Finding the right investment

Creating and man­aging a mu­tu­al fund port­fo­lio can be quite a task without the right set of tools to com­pare funds. On the one hand, you have volumes of in­form­a­tion from re­search re­ports. On the oth­er hand, you have data on Excel that let you com­pare spe­cific para­met­ers. But few tools of­fer a com­plete view of the port­fo­lio with all rel­ev­ant de­tails across the funds in a single snap­shot.

At Gramener we ap­plied visu­al­isa­tion to a set of equity funds, com­par­ing them on vari­ous para­met­ers.

The lay­out has been kept sim­ple, but the in­form­a­tion dens­ity is rather high. Both are on pur­pose, This, clearly, is quite a com­plex visu­al­isa­tion and mer­its closer ob­ser­va­tion.

The first block shows the name of the fund, the cur­rent net as­set value (NAV) and the over­all trend over the last 30 days. The black line shows the daily trend while the light blue line be­hind it shows the move­ment of the bench­mark – the Sensex in this case.

The next block shows daily re­turns as a ho­ri­zon graph. The re­turns on each day over the last 30 days (from left to right) are shown in red (for neg­at­ive re­turns) or green (for pos­it­ive re­turns). The in­tens­ity of the col­our in­dic­ates the mag­nitude of the re­turn. This makes it easy to com­pare re­turns for a given peri­od across funds. For in­stance, it is clear that the be­gin­ning of the month was a bad time (since the left-end is red) for al­most every equity fund.

The re­turns are also plot­ted again­st the re­turns of the Sensex as a jit­ter plot. The red dots on the left are days when the re­turns fell be­low the Sensex. Far left in­dic­ates very low re­turns, far right in­dic­ates very high re­turns. A glance shows that the second and third funds have re­turns that are spread out, but the oth­ers are fairly closely clustered around the Sensex’s re­turns.

The next block shows the av­er­age re­turn over longer peri­ods – a week, month and quarter. The col­our in­dic­ates how high or low the re­turns are with re­spect to oth­er funds. Red in­dic­ates the lowest re­turn, green in­dic­ates the highest re­turn. Here, the second and third fund have had a fairly high re­turn com­pared to oth­ers dur­ing the last week.

Some of the more com­plex para­met­ers that in­vestors look at are Jensen’s al­pha, beta, sig­ma and the in­form­a­tion ra­tio. These too are in­cluded in this visu­al­isa­tion, again show­ing their re­l­at­ive per­form­ance with re­spect to the oth­er funds. The only fund that does not per­form poorly on these para­met­ers here is the third fund.

But one oth­er para­met­ers that would be of in­terest is, how good is the fund’s re­turn com­pared to oth­er funds. Irrespective of the re­turn with re­spect to the Sensex, a key para­met­er is the re­turn with re­spect to oth­er equity funds if these funds are the in­vest­ments of con­cern to us. We plot­ted the weekly re­turn as a per­cent­ile rank. If the fund is the be­st per­form­ing fund on a given day, we plot a dot at the right end. If it is the wor­st per­form­ing fund, we plot a dot on the left end. The mix of dots tells us how the fund has per­formed re­l­at­ively over the last month.

The last column shows the av­er­age of the weekly per­cent­ile rank. This is a good in­dic­a­tion of the fund’s re­l­at­ive per­form­ance av­er­aged over time. The first fund, for in­stance, has on av­er­age out­per­formed 63.7% of the oth­er funds. The next has out­per­formed 61.6% of the oth­er funds. And so on. The table is sor­ted based on this column.

The aim of this visu­al­isa­tion is to present, in a very con­densed and information-dense fash­ion, all the in­form­a­tion an in­vestor re­quires to make a de­cision. As you can see, an ex­plan­a­tion of the visu­al­isa­tion takes more space than the visu­al­isa­tion it­self! Yet with a bit of un­der­stand­ing, get­ting in­sights out of this plot can take just a few seconds. A far cry from hav­ing to read 50 equity re­search re­ports!

When to invest

Sometimes, tim­ing is everything in in­vest­ments.

Last year, The New York Times pub­lished a piece titled In Investing, It’s When You Start And When You Finish. This showed the sig­ni­fic­ant im­pact of tim­ing in in­vest­ment de­cisions.

At Gramener, we ap­plied the same visu­al­isa­tion to a few Indian stocks over the last 5 years.

Here’s what it looks like for ICICI’s stock.

If you in­ves­ted in ICICI stock in Jan 2007, the first row of boxes show the kind of re­turns you would have seen.

The col­ours in­dic­ate the de­gree of profit or loss. Red for losses, green for profits, and yel­low for neut­ral val­ues. Selling in March 2007 would have made sig­ni­fic­ant losses. Selling in Jan 2008, one year later, would have given you a good profit. And so on.

The same is ex­ten­ded to in­vest­ments made in oth­er months.

The black boxes show a hold­ing pat­tern of 1 year, 2 years, etc. You can get a sense of what kind of re­turns you would make with a strategy of hold­ing for 1 year, 2 years, and so on.

Here are sim­il­ar pic­tures for Infosys stock and SBI stock.

At Gramener, we took a look at a num­ber of such stocks and their per­form­ance over the last five years. A in­ter­act­ive app show­cas­ing sample of those is avail­able at

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?