The Thought Refuse

A Virtual Repository for the Mind

Posts Tagged ‘Randomness

The Problem of Correlation As Causation

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The confusion of supplanting correlation for causation is one of the most common logical fallacies we make.  This is the fallacy of correlation.  The basic premise is that you will attribute a connection between two experiences as the root cause of one experience being the cause of the other.

While it’s one of the easiest logical fallacies to spot, we continually fall for the trap of the fallacy of correlation.  It does require a minor exertion in mental analysis to catch ourselves spiraling down it’s pitfalls, but it’s shouldn’t be too much to ask a person to invest that energy into their own line of thinking.  None-the-less, it’s a logical fallacy which pervades everyday thinking, and, regretfully, even scientific research.

As a general schematic, think of the fallacy of correlation to be as follows:

  • Event A occurs synchronously or chronologically to Event B‘s occurrence, therefore
  • Event A is the cause of Event B.

The possibilities of the relationship between Event A and Event B are too numerous to conclude A caused B.  Some of these include:

  1. A is the cause of B;
  2. A is the cause of B, and B is the cause of A (or both events sharing a circular causation);
  3. an unknown Event C is cause for either A or B, or both;
  4. the incidence of A and B share no relationship other then temporal occurrence.

It is rare for Case 1 to be true, yet far too often we prefer it from the other possible Cases.  This is often the outcome when a layer of plausibility exists within our empirical history interconnects two events(Hume’s definition of causation).  Plainly stated, if I have been witness to two events occurring in the past, I am likely to make a connection between these two events when the happen again in the future.  They are believable.

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Written by huxbux

February 4, 2009 at 7:25 pm

Posted in Logic, Philosophy

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Why Your Blog Hits Aren’t Really Yours

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With the explosion in the number of blogs littering the internet, every blogger takes extraneous efforts to increase their visitor count.  The all important hit count is the singular indicator whether your blog is popular or not.

There are blogs dedicated soley to advising bloggers on who to get their blogs noticed, and attract a larger share of readership.  Focusing on key words, interlinking blogrolls, content distribution, and comment links in other blogs are all part of the “how to get your blog hits” mantra.

You’ve followed all the steps to get you blog hits – selected a topic to focus on, included important key words in your tags and post titles, built a nice shared blogroll with other blogs, and politely commented on other blogs to get the word out about your blog.  After all these steps your blog is averaging a couple hundred hit per day.  All things considered, you’ve done fairly well for yourself.  But your blog is middle-brow.  One of among tens of thousands of other blogs that suffer from medicrioty in terms of hits.

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Written by huxbux

January 12, 2009 at 6:57 pm

Posted in Business, Logic, Philosophy

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Cantor Fitzgerald Moving HSX Into The Real World

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The trading firm Cantor Fitzgerald will be starting a real world version of the Hollywood Stock Exchange – an online virtual trading world where players buy and sell movie and celebrity stocks.  Cantor will now offer movie studios to purchase bonds relative to a movie’s financial performance.

Cantor is actively recruiting veteran HSX traders to participate in advertising and selling these bonds to movie studios.  Cantor Fitzgerald needs “experts” to sell their product.  While there are undoubtedly individuals who have fared appreciably more successful then other traders on HSX, I’m hard pressed to say there are any “experts” in the fantasy trading game.

Let us assume the Cantor incarnation will operate similar to HSX.  A movie stocks price is based on it’s expected total gross(for wide releases four weeks, twelve weeks for limited release).  On it’s opening weekend, a movie will adjust according to it’s weekend gross along with an internal multiplier(typically 2.8).

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Written by huxbux

January 7, 2009 at 7:08 pm

The Unpredictability of Sports

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As with all systems that aren’t manufactured, for instance casinos, sports is a highly unpredictable arena.  But littered with experts from former players to journalists eager to give you their arrogant self-predictions for future seasons.

Every season for every sport, these experts publish their predictions in newspapers, magazines, and television shows(the latter particularly prone to bombastic proclamations of arrogance).  The experts put on record every year(and every week) their elevated knowledge in the sports domain.  Take a quick look at just how much these so-called professionals really know, and you’ll find that sports is as unpredictable as most everything in life.  That guy on ESPN whose studied football for twenty years is probably no better at predicting the final NFL standings then you or me.

Take a look at ESPN’s preseason power ranking for the NFL.  The error rate is astounding.  Of the 16 bottom ranked teams, 5 of them made the playoffs this season.  Two of the supposed three worst teams ended up with records 11-5 records(Atlanta and Miami).  The Titans were ranked 16th and ended up with the best record in the league.  The Jaguars, Saints, and Seahawks were all ranked in the top ten, but not one ended with a winning record.

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Written by huxbux

January 5, 2009 at 7:05 pm

Posted in History, Philosophy, Sports

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The Presidential Race Not Immune To Randomness

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I have sobering news for all of the political fanatics out there feverishly consuming every tidbit of news around the presidential race as confirmation or refutation for the support they’ve thrown beyond their respective candidate.  The presidential race will not be decided on the laundry list of pros and cons for each candidate that you’ve taken painstaking effort to lay out.  No, the presidential race will be decided by a randomness.

The presidential race of 2008 will be decided based on the crash of the housing market and the subsequent drop it caused in the nation’s economy.  For as much as the media and average citizens strive to transform the economic crisis into a politically derived problem, it is not.  I have previously made numerous posts concerning the reasons behind the economic crisis, and specifically attributed the error in management on the risk management models used by financial institutions.  The post illustrated how randomness poses a severe threat to these models.  The qualification for a random event is an event which cannot be predicted which precipitously qualifies the economic crisis as the consequence of a random event.

Just as the financial institutions risk management models did not predict a fall in home prices greater then 10%, political pundits could not predict the massive shift in the presidential race that the economic crisis would cause.  Prior to the first day the stock market plummeted nearly 800 points and the final realization that the economy was teetering, McCain was neck and neck with Obama.  Some polls showing McCain with a slight lead and others having McCain trailing by only a couple points.  Immediately after, McCain’s poll numbers began slipping, and as the nation became inundated with daily news that the economy was on life support, McCain’s numbers began to suffer from the war of attrition.

The economy became the center issue in the presidential race.  It thrusted itself to the forefront to become the deciding factor.  For political purposes, the affair was tailored by each candidate to suit their campaign in what amounted to an advertising campaign.  Voter perception leaned heavily towards Obama as being the one best suited to guiding the country back to economic health.  This voter acumen, as it turns out, resides without substance.

Considering risk management models bore the fertile blame for the financial catastrophe, how then can the politicization of the problem be justified?  It simply cannot.  However, it certainly has played a critical role in the shape of this presidential race.  Partisan advertising, voter ignorance, and media saturation loaned it the power necessary to become the deciding factor.

Barack Obama constructed an unwittingly genius advertising campaign while battling Hilary Clinton in the Democratic primary that was designed to link the presumptive Republican nominee to Bush’s economic policy.  Coupled with Obama echoing sentiments of economists, he painted a bleak economic picture.  In a speech earlier in the year, Obama said:

We are not standing on the brink of recession because of forces beyond our control.  This was not an inevitable part of the business cycle. It was a failure of leadership in Washington — a Washington where George Bush hands out billions of tax cuts to the wealthiest few for eight long years, and John McCain promises to make those same tax cuts permanent, embracing the central principle of the Bush economic program.

The Obama campaign as continued to connect the economic polices of George Bush as “failed” and inexorably tying McCain to those “failed” policies.  From a strategic standpoint, it stands as the center point for his presumed presidential victory.  Yet, it’s quite simply inaccurate in the sense that the term “failed” predicates that the economic policies of Bush/McCain caused the economic crisis.

The center of this recession and possible depression does not even remotely revolve around tax cuts.  Tax cuts putting money in the pockets of the poor, middle class, and rich has no bearing on sub prime lending practices or the flaws in statistical risk management models.  The concept is absurd.  In fact, it’s counter intuitive.  A middle class family receiving a tax cut would be more likely to take that money and use as a   on a home mortgage and would be less likely to enter into a sub prime, no down payment home loan.  Additionally, it’s clear that the wealthy, for whatever tax cut they might receive, are not consumers who are or were entering into sub prime mortgages.

The only credible accusation that can be made against Bush and his administration is government regulation.  But it’s difficult to conceive that the government, using the same risk management models and statistical information as the lending industry, would have been able to see what the financial sector could not.

Despite Obama’s inaccuracy, it was a strategic success due to voter ignorance.  Voters are not apt to critical thinking when examining the issues.  They display a preference for short and concise soundbites that can regurgitated on command.  We gravitate to linear paths and there is not a more straight path to making the connection between an administration that’s been entrenched for the last eight years, “failed” economic policies, and an economy entering a recession.  We are susceptible to the narrative fallacy and Obama beautifully catered to our ignorance for his own political gain.

The media onslaught that followed the stock market crash solidified Obama’s strategy.  There has not been a day in the last month that we have not heard more bad economic news.  Every time a voter read or watched a news piece on the economic crisis they made the connection in their mind between the state of the economy, those “failed” policies, and the message Obama has been preaching for months and months.

The fact of the matter is that no Republican or Democrat administration would have prevented the current economic crisis.  Short of the government heavily reducing the capitol requirements for lending and shifting the economy away from the debt based economy we have been operating on for nearly half a century, politicians were equal bystanders in the unfolding events that lead us to where we are.

So, it should be quite sobering to realize that if your voting for Obama or McCain primarily because you believe either candidate can bring the economy out by it’s bootstraps to know that you are casting your vote based on a random event.  It might even seem incomprehensible.

Randomness has the peculiar nature of being incomprehensible.  How can we understand that which we cannot predict, otherwise we would have already known it’s impending occurrence and been able to take steps to avoid said event.  Just as the statistical risk management models failed to predict the drop in housing prices, no one predicted that the decisive event in the presidential election would be, at it’s root, born from randomness.

How are we to feel knowing that the next leader of our country rode the wave of a random event into office?  I know I’ll be punching Obama’s name come November 4th for reasons divested of that random event, but it’s given me serious pause to come to terms with the fact that many other have been influenced by randomness knowing Obama’s poll numbers have reached double digits since the stock market crash.

Written by huxbux

October 17, 2008 at 6:33 pm

Financial Risk Management: The Problem With Applying Statistical Models To A Random System

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Having digested more then my fair share of reasons behind the market crash from television to radio to print to the blogosphere, I’ve been saturated by the common theme of corporate greed and corruption.  I don’t buy it as anything other then a compulsory and secondary component to a free market economy.  This is, of course, beyond obvious.

Add to the equation profitable longevity, and the greed/corruption arguement becomes less credible.  If I gave you the option of taking $5000 right now or $1000 for the next ten years which option would you take?  Profitable longevity is not a difficult concept to understand, yet it seems lost on the relentless pounding being doled out on anyone remotely associated with the market crash – market traders, business executives, and government officials.

While there are certainly businesses that run counter to profitable longevity, i.e. Enron, this is not an isolated incident of a single lending institution engaging in suicidal greed.  The problem spans entire business sectors including lending institutions and trading firms.  From a logical perspective, it’s difficult to assume that two entire business sectors concurrently decided to knowingly engage in practices fueled by excess at the cost of self-destruction.

The need to balance profit and longevity within the financial sector following the Great Depression, the concept of stock and bond risk management was born.  By the 1950s, financial risk management evolved from a concept into a full fledged theory centered around mathematical diversification.  A decade later risk management theory adopted stochastic calculus in order to better account for the inherent randomness involved in stock trading.  Today the financial corporations are littered with what are known as quantitative analysts, otherwise known as quants.

The role of a quant is to develop sophisiticated mathematical and statistical models designed to simulate stochastic processes and it’s potential impact against illiquid products.  Uneffected by supply-and-demand price fluctuations, illiquid asset management depends entirely on these quant models to determine value.  Another tool employed by quants is the process of statistical arbitrage that relies on quantitative data mining in order to determine the expected value of an asset.  These two quant instruments are the foundation for the risk management techniques finicial corporations employ today.

Stochastic calculus attempts to deal with multiple outcomes and given an array of possible initial values, marks the possible outcomes for multiple initial conditions.  While it accounts for a wide possibility of paths, it determines which paths are more probable and which ones are more improbable based on the probability of the initial conditions being present(whose sheer numbers can be exponentially mindnumbing).  Statistical arbitrage relies heavily on gathering statistics over time, in order to create a computational value based on the expected outcome where multiple outcomes exist.

These two systems are attempting to deal with and account for rogue events.  It is in trying to deal with events which have never occurred previously that a fundamental flaw becomes apparent.  These two systems, particularly statistical arbitrage, relies almost exclusively on the collection of event data.  These statistical models derive their accuracy from the amount of data collected.  Hence, the more finite a time data is mined, the less accurate the model becomes.  In order for either of these models to be bulletproof, it would require an infinite amount of time to aggregate data.

Here we have two systems employed by the financial sector to determine the improbability of events and conditions which have never occurred.  The problem exacerbates itself when you consider how the data that is amassed is disseminated.

In order to map these probable and improbable events, statistical analysis employs the use of the Gaussian curve.  It’s a bell curved shaped graph illustrating the probability density of all potential values.  The Gaussian curve bends downwards at it’s edges towards improbable events and upwards towards the probable.  The width of the curve correlates to the weight of those probable events against those improbable ones.  Basically, it charts the normal distribution for events, and giving an indication of which events are more probable then others.

The Gaussian curve is essentially a median value for all possible events.  And that is where it fails.  A model cannot really account for and give the proper value to a rogue event, the most improbable of occurrences, when it gives greater value to standard, the most probable of events.  It fails in calculating the impact of heavy swings against the normal distribution value.  A singular, yet improbable event, will hardly impact the height or width of the curve against the statistical weight of a multitude of those that are likely.

To better illustrate this take 100 middle class American households, and calculate the average income.  Now put every name of every adult in the United States, and put them in a hat.  Randomly draw one name from the hat.  Now take that persons household income and recalculate our income average.  99% of the time you would have selected someone close to the median US household income of $40k.  The average will not make an appreciable movement upwards or downwards.  You could have selected the poorest household in America and the number would hardly have budged.  Let’s say you draw from the hat again, but this time you pick out someone in the top 1% income bracket out of the hat with an improbability factor of 298,128,548 to 1.  Recalculate the average again.  A massive swing upwards in the average will occur.  An event that has a 0.01% chance of occurring will appreciable change our end value, where as 99.99% of possible events will not.

One singular, blip on the Gaussian curve can drastically effect our end value.  But the curve itself doesn’t properly adjust for that highly improbable, rogue event.  Combine this massive swing due to random events with quant statistical models that are ineffectual in mapping these Gaussian blips, and you can begin to see where the problem might lie.

Let’s throw these inadequate models in bed with profit longevity, and it becomes a sticky situation.  Financial corporations have a tricky balancing act to perform.  Faced with reams of data pointing at the normal distribution of the Gaussian curve coupled with the blind spots for events which have never occurred in stochastic calculus and statistical arbitrage, conclusions have to be made as to which path to follow.

Taking the path of improbability would severely limit potential profits.  Under performing profit margins threat longevity.  Following the road towards the probable, gives some assurance in profits and promotes company longevity.  The decision making process cannot even account for rogue events which the statistical models fail in forecasting.

The historical housing market rise and fall would, at it’s very least, qualify, as an most improbable event.  A case could be made that it was a rogue event incapable of being calculated by the quants models.  Instead of tossing around the idea that corporate greed was at the center of the economic crisis our country now faces, let’s consider that, at best, the financial sector was presented with misrepresented data by a fundamentally flawed model.  And, at worst, their models simply were not able to account for never before seen event chain.

Either way, the quant risk management system might be to blame, and desperately needs to be reexamined to better account for those improbably catastrophic occurrences.  No amount of money infusion will turn failing statistical financial models into pinpoint accurate prediction machines – if that’s even possible.  Or maybe we should just learn to accept certain levels of risk that randomness carries with it rather then constantly acquiescing to the fat cat blame game.