Indy Telecom & Industrial Media

From Instapundit:

CONSTANT-DOLLAR DOW: Today’s 10,000 is really 7,537. Or, in another metric: “It cost about 30 ounces to buy the 10,000 Dow last time. Now it costs less than 10.”

And here’s an interesting Bloomberg screen cap of the Dow over CPI –

(Link du jour Prasenjeet)

I’ve been meaning to post this for a while now.

Some time ago, the WSJ had an excellent interactive graphic that chronicled the timeline of credit crisis by stacking the 6 key financial indicators through these two years.

The key indicators were DJIA, Treasury Yields, Libor, Commercial Paper Yields, CDS Spreads & Mortgage Backed-Securities Spreads.

An excellent piece by Robert J. Moore of RJ Metrics called Twitter Data Analysis: An Investor’s Perspective that goes into how a potential investor would perform analytics on a platform like Twitter before investing.

With the use of some very basic statistics that’s easily available through Twitter’s API, he performs a very interesting cohort analysis of user activity and engagement activity.

Fascinating stuff. A few thoughts from my end –

• How could this be monetized based on user preferences, demographics (those of users & followers) etc?
• What will be needed to encouraged people to tweet more?  I’ve been on Twitter for a long time, but I’ve less than 10 tweets. Surprisingly, I’ve 45 followers.
• What could be done about bots? More importantly, how could user credibility be built (ratings, karma, community moderation etc)?
• On the same note, how about differentiating corporate users? E.g. Google’s official Twitter channel vs. that of their fans (and detractors, of course).

So, turns out that the US trade deficit went down to $30.7 billion in August 2009 from $31.9 billion in July 2009.

What is particularly interesting is the strong correlation between the imports and the exports.

The worst percentage loss were for Northwest, United & American. The ones that did well with minimal loss were JetBlue, SkyWest & Southwest.

The data was sourced from the Airline Data & Statistics section of the Bureau of Transportation Services.

Here’s a slightly better attempt at creating a heat map, this time with the Google Gadgets API.

The heat map below is that of the Average Gross Yield (Nominal) of MFI institutions from various countries in the world. The yield is in percentage (i.e. .06=6% and .85=85%).

Data was sourced from MIX Market.

Here’s a fun visualization created using Google’s Visualization API.

The data was queried from International Monetary Fund’s World Economic Outlook Database, October 2009.

Lately, I’ve been playing around with some fun MFI data from around the world. In particular, I was curious to see what the Gross Loan Portfolio across various microfinance institutions were, and how they aggregated within each country of operation.

Luckily, I was able to source some rather excellent data from MIX Market.

Given this (and the fact that I’ve been playing around with Google’s Chart API for a while), I went ahead and created a little heat map of the global MFI rankings by Gross Loan Portfolio:

If you look at the above map, those countries with higher portfolios are in a darker shade of green and those with lower portfolios are in a lighter shade of green.

The list of the top all countries with more than 1% of the global MFI Gross Loan Portfolio is also listed below.