marginally subversive

on #analytics: data availability is no longer the problem

Data availability is no longer the problem.  Accessibility is no longer the issue.  The cost of acquiring data has gone down dramatically.

Neither is the technology behind data collation the problem.  There are a myriad of options that one can collect, store, discover, collate, systematize, analyze, and use data.

The real problem is people's apathy and/or antipathy to data and data analyses.

It's all about changing the mindsets of business managers - specifically of marketing managers who think that their jobs encompass merely looking after advertising and media technologies.

David Campbell, as interviewed by Alex Howard (at Oreilly Radar) has this advice to clients about using their data:

From the data side, we've lived in what we'd call a world of scarcity. We thought that data was expensive to store, so we had to get rid of it as soon as possible. You don't want it unless you have a good use for it. Now we think about data from a perspective of abundance.

Part of the challenge, 10 or 15 years years ago, was where do I go get the data? Where do I tap in? But in today's world, everything is so interconnected. It's just a matter of teeing into it. The phrase I've used instead of big data is "ambient data." It's just out there and available.

The recommendation would be to stop and think about the latent value in all that data that's there to be collected and that's fairly easy to store now. That's the challenge and the opportunity for all of us.

(Emphases mine; from http://radar.oreilly.com/2011/12/david-campbell.html)

Here's the video of the interview:

 

 

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The Russian Digital Landscape: A Snapshot

I came across this informative presentation on what the digital landscape in Russia could look like.  This presentation was done by Andrey Anischenko of Grape.

Of note are the following factoids:

1. Russia has the fifth largest population of Netizens after China, the US, Japan, and Germany

2. 62% of these are from one of the most elusive demographic group, the 15-34s

3. Social networks in Russia are HUGE: they spend 9.8hours on social networking sites versus the global average of 4.5hours (I am deducing per month - but it seems a bit low)

4. And nope, it ain't Facebook that dominates: the two two slots are dominated by two home-grown social media services, "B" (www.vkontakte.ru) and (www.odnoklassniki.ru).  Twitter is not there yet - but it's popular with the geekfluentials and celebrities - and may therefore have potential.

5. "Augmented~social" is promising.

Here's the presentation.

(Thanks, Andrey Anischenko of Grape!)

 

Russia digital by Andrei Anischenko

View more presentations from GRAPE digital agency

what is "REAL" econometrics-based analytics?

A number of my friends have told me that the sexiest job in the world soon would be that of a statistician. 

I am not a statistician - I am a researcher who happened to believe that statistics is a tool that could be used to extract information and uncover insights from phenomena in the world.  I also happen to be interested in all things "numeratic": linear algebra, calculus, ordinary differential equations, operations research, queuing theory....

I have used statistics since day1 in my first job as a media strategist at Basic Ad, working on accounts such as McDonald's which demanded that their media plans be as accurate as possible (i.e., gotta use linear regression to predict with certainty the ratings) and which encouraged us to assess the relationship between their sales and advertising promotions.

Since then, I have not looked back.

A few years ago, I got introduced to the theory of econometrics - how statistical principles are used to explain economic phenomena, either in the macro- or microeconomic setting.  Then there was the growing interest in the marketing field towards using econometrics to justify media budgets and laydowns, as well as channel selections and all that.

It took not that long enough to realize that well, anybody who has the software and the patience to go through some wonderful things with numbers using matrices and spreadsheets, can do any model.  Heck, I even fell into the same trap - creating models that didn't make any sense.

That is when I realized that econometrics is all about - and perhaps, what statistical modeling on which econometrics is based: 

Creating, formulating, and coming up with equations relating two or three or more variables can be done by almost anyone who is interested.

It is like anybody who has the capacity to understand the saying (questionable, it may be) that "the past could be a good indicator of the future" and has the mathematical interest and bandwidth to create models can.

Take this case for example:

I am fascinated by the movements of the stock markets.  (And I am not an expert in finance nor in equity trading.)  Particularly, I was interested in the movement of the stock market.

I hypothesized - urged on by the current global economic crisis - that stock markets must be related one way or another:  That the Straits Times Index (STI) of Singapore should be affected by movements in the NASDAQ, the S&500, FTSE, VIX, and others. 

My hypothesis was that investors in the STI do take an interest in the overnight performance of these indicators: NASDAQ and the S&P500 were indicators for the US, FTSE is for the Euro markets, and VIX is an indicator that somehow reflects the "risk-attitudes/risk-expectations" of investors.

I therefore hypothesized that when the NASDAQ goes up and the S&P500 go up, and so do the FTSE - the STI would also go up.  But since investors are risk-averse, I also thought that the higher the VIX (and therefore the higher the expectations of risks and volatility in the future), it should impact the STI negatively. 

With this very little amount of information and hypothesis, I went on and crafted my models.

(Hold you criticisms of multicollinearity and autocorrelations...  For now, I am loosening the assumptions for my simple experiment.)

So... after several attempts, I got the following first model:

The model used the abovementioned weekly variables - FTSE, NASDAQ, S&P500, VIX - as predictors and ST as the variable.  (I also created an index - a NASDAQ/S&P500 index - since NASDAQ was more tech-orientated and SIngapore is more closely related to the tech-orientated indutries.)

Using data from 2000, the model (in red) seemed to track the data fairly well.  It was had several underpredictions at first, which attributed to the post-tech-bubble and 2001-2002 uncertainties.  The goodness-of-fit indicators were acceptable.  Adjusted R-squared is 0.721 - not bad!  The residuals were also fairly good.

(Again, hold off discussions of autocorrelations and multicollinearity.)

I figured, "Hmm, there must be a better way - could there be a better way?"  Can I increase my R-squared and other GIFs?

So after several (perhaps 25-30) iterations, I achieved the following:

 

Not too shabby, I said to myself - indeed, the GIF's increased.  Amongst them, an R-squared of 0.976 - for the movement of the STI.

(To the technicaly savvy: This was arrived using the same variables, but this time using weighted least squares.)

Obviously, I was giddy - I may just have modeled the movements of the STI in the past 9 years with simple mathematics and good old Microsoft Excel (and an add-in called XLSTAT).  I have derived the respective weights and parameters of each of the variables - and established their significance at an alpha-level of 0.01! (In some cases, some variables are actually less than 0.01!)

Then the realization crept in:

But modeling - econometrics, statistical modeling - is beyond creating models and equations.  It is creating explanations of why things have moved and acted that way in the past in order to clearly see the future.

And that was when I got into trouble.

 

So... the above are the most significant positive predictors.  The "FTSE x SP500" taken together explains a significant amount of volatility - not my hypothesis realy, but close enough.  But surprisingly, VIX also had a significant positive effect.  Not only that: If we multipled NASDAQ with VIX, there is an almost equally strong effect on the STI.  Squaring the NASDAQ and dividing it by the S&P500 also indicated that, well, it explained the changes in the STI - at an alpha level of less than 0.0001!

It became interesting when I started looking at what I called the "STI dampeners":

S&P500 and FYSE on their own were dampeners - and could shave off values in the STI.  Multipliying VIX with S&P500 also indicated a significant decline.  The most perplexing one was the last variable: VIX (risk-perceptions/expectations) x NASDAQ cumulatively divided by the S&P500 was the most significant dampener!

And again, modeling - econometrics, statistical modeling - is beyond creating models and equations.  It is creating explanations of why things have moved and acted that way in the past in order to clearly see the future.

And that was when I got into trouble: why?

Why would these relationships exist?  Why would the FTSE and the S&P500 be dampening the STI?  Don't we get cues from these two markets?  NASDAQ - on its own - was not a significant predictor, with t-statistic that is way below the acceptable range.

One could argue that it is the complexity thinking of investors - but still, why would VIX not be a dampener?  And it is only when one multiplied VIX with the S&P500 that I could accept that VIX is a dampener for the STI!

Econometrics is about why's - and not just the numbers and formulae.

I am no finance expert - I have a very basic understanding of the equity markets.  I have some knowledge of finance theory.  But I just cannot explain these numbers - and the formulae that I have derived.

If you ask me what the numbers and the formulas meant, I wouldn't be able to explain.  I could only say for certain that the suggested model from the equations tracked the real data well.  I could recite - in a non-monotonous voice (because I try to be not too boring when I present data) - the effects of each in terms of numbers.  And perhaps, even come up with an interesting visualization of the impacts of each of the factors and their interactions.

But ask me why VIX is positive on its own but now when it is multiplied with another variable - and I'd be lost.

Because econometrics - and statistical - modeling is all about uncovering the stories behind the numbers and explaining them.

Sure, because I now have the formulae for predicting the future of the STI given varying levels of the FTSE, the NASDAQ, the S&P500, and the VIX indices (predicting them altogether is another story), a software can be created, risk-tables associated with varying levels of the predictor indices can be programmed, and decisions can be made.  One can even make 'self-correcting', dynamically-adapting models from these models.

But as to why these formulae hold, I have no clue.

And that is where I would fail against the tenets of econometrics and statistical modeling:  The explanatory power, the stories, the why's and the how's and the how-come's.

That is where people's brains - and dare I say, people like me - come in.  Because after all the formulas have been established and tested, after all the parameters have been derived, and their individual and aggregate/combined effects on the predicted variable have been uncovered, the question then becomes "Why?"

The bottom-line:

• Whether we like it or not, theory and hypotheses before even the modeling starts is critical.
  
• Whether we like it or not, computers - for now - cannot take the place of creating explanations and stories.  Maybe, there will be a time when stories can be crafted by computers.  But for now, brains still matter.
  
• We need both people with technical and theoretical statistical and econometric background - but in the field of finance, of consumer behavior, of marketing... we also need experts who can ground these formulae into the real business world.  Into answers for real business questions.

To paraphrase Joyce Kilmer:

Models are made by fools like you and me
But only real integrative minds can make it gounded on reality.

---------------------------------------------------------------

PS. I got the data from Yahoo! Finance (http://finance.yahoo.com) which I think is one of the more friendler consumer-orientated finance sites on the web for all the indices from January 2000 till August 2009. 

In the first model, I used OLS, for the second model I used Weighted Least Squares with the last observations weighted 10x more than the older data. 

The residuals, by the way, for the first model are slightly heteroscedastic.  The second one fared better.  Both models, however, fail the Durbin-Watson Tests, which means they are autocorrelated at order 1.  The models also fail the more general Breusch-Godfrey Test, which means there are higher AC-orders.

My suggestion: use time-series if you are really interested.  ARIMA would do you good - and GARCH modeling.

Uncovering the Drivers of the Singapore Media Industry's Growth

Not a lot seems to have been written about the effect of the slowdown of the economy of Singapore on the media industry and its allied professions.  (Either that or I have been quite late with the news.)

In any case, I have decided to take matters into my own hands and looked at the trends in the media marketplace.  What I did is looked at the past four years in terms of advertising spends for those which have been monitored by Nielsen Media Research.

Here is what I saw:

Looking at the above trends, we a generally upward, linear trend in the way advertisers are investing in the media marketplace - though several things caught my attention:

1. There appears to be some volatility in the data since 2005.  There is some seasonality - particularly in the middle of the year during the summer holidays and towards the last quarter, we see a general uptrend. 
   
   On the opposite trend, we see that post-Chinese New Year, we see that investments in the media marketplace declined dramatically.  This post-New Year could very well a "recovery phase" after having concentrated moneys in the fourth-quarter and the weeks leading to the festive season.
2. As soon as we entered 2009, we began to see a deviation from this 'average' upward trendline. 
   
   If you look at the last data point, you will see that there is a significant deviation from the linear expectation for January and February 2009.

This led me to the question:  So what is causing these deviations?

The deviations for the first two months of 2009 - and perhaps, even the second half of 2008 - could very well be explained by the onset of the financial crisis and the recession.  But then again, the inquisitive - and quantitative-orientated - mind asked: "Can that be quantified?  Can the effects of the global and local slowdown be quantified?"

Together with some members of the UM Singapore team, we looked at possible explanations.  Using good old Microsoft(R) Excel, we came up with several models incorporating different variables that could explain the variations of the marketplace in the market.

After reviewing more than 10'000 combinations of economic indicators - such as GDP growth, seasonality, consumer price index (which we assumed to be an indication of demand and supply of basic commodities), retail sales and catering indices, and others from SingStat, arrived at a model that was 'good enough' and has explanatory power.

The model above compares the actual (in blue) and the modelled values.  The adjusted R-squared - an indication of how much variance could our model explain - amounted to almost 90% (0.889, to be exact).  In addition to this high adjusted R-squared, other goodness-of-fit indicators were also significant. 

(Visually, it was able to explain the significant decline in January and February 2009 - which is the most problematic data point.  There was one outlier that we had to sacrifice: June 2007.  It appears that June 2007 saw a significant increase that our model couldn't explain.)

As with all models, we wanted to go beyond pure fits.  We wanted explanations.  And we broke down the effects of each of the different components of the model.

I cannot divulge the entire model and the equation, but here is a snapshot of the model's breakdowns:

Several things to note from the analysis of the past four-years' advertising media investments in Singapore:

1. There is a general uptrend - as I have noted above.  We call this the innate momentum of the marketplace.  This innate momentum is significantly strong to drive the media industry upwards in the future.  Our tests indicate that even in the rest of 2009, this innate momentum could potentially be the base on which the market will build on.  Whilst it is on an uptrend, it explains only about 40% of the total movements in the marketplace's investments in traditional meda.

2. There is seasonality in the marketplace with each month contributing significantly.  In fact, if you looked at the last two months in the data set - January-February 2009 - it was the seasonality factor that held the fort together with the innate momentum that we have uncovered.

2a.Special events (which would not be a secret to those with keen eyes) would drive some uplifts - in addition to seasonality. 

3. There are two economic factors that we have uncovered that affect the media landscape's investments - one (the green one) accounted for almost 40% as well of the movements in the investments in the past five years.  The green economic factor was quite strong that it had 'spillover' effects up to two months.

The other one - in purple - had very limited effects on thye changes in the media industry.  If you look closely however, the purple factor could hold the industry on its own.  See, for example, August-October 2007: the green economic factor was on the decline, but the purple economic factor was contributing a positive impact on the total media investments.

The next question...:  What's going to happen next?

A very good question, really.

The next step is to identify what's going to happen in the future.  But remember: our models were based on economic indicators and at least one economic indicator accounted for about 40% of the data's movements.  That makes predicting the future of the media industry's size in 2009 and 2010 even more difficult.

But my team and I may just have fuond a way.

That will be for another day.

The Usual Caveats

These are based on my personal estimates and are not necessarily reflective of the company and brands that I work for.  The models - whilst acceptable from a statistical and econometric POV - are just models: they cannot explain everything.  And as we learned from the financial crisis that is now gripping the world, the past is not necessarily a good predictor of the future and hindsight is always 20/20.

These are what I personally call "descriptive models" - they describe what have happened in the past with the hope that we learn something.

I used Nielsen Media Research data on this - thank you very much.  In addition, the econometric modeling was done using a combination of pure manipulation of numbers on Microsoft Excel and a proprietary tool of UM/Initiative.

 

of oranges, correlations, copulas, and the credit crisis

(Image is from Wired.Com)

This is apparently the formula that got us into the credit mess in the US and now is threatening the entire world’s financial system. Developed by David Li, a Chinese-born New York banker, the formula came out of a “broken heart”. The hypothesis that David Li started with was this: People tend to die faster after the death of a beloved spouse.

With this simple formula - called copulas - Li was able to relate different variables and measure the intensity of each variables’ effect on one another.

A good example would be this: Let’s say you’re going to the supermarket. You look at an orange. In the normal world, you would see the price of the orange - let’s say 10USD (some expensive oranges from Japan - flown overnight, first-class, and straight from the orchards on the slopes of Mount Fuji… I am exaggerating).

That’s a pretty big sum of money for the simple pleasure of eating an orange.

There are risks involved in your purchasing that orange at 10USD: what if

(1) when you get home, it’s not as great as you expected it to be? I’d call this the “satisfaction risk”;

(2) when you get home, the orange tastes like a normal apple that you could have purchased at a lower price? I’d call this the “relativity risk”

(3) in spite of it being orangey and brightly-orange, it actually is rotten inside? “Real-freshness risk”

(4) you don’t get to eat it - and you had to postpone eating it… and this 10USD-orange will only last for only 12-24 hours after having been purchased? “Time-Effects Risks”.

In the world of copulas and correlations, this apple would have taken into account the different risk aspects. So in this world, you will see not only the price - 10USD per orange - but also descriptions of the risks. The 10USD pricing takes into account not just the price of produce but also the risks involved in your purchasing the orange.

Or better yet - and this is what happened with the CDOs, SIVs, and what-nots - you have different bunches of oranges priced differently to take into account their risk-profiles:

1. In this risk-world, you’ll have 10USD oranges - high-grade, minimal risks across all four risk-dimensions.

2. You’ll also have 8USD oranges - still high-grade, but perhaps there is a higher likelihood of it being not fulfilling the Real-freshness/Relativity Risks.

3. You’d also have 5USD oranges - which can guarantee risks on satisfaction, relativity, and real-freshness - but not necessarily the time-effects risks.

4. You’d also have 2USD oranges - which are “junk oranges” - high-risk, caveat-emptor, ‘junk’ oranges.

The idea behind it is that, the risks have been ‘dimensionalized’ and have been ‘measured and priced’ accordingly. (This is what ratings agencies - such as S&P, Moody’s, and Fitch - do.)

And another core tenet is that there is a market for these tranches of oranges - regardless of whether they are high-grade or junk. Why would anyone buy the junk ones? Well, remember the tenet “Without risks, there are no rewards” and that “The higher the risks, the higher the reward”. So even the junk ones can potentially reward a buyer with high returns.

And another thing: Because you have the copulas, you can even segment the 2USD oranges when you have bought them into different segments: If I bought 100 of the junk oranges, I’d be able to sub-divide them even further - say into 5 more sub-division - based on their risk profiles and resell them for a profit.

I could probably have 2USD oranges, 1.50USD oranges, 50cents, and 25cents.

(Remember: there’s always a market for everything in the market!)

This is what copulas have allowed financial engineers to do: peek into the risk profiles of each of the different oranges, put a dollar value into them, and sell (and resell) them.

The formulas essentially created for us a way to put a number to the correlations and co-relations between different variables, which at first are seemingly unrelated, put a dollar value to that risk, and package them and sell it to others.

I’d say this is pretty neat.

But what’s happened in the credit-crunch is different: You see, you were not just buying oranges - you were buying people’s debts. And the assumption was that people are going to be rational when it comes to paying their debts - particularly their home mortgages.

What was forgotten in the credit-crunch is that, people are not rational: we tend to move in herds - and we tend to move in response (logical or illogical) to news (real or unreal) that are broadcast in the media (which tend to be gloom and doom).

Essentially, people are not oranges. And that’s where the financial engineers failed.

(Disclosure and disclaimer: Much of this came from reading Wired.Com cover story. I am not an expert on risk management - I am currently studying risk management but I am not an expert. These are my interpretations - and my attempt to understand them. Copulas, having tried them in an exercise in a class, are I think a great tool. There is more to copulas than these, I am sure. Copulas are not - in themselves - good nor bad. They just are tools - and it is the people who use them and abuse them that make the difference - or not. Like the Black-Scholes Options-Pricing Model, it’s neither good nor bad. It is what it is - a set of formulas that can be used for good. Or for bad.)

Re-blogged from Experimentalist.

Singaporeans Bunker Down for the Global Financial Crisis

56% expect local economy to perform better than our neighbors

Singapore, November 6, 2008: The global financial crisis has become an omnipresent spectre for the people of Singapore. No one can escape the overwhelming media coverage of the billions of dollars being poured into the world’s financial systems by governments desperate to avoid a meltdown of their most fundamental institutions.

What does this all mean for Singaporeans? How do we feel about the crisis and what does it mean to us? What are we doing about it? What do we think will happen?

Universal McCann, a media communications agency, surveyed 400 people aged 20 to 50 years to find out some answers as we move towards the end of 2008. The survey was conducted between October 20 and 25^th.

Key results:

· 72% of the Singaporean residents expect the global economy to turn for the worse in the next six months. Only 12% were upbeat in their expectations of the global economy, believing that it will be better, and only 16% believed that it will remain the same.

· Their perspective on the local economy is pretty much correlated with this belief: 67% of the 400 Singaporeans aged 20-50 that were polled are of the belief that the local economy will also turn for the worse in the next 6 months, with only 19% believing that it will remain the same and the rest (14%) believing that it will be better.

· These negative perspectives are in line with people’s expectations of the local stock market’s performance: 68% believe that the local stock market is yet to hit rock-bottom, turning for the worse in the next six months, with only one in three (32%) believing that it will remain at least the same or grow.

· 71% say that job opportunities are going to be tighter in the next six months, and close to 60% believe prices of household and food items will be a challenge in the next 6 months.

· In the real-estate sector, 69% of people expect a decline in value in the next 6 months as the economic downturn takes its toll in Singapore.

 

Rays of Hope

While there is some negativity in the market, there are signs of confidence and hope among Singaporeans: About 64% expect their personal financial situation in the next six months to be the same or even better. Close to 70% are secure in their household financial situation, with opportunities of getting slightly better in the next six months.

In fact, Singaporeans believe that the local economy is better poised than our neighboring countries to weather the economic storm. More than half (56%) of Singaporeans surveyed believe the economy is going to fare better than its neighboring countries.

Against major economies such as the US and the UK, the epicenters of the global economic slowdown, only 21% believe that Singapore is going to perform worse than these economies. Twice this amount – 40% – believe that Singapore is going to fare better than these major economies.

 

Technorati Tags: Singapore,Economy,Economics,Global Crisis,Recession,Singaporeans,Universal McCann,Consulting

So are we really ready for the downturn?

I just finished some 'runs' (i.e., data processing) on the data that we have gathered from a poll of about 400 Sg respondents about their views about the next 6 months.  I would say we got some interesting results.  It's - for lack of a better term - interesting.  One may look at it and say "conflicting viewpoints" - but further analysis actually indicate that it all makes sense.

Anyway, if you're interested about what I am talking about, just ping me or leave a comment and I'll get you the stuff as soon as they are out.  I am still writing and writing and writing - whilst at the same time churning stats and tables and conducting tests.

Reality checks though...  This is a presentation from Sequoia Cap which VentureBeat.Com called "RIP: Good Times".  I honestly don't know much about finance - I am still studying my basics after having realized that I can't those frigging numbers - and I don't know much about macroeconomics, having focused on micro an behavioral economics in my electives in college.  So I really can't agree or disagree in indeed, it is "RIP" to good times.

Have a read through the presentation:

Sequoia Capital on startups and the economic downturn

View SlideShare presentation or Upload your own. (tags: depression recession)

 

Here are my thoughts:

• As I have said, I don't know much about economics.  But all I can say is there seems to be a lot of evidence cropping to suggest that we're in bad shape - regardless of where we are.
  
• The supposed benefits of globalization and global economies are still there - BUT so are the risks and the threats of globalization.  Friedman was right - the world is flat - and the thing is, with 'flatness' comes a faster 'negative contagion'.
  
• This is perhaps already apparent in all the things that have been said and done about the crisis: but we're talking about a system here.  It's not a "US problem" - it's not even "just a subprime" problem.  It's a systemic problem - that will require a solution that is integrated and isn't piecemeal.
  
  (Michael Porter, author of Competitive Strategy, has a great article on the latest issue of BusinessWeek.Com on economic strategies - and why the US needs one.  Worth a read for all CEOs and strategists.)
  
• The last slide is the real killer slide: GET REAL or GO HOME.  We need to acknolwedge that there is a mess.  The funny thing is, there are still camps of people who think that "this too shall pass".  Yes, in the long run, it will.  But Keynes said "In the long run, we'll all be dead!"
  
• In the mean-time, however, we need to placate the markets and the consumers.  The thing that really fuels all these is our humanity.  Our fears.  That instead of "things are going to be better tomorrow", it's "things are getting worse tomorrow..." 
  
  Now that's really being "Polyannaish" of me - but that's the only way to get out of the downward spiral: reinstill confidence in the market.  Reinstill confidence in the consumer.
  
  Easier said than done - considering that even banks don't trust each other - and the effectiveness of the bailout the Feds and the Treasury of the US have initiated is being questioned.
  
  But that's the only way to get out of this mess.

So is anybody immune?  How much longer will it take?  Will we still be in this rut next year?  And more importantly, will we still have our jobs?

Well, we'll see.

This is an interesting presentation picked up from SlideShare.Net about the subprime mess. A little rough - and definitely not meant for presentations using a projector. But hey, who cares? It's fun. And it's informative.

Subprime Primer

View SlideShare presentation or Upload your own. (tags: subprime mortgages)

Robert Shiller, economist and author, on the US Economy

Robert Shiller talks about the US economic trends and financial trends in the marketplace.  What interests me really is his point of view that these problems are largely because of "fundamental" inadequacies of the financial system when it comes to risk management impact not just the immediate financial and business world.

Oh well.