­er.

For ex­ample, in the ori­gin­al visu­al­isa­tion, there ap­pear to be a set of lo­gic­al blocks


At the centre, four se­cur­it­ies – the Pakistani Rupee (PKR), the Sensex (^BSES), the FTSE (^FTSE) and the S&P (^GSPC) – tend to move to­geth­er, with each oth­er; but move in the op­pos­ite dir­ec­tion to the next group of se­cur­it­ies – the Singapore Dollar (SGD), the Japanese Yen (JPY), Gold (XAU), the Swiss Franc (CHF) and the Chinese Yuan (CNY).

Similarly, the Swedish Krona, Canadian Dollar, Indian Rupee, Hong Kong Dollar and Mexican Peso form yet an­other group that moves to­geth­er, but in the op­pos­ite dir­ec­tion from the strong Asian cur­ren­cies in the block above.

We at Gramener have named this visu­al­isa­tion a cluster­plot. It’s a power­ful tech­nique when ap­plied to time series of mul­tiple (typ­ic­ally 5 – 50) vari­ables.

Here are some cases you might con­sider us­ing them:

  • Group your products based on con­sumer be­ha­vi­our. Which products tend to sell to­geth­er? Which ones can­ni­bal­ise the sale of the oth­er? Is there a way of ra­tion­al­ising the pro­duct base to re­duce com­plex­ity – without los­ing cus­tom­ers?
  • Group re­tail­ers based on sales. Which re­tail­ers tend to can­ni­bal­ise the sales across each oth­er? Which ones com­ple­ment each oth­er? Where would you need to ra­tion­al­ise to avoid du­plic­a­tion or over­lap?
  • Analyse pro­cess qual­ity drivers. For ex­ample, if tem­per­at­ure, pres­sure and sa­lin­ity af­fect your pro­duct qual­ity, what im­pact will in­creas­ing one para­met­er have on the oth­er?

Gramener JV with Teknoturf

Gramener IT Services a joint ven­ture between Gramener and Teknoturf. We bring to­geth­er Gramener’s in­dustry ex­per­i­ence of de­liv­er­ing large and com­plex en­gage­ments with Teknoturf’s deep tech­nic­al skills to provide the most rel­ev­ant and cost ef­fect­ive solu­tions for our cus­tom­ers.