Google Hits on Actuary

Google can be a great help for actuaries. Especially 'Google Insights' and 'Google Trends' are two useful applications for retrieving relative Google Search Hits data from the Internet.

Google Insights Example
Let's dive a little deeper into Google Insights and start with researching the relative development of the number of hits on the word 'Actuary'.
Here is the result (period 2004-2011-May, extracted csv-file, Excel-Graph):

Explanation
The numbers on the graph reflect how many searches have been done for a particular term (e.g. 'Actuary'), relative to the total number of searches done on Google over time. They don't represent absolute search volume numbers, because the data is normalized and presented on a scale from 0-100. Each point on the graph is divided by the highest point, or 100.

Conclusion

Clear is that the search for (the word) actuary is relatively declining from 2004 to May 2011.

To keep the actuarial profession virtually alive we'll need to make more noise as actuaries on the Internet.

Step outside, spread the (acturial) word, make yourself visible in the outer world and let people wonder:  'who's that?',  'what a professional', 'what's his job?', 'Actuary?', 'I will google it!'.

So let's Twitter and Blog to get more actuarial exposure...


Actual Data

Apart from generating these kind of relative time-data, Google Insights can generate actual data anywhere on any web-application or presentation.

This way your data will always be up to date!
Moreover Google Insights is easy to handle without any code knowledge.....


Some examples....

(1) Actual relative development of the number of hits on the word 'Actuary'


(2) Top searches and rising searches on Google for the word 'Actuary'

More applications

The next example shows how you may use Google Highlights as a market crash predictor.  

It turns out that in advance of the 2008 market crash, Google searches on "Stock market crash" increased...

Make you own discoveries, highlights or trends (e.g 'Solvency II') and enjoy!


Related Links:
- Actuaries on Twitter
- Google Insights 
- S&P 500 Data 
- How Google Trends and Internet Searches Correlate with Asset Prices
- Google trends: 21 May 2011: End of the world, predicted by Harold Camping

Actuarial Proverbs: Will Europe Survive?

According to Eurostat, Europe - especially the Euro (€) 'Coin' Countries that put all their Euro eggs in one basket -  face a difficult time. In a world where money seems to grow on trees, it's hard to take the right measures to prevent Greece from a financial meltdown with unknown consequences.

Questions
Even for actuaries it's hard to understand what's happening and what makes sense or not, It's over our 'actuarial' head....

• Should 'Europe donor countries' support Greece fore more than the '110 billion Euro rescue' in 2010?
  
  
• Is Greece’s 10-year bond rate of 15% an adequate risk premium?
  
  
• Will restructuring Greece's debt solve anything, devaluate the Euro,  or pose other  incalculable risks to the overall Euro zone?

Difficult questions that are hard too answer....


Debt-Deficit Comparison
Let's take an actuarial look at the facts by comparing 2010 Government Debt with Deficit (all in % GDP):

From this chart it's clear that not only Greece is in the danger zone, but also Ireland and the US as well... Moreover, the UK is not free from worries, to put it mildly...

The blind are leading...
Another chart-conclusion might be that the blind are leading the blind'. Relative strong less-weaker countries like Germany and France,  have to carry the financial consequences of cheating and not-performing countries. Above all, we all know: one rotten apple spoils the barrel!!

In fact to save or revive 'Financial Europe' it would take some countries with no debt and a strong positive surplus (= negative deficit) instead of a deficit.

It seems neither sensible nor logical  to restructure another  country's debt if the outlook of the governments debt and deficit of the' helping country' is (slightly less) negative as well. But as we know: only fools rush in where angels fear to tread.

Trying to help other countries that fail to restructure themselves is like banging your head against a brick wall...  No risk premium on government bonds can compensate that...

Countries with a strong relative debt and a deficit should restructure their own country and financial situation at once, before asking ore receiving any outside help.

Growth: The Solution?
Some argue that debt and deficits are not so bad as long as countries are growing. Let's dive into this argument with the next chart (data source: Eurostat):

Indeed, from this 'Growth-Believe' we can now understand why (only) Greece is seen as such a major problem.

From this chart it's also clear that if Ireland and Spain are not going to grow one way or the other, they will become the next big problem. These countries have to take the bull by its horns, before it's too late.

It's throwing caution to the wind when 'debt and deficit countries' with a positive 'Real GDP Growth Rate' try to save sicker country-brothers by lending them money.

Moreover, it's lending money you don't really possess or own, it's like robbing Peter (yourself) to pay Paul....

Combining the two Eurostat charts it becomes clear that that not all 'Garlic Countries' (Mediterranean countries:Greece, Spain, Portugal, Italy) can be lumped together.

Greece is indeed the greatest risk , secondly a non-garlic country: Ireland...
Spain, Portugal and Italy are relatively at arm’s length and could perhaps keep their head above water if they take the right measures in time.

U.S.' Fiscal Gap

Finally, don't forget about the U.S., as the U.S. Real GDP Growth Rate is already declining to 2.3% in Q1 2011.

According to Boston University economist Kotlikoff, the U.S. is broke.  Kotlikoff doesn’t trust government accounting. He uses “Fiscal Gap,” not the accumulation of deficits, to define public debt. This "Fiscal Gap" is the difference between a government’s projected revenue  and its projected spending .

By this measure, the U.S. government debt is $200-trillion – 840 percent of current GDP. 

Conclusions

From all this it's clear Europe is stuck between a rock and a hard place...
Although ECB President Mr. Trichet thinks different, it looks like €-Europe has to choose between two blind goats (Irish saying):

(1) A complete Financial Europe Meltdown in case of endless financing default countries like Greece or

(2) Letting individual default countries go bankrupt, with unsure (systemic) consequences for local banks and other financial institutions that financed or invested in default countries.

How to decide? Guideline:  Of two evils, always choose the less....
As option (1) is clearly putting the cart before the horse, and surely leads to a meltdown, only option 2 is left: QUIT!

Sources and related links:
- Spreadsheet: Used Data, Tables for this blog (xls)
- US Real GDP Growth Rate
- Government Debt and Optimal Monetary and Fiscal Policy (2010)
- English proverbs and sayings (!)
- English deficit (including time table)
- Shadowstats (for the real stats!)
- The U.S. is broke?
- Eurostat: Euro area government deficit at 6.0% GDP (2011) 
- BILD: Interview with Jean-Claude Trichet, President ECB, 15 January 2011

Oversized Supervision?

In April 2011 EIOPA  published  the findings of its 2010 survey:

 REPORT ON REPORTING REQUIREMENTS TO SUPERVISORY AUTHORITIES

applicable to the Institutions for Occupational Retirement Provision (IORPs) in the context of the IORP Directive.

The report analyses several interesting differences in reporting among member states.

I'll will confine myself in this blog to two remarkable results....
 
1. Difference in number of Supervision employees per country
It's remarkable (and not directly explainable) to see that the UK and The Netherlands outnumber the other European countries on number of supervision employees....

 
2. Influence Actuarial Reporting
The survey provides a large number of reporting and monitoring issues that aim to monitor or mitigate several types of risk.
I'll provide a short report that shows the connection between some actuarial reports and types of risk.

Clearly the risk of funding is one of the most important issues with regard to actuarial reporting. Perhaps it's even a little bit overweighted......

Anyhow, check your reports with regard to the above risks, especially if your living in an oversized supervision country like the UK or The Netherlands....

Homo Actuarius Bayesianis

Bayesian fallacies are often the most trickiest.....

A classical example of a Bayesian fallacy is the so called "Prosecutor's fallacy" in case of DNA testing...

Multiple DNA testing (Source: Wikipedia)

A crime-scene DNA sample is compared against a database of 20,000 men.

A match is found, the corresponding man is accused and at his trial, it is testified that the probability that two DNA profiles match by chance is only 1 in 10,000.


Sounds logical, doesn't it?
Yes... 'Sounds'... As this does not mean the probability that the suspect is innocent is also 1 in 10,000. Since 20,000 men were tested, there were 20,000 opportunities to find a match by chance.

Even if none of the men in the database left the crime-scene DNA, a match by chance to an innocent is more likely than not. The chance of getting at least one match among the records is in this case:



So, this evidence alone is an uncompelling data dredging result. If the culprit was in the database then he and one or more other men would probably be matched; in either case, it would be a fallacy to ignore the number of records searched when weighing the evidence. "Cold hits" like this on DNA data-banks are now understood to require careful presentation as trial evidence.

In a similar (Dutch) case, an innocent nurse (Lucia de Berk) was at first wrongly accused (and convicted!) of murdering several of her patients.

Other Bayesian fallacies
Bayesian fallacies can come close to the actuarial profession and even be humorous, as the next two examples show:

1. Pension Fund Management
   
   
   It turns out that from all pension board members that were involved in a pension fund deficit, only 25% invested more than half in stocks.
   
   Therefore 75% of the pension fund board members with a pension fund deficit invested 50% or less in stocks.
   
   From this we may conclude that pension fund board members should have done en do better by investing more in stocks....
   
   
2. The Drunken Driver
   It turns out that of from all drivers involved in car crashes 41% were drunk and 59% sober.
   
   Therefore to limit the probability of a car crash it's better to drink...

It's often not easy to recognize the 'Bayesian Monster' in your models. If you doubt, always set up a 2 by 2 contingency table to check the conclusions....

Homo Actuarius
Let's  dive into the historical development of Asset Liability Management (ALM) to illustrate the different stages we as actuaries went through to finally cope with Bayesian stats. We do this by going (far) back to prehistoric actuarial times.
 

As we all know, the word actuary originated from the Latin word actuarius (the person who occupied this position kept the minutes at the sessions of the Senate in the Ancient Rome). This explains part of the name-giving of our species.

Going back further in time we recognize the following species of actuaries..

1. Homo Actuarius Apriorius
   This actuarial creature (we could hardly call him an actuary) establishes the probability of an hypothesis, no matter what data tell.
   
   ALM example: H₀: E(return)=4.0%. Contributions, liabilities and investments are all calculated at 4%. What the data tell is uninteresting.
   
   
2. Homo Actuarius Pragmaticus
   The more developed 'Homo Actuarius Pragamiticus' demonstrates he's only interested in the (results of the) data.
   ALM example: In my experiments I found x=4.0%, full stop.
   Therefore, let's calculate with this 4.0%.
   
   
3. Homo Actuarius Frequentistus
   In this stage, the 'Homo Actuarius Frequentistus' measures the probability of the data given a certain hypothesis.
   
   ALM example: If H₀: E(return)=4.0%, then the probability to get an observed value more different from the one I observed is given by an opportune expression. Don't ask myself if my observed value is near the true one, I can only tell you that if my observed value(s) is the true one, then the probability of observing data more extreme than mine is given by an opportune expression.
   In this stage the so called Monte Carlo Methods was developed...
   
   
4. Homo Actuarius Contemplatus
   The Homo Actuarius Contemplatus measures the probability of the data and of the hypothesis.
   
   ALM example:You decide to take over the (divided!) yearly advice of the 'Parameters Committee' to base your ALM on the maximum expected value for the return on fixed-income securities, which is at that moment  4.0%. Every year you measure the (deviation) of the real data as well and start contemplating on how the two might match...... (btw: they don't!)
   
   
5. Homo Actuarius Bayesianis
   The Homo Actuarius Bayesianis measures the probability of the hypothesis, given the data.  Was the  Frequentistus'  approach about 'modeling mechanisms' in the world, the Bayesian interpretations are more about 'modeling rational reasoning'.
   
   ALM example: Given the data of a certain period we test wetter the value of H₀: E(return)=4.0% is true : near 4.0% with a P% (P=99?) confidence level.

Knowledge: All probabilities are conditional
Knowledge is a strange  phenomenon...

When I was born I knew nothing about everything. When I grew up learned something about some thing. Now I've grown old I know everything about nothing. Joshua Maggid

The moment we become aware that ALL probabilities - even quantum probabilities - are in fact hidden conditional Bayesian probabilities, we (as actuaries) get enlightened (if you don't : don't worry, just fake it and read on)!

Simple Proof: P(A)=P(A|S), where S is the set of all possible outcomes.

From this moment on your probabilistic life will change.

To demonstrate this, examine the next simple example.

Tossing a coin

• When tossing a coin, we all know: P (heads)=0.5
• However, we implicitly assumed a 'fair coin', didn't we?
• So what we in fact stated was: P (heads|fair)=0.5
• 
  Now a small problem appears on the horizon: We all know a fair coin is hypothetical, it doesn't really exist in a real world as every 'real coin' has some physical properties and/or environmental circumstances that makes it more or less biased.
• We can not but conclude that the expression
  'P (heads|fair)=0.5'  is theoretical true, but has unfortunately no practical value.
• The only way out is to define fairness in a practical way is by stating something like:  0.4999≥P(heads|fair)≤0.5001
• Conclusion: Defining one point estimates in practice is practically  useless, always define estimate intervals (based on confidence levels).

From this beginners  example, let's move on to something more actuarial:

Estimating Interest Rates: A Multi Economic Approach

• Suppose you base your (ALM) Bond Returns (R) upon:
  μ= E(R)=4% and σ=2%
  
  
• Regardless what kind of brilliant interest- generating model (Monte Carlo or whatever) you developed, chances are your model is based upon several implicit assumptions like inflation or unemployment.
  
  The actual Return (R_t) on time (t) depends on many (correlated, mostly exogenous) variables like Inflation (I), Unemployment (U), GDP growth(G), Country (C) and last but not least  (R_[t-x]).
  
  A well defined Asset Liability Model should therefore define (R_t) more on basis of a 'Multi Economic Approach'  (MEA) in a form that looks more or less something like: R_t = F(I,U,G,σ,R_[t-1],R_[t-2],etc.)
  
  
• In discussing with the board which economic future scenarios will be most likely and can be used as strategic scenarios, we (actuaries) will be better able to advice with the help of MEA. This approach, based on new technical economic models and intensive discussions with the board, will guarantee  more realistic output and better underpinned decision taking.

Sources and related links:
I. Stats....
- Make your own car crash query
- Alcohol-Impaired Driving Fatalities (National Statistics)
- D r u n k D r i v i n g Fatalities in America (2009)
- Drunk Driving Facts (2006)

II. Humor, Cartoons, Inspiration...
- Jesse van Muylwijck Cartoons (The Judge)
- PHDCOMICS
- Interference : Evolution inspired by Mike West

III. Bayesian Math....
- New Conceptual Approach of the Interpretation of Clinical Tests (2004)
- The Bayesian logic of frequency-based conjunction fallacies (pdf,2011)
- The Bayesian Fallacy: Distinguishing Four Kinds of Beliefs (2008)
- Resource Material for Promoting the Bayesian View of Everything
- A Constructivist View of the Statistical Quantification of Evidence
- Conditional Probability and Conditional Expectation
- Getting fair results from a biased coin
- INTRODUCTION TO MATHEMATICAL FINANCE

Humor: Scrambled Actuarial Reporting

Some actuaries are convinced that adding more important details really helps. With more details and more information you are able to explain you models better and as we all know: better communication is key in actuarial science.


Here is an example of detailed information (click on the image!)

Some(times) details don't matter
Unfortunately more information and more details generally disturb efficient decision making. The next text shows that some details don't really matter.

Smoe acaruites are covcnined taht adding mroe imnrpotat deaitls rlaely hleps. Wtih more dleitas you are albe to eplaxin you mlodes bteter and as we all konw: btteer cmniutcoiaomn is key in aratiuacl sieccne.

Sirnpigrulsy tihs is not ture. Tihs txet sowhs taht smoe daeilts dno't rlaley mttear.

The arutacial aidnceue isn't rlaley istretneed in the daeilts, but in caelr ipunt (fsrit ltteer of a wrod) and oumotces (last letetr of a word). The dtilaes (letetrs) in bweteen can be mexid up in evrey rodnam oerdr you lkie. Keep in mnid tihs iponmatrt lsosen in your nxet peeiatntsorn.

Explanation
According to a study at Cambridge University, to read and understand a text well, it doesn't matter in what order the letters in a word are placed. The only condition is that the first and last letter of each word remain the same. The rest can be a total mess up. This is because the human mind does not read every letter by itself but the word as a whole.

DIY
Let's conclude with an 'example text' for the opening-slide of you next board presentation:

Daer Board mrebmes,

Agtlhouh we hvae to tkae fetdanmaunl dniecioss tdoay, it wlil not be ncseresay to udasnertnd or dcssius all knid of tcihcenal dtileas.

The relust of my avicde is pertseend in scuh a way as to esurne taht we can stcik to the mian ptinos and hneieadls.

The vrey fcat that you wree albe
to raed and udnreastnd tihs txet,
greauetans taht we wlil hvae a
sefscuucsl mtineeg.

Yuor aivdosr

Scramble your own opening-slide text for your next presentation at:

Words Scrambler

No doubt, your next report will be actuarial scrambled.... ;-)

Related sources and links
- Words Scrambler
- MRC Cognition and Brain Sciences Unit
- All My Faves

The Ten Actuarial Commandments

We all (think to) know The Ten Commandments from the holy scripts by heart, do we?

Now close your eyes to see how far you can get in quoting those simple ten guidelines in life.............

The Ten Commandments for Investors
Just like the Ten Commandments for Man, God - more specific - created The Ten Commandments for Investors. Let's compare the two, while - at the same time - you can check out your Commandment-Memory on Man as well:

Risk-Return-Supervision Development
As you may have noticed, The Ten Commandments are a mix of rules-based and principles-based principles.

Just as in our own life, it's interesting to see how we apply and implement these two different kind of rules during the evolution of a financial institution (insurance company, pension fund, bank, etc.):

In time, the ideal supervision model consists of three phases:

• Phase I: No rules
  In this phase we cannot value or the company. Chances are substantial the company is 'at risk'.
  
  
• Phase II: Rules-Based Supervision
  In phase Ia 'Rules' are mostly perceived as 'Have to's" . As a result Risk will be reduced, but Return as well. Once the board, actuaries and financial specialists are becoming aware and will see the advantages and new possibilities of managing risk. 'Have to's" will develop into 'Want to's" . The Risk-Return Ratio will increase  and even a better Return will result.
  
  
• Phase III: Principles-Based Supervision
  Just like with the implementation of Rules-based Supervision, in case of Principles-Based Supervision, the Financial Institution needs time to adept to the new situation. At first there might be a unbalance between Risk and Return. It takes time to calibrate Risk and Return again.
  
  After a while actuaries, investors and management will translate Rules-Based principles into own rules that fits the company's specific risk in an optimal way. The company will be able to take more risk and to optimize its own Risk-Return Ratio.

Take a look at your own company's development and see for yourself where you fit in on the Risk-Return-Supervision lines....

It might be possible that you have to conclude that you aren't able to increase your Risk-Return ratio in the end. In this case it's likely you've become (so called) 'Supervisory Compliant': Your risk appetite probably corresponds more or less with the supervisor's minimal risk view. Instead of redefining your own risk appetite and restructuring your products from a risk-management perspective you merely implied new regulations and supervisor guidelines. As a result your Return and Risk-Return Ratio implode....

Ten Actuarial Commandments
Having learned the possible effects of supervisory rules in practice, we may now conclude with The Ten Commandments for Actuaries.

The Ten Commandments for Actuaries

1. There's only one God, as he's omnipotent he's also an actuary.
   As you're only an actuary: be humble.....    Remember: As God wants something in Return, you'll have to take Risk!!
2. Reality can't be comprised in a model.
   Use your brains. A model is a help, not a decision machine. Don't mix up God with Risk or Chaos. Chaos for us humans (actuaries) can be defined as "Unrecognized Order" (quote). 
3. Never blame anything or anyone than yourself for an unexpected or negative outcome.
4. Be consistent, act sustainable. But change your opinion just in time, if circumstances or facts urge you to do so.
5. Alway show respect to others, even if you think different. Appreciate where you come from. Nobody is perfect, not even you.
6. As there is no 'right' model, never criticize other models, actuaries or other people. Try to give your opinion without slaughtering the other.
7. Never advice or state anything you do not really mean or cannot defend.If you're not sure or don't know, tell it or get help.
8. Always cite your sources or give credits to others that helped you.
9. Don't 'steal' the advice.
   Never include the final decision to be taken in your advice. Wrap up arguments, consequences and present scenario's so the board has to make a choice and not you.
10. Don't get carried away by results, reports or performances of others.
    Stick to your own consistent approach.

Apply supervisory rules and actuarial commandments in a conscious way...