Q: What is the difference between a banker and a trader?
A: A banker will try and tell you a 10% loss followed by a 10% gain is breaking even.

This is a bit less arcane than some of the issues we usually discuss on the Win-Vector Blog, but it is a fun one. And it does take some effort to disabuse yourself of the banker’s fallacy.

It turns out that a lot of our instincts about something as simple as ratios is not quite right. Likely this is because the innate skills of counting leads to a deep understanding of addition and not of multiplication. Take for example the opening joke: a 10% loss followed by a 10% gain sounds like it should be exactly breaking even. But in fact it is exactly a 1% loss.

To compute the loss and gain on $100 we would say after the 10% loss we have $100*(1-0.1) = $90. A 10% gain on this remaining portion would be written as $90*(1 + 0.1) = $99 which, as predicted, is missing a dollar. An incorrect explanation would be something along the lines “well the loss was first, so it applied to a larger number than the gain.” But relative losses and gains work by multiplying and therefore is insensitive to order. It is a fact that a 10% loss followed by a 10% gain is exactly the same as a 10% gain followed by a 10% loss (which eliminates the attempted explanation). The correct explanation is the flaw was far earlier than you would think: you should not believe that the opposite of 10% loss is a 10% gain. To undo the effect of a 10% loss you need just over an 11% gain (a 11.1111111% gain).

For a more dramatic example consider the Dow Jones Industrial Average. It was at $12827 on January 7th 2008, by March 5th of 2009 it had fallen to $6594 or a 48% loss. By January 4th 2010 it had experienced a 60% gain relative to March 5th 2009- but that only got us to $10583, still a 17% loss relative to January 7th 2008. The opposite of 48% loss is in fact 192% gain (which obviously has not happened).

Bankers typically quote interest rates as if they were additive. Things like points and fees are all added. This is almost correct for small interest rates. This nearly right (but actually wrong) language is why we have a bestiary of confusing terms describing interest: simple interest, compound interest and yield. The bankers need some way to signal which numbers will actually be used for computing your mortgage payments versus which numbers will be used for advertising (and in the US they tended not to tell you many of the more important numbers until they were required to by law).

Traders, on the other hand, are very comfortable with multiplying relative losses and relative gains. The main trick of achieving such mastery is to convert multiplication into addition. The way to do this is the log() function (or the logarithm).

The log() function is simple function that has the property that log(a*b) = log(a) + log(b). For connivence lets pick our notation so that log(10) = 1. From this we can deduce that it must be the case that:

log() can not be used on zero or negative numbers (at least not if you expect a real number as a result). For other values we use our calculator.

A trader uses logarithms to think additively about relative changes (also called “returns”). Breaking even is represented as 0 (our friend log(1)), relative increases are represented as positive numbers and relative decreases are represented as negative numbers. For example a 10% loss is represented additively using logarithms as log(1- 0.1) = -0.0458. Now in this logarithm notation the additive opposite of a -0.0458 is in fact (as you would hope) +0.0458. You can even double check: log(1 + 0.1111111) = 0.0458. In this notation the mathematics and the language work together- the opposite of a loss is a gain with the same magnitude (and positive sign).

Returning to our initial example: a 10% loss is represented as -0.0458 and a 10% gain is represented as log(1 + 0.1) = 0.0414, so if we add them (how we combing operations in the logarithmic notation) we get -0.0044. Notice this is not zero, and is in fact equal to log(0 – 0.01) or a net-loss of 1%.

The point is that even trivial math becomes difficult if you are forced, by language or convention, to work from false premises.

Related posts:

1. R annoyances
2. Thievery considered harmful
3. Programs reduced to statistics

• Special Agent Michael G. McSwain’s charges
• Mathew Goldstein’s Reuters article
• Zero Hedge blog entry

For me this triggers some strong (and sad) personal memories.

No matter what inappropriate “Robin Hood” intellectual property fantasies you have this (if true) is just wrong. I have never been a huge fan of the ACM Code of Ethics (which does cover this situation, but fails to seriously address much beyond having responsibilities to your employer) but this sort of incident reminds me why computer science needs some approximation of a shared set of ethics that we can try to refer to.

A particularly sad part of the story that attracted my attention was the reliance on “bash history” to try and establish what happened. Attempting to “prove” something using a “bash history” is something I have painful experience with. The “bash history” system is incredibly inadequate even for what it was designed for (caching recent commands). Simply having two shells open can cause non-deterministic overwriting, deletion, clobbering and time disorderings in the history file. Furthermore bash history has no dates, times, directories or any other contextual hints written into it. Finally bash history has no hashes, signatures, nonces, sequence numbers or any other device that helps establish authenticity.

Now for my story. We (by chance) caught somebody walking off with our group’s entire source tree. In the end all we had to go on was the bash history. To hostile eyes bash history is nowhere near what you would call “forensic grade evidence.” Unfortunately for us the theft was intramural, the thief was merely taking the code to another group in the same company to later mine and represent as their own work. At this point even language worked against us- every time we accidentally said something like “our code” (as in the code we produced, not the code we own) we were perceived as being anti-company. Evil prevailed (the thief was promoted) and I looked stupid for working so hard to try to interpret such low-quality evidence. But we live in an objective world- just because you can’t prove something doesn’t mean there isn’t some buried ugly truth.

So what was stolen? Not the code, that moved from one pocket of the corporation that owned it to another pocket of the same corporation. What was stolen was reputation. The thief presumably appeared to out-produce both his old colleagues and his new ones (who don’t have a few absconded person-years of development to draw from). So an apology to anyone who was asked why they could not code as fast as our escaped “genius,” it was certainly not our intent to so equip him. And a larger apology to the rest of the team, sorry we could not prove the misappropriation of your work.

Of course Shakespeare said it much better (from Othello):

Good name in man and woman, dear my lord,
Is the immediate jewel of their souls:
Who steals my purse steals trash; ’t is something, nothing;
’T was mine, ’t is his, and has been slave to thousands;
But he that filches from me my good name
Robs me of that which not enriches him
And makes me poor indeed.

Related posts:

1. Sorting Used in Anger
2. Relative returns: a banker versus trader paradox
3. A Quick Appreciation of the Sharpe Ratio

The Sharpe ratio is a famous measure of “risk adjusted return” and is defined as “the ratio of the expected excess return from an investment divided by standard deviation of the excess return.” It is most easily demonstrated by an example (which we work in pieces).

If an investment is expected to generate a profit of 15% in the next year and an insured bank account would generate 10% profit then the expected excess return invested is 15% – 10% = 5%. A rational investor would never take a risky investment that did not have a positive excess return (else they would expect to make more money at a bank). “Expected” is a technical term which means the average return of the investment averaged over all possible outcomes (weighted by the odds of each outcome), we can explain this by working a couple of examples.

Consider investment “A” which is a generally good idea that returns a 20% profit in half the possible years and a 10% profit in the other half of the years. Investment A has an expected return of 0.5*20% + 0.5*10% = 15%. Investment “A” has 15% – 10% = 5% excess return.

Also consider another investment “B” which is a risky bet that returns 20% profit most years (around 95.8% of them) and goes bankrupt in the other years. The expected return of investment “B” is 0.958*20% + 0.042*(-100%) = 14.96%, or essentially 15%. Investment “B” has 15% – 10% = 5% excess return.

As we can see “expectation” alone can not really tell these two investments apart. That is why the second component of the Sharpe ratio is something called the standard deviation. The standard deviation is defined as the square-root of the squared deviations of the return from the target value of 15%. What we do is measure for each possible outcome how far off the return is from the target of 15%, multiply this number by itself (called squaring it) and then take the square-root of the sum of all such values. Again, this is best explained by an example.

Investment “A” has a standard deviation of:
square-root( 0.5 * (20% – 15%)*(20% – 15%) + 0.5 * (10% – 15%)*(10% – 15%) ) = 5%

And investment “B” has a standard deviation of:
square-root( 0.958 *( 20% – 15%)*( 20% – 15%) + 0.042*(-100% – 15%)*(-100% – 15%) ) = 24%

Just like in the calculation of expectation we are taking every possible situation and summing (weighted by the likelihood) our value of interest (in this case the squared variation).

The standard deviation’s opinion is that investment “B” is about five times riskier than investment “A.” And this is the grace of the Sharpe ratio: it says that investment “A”‘s value is (15% – 10%)/5% = 1 and “B”‘s value is (15% – 10%)/24% = 0.2.

An interesting feature of the Sharpe ratio is that, unlike Wall Street, it does not believe that leveraging increases profitability. A common desperation move is to take an investment that has a moderate return and borrow money to simulate larger returns by having larger exposure. For instance an investment that returns 15% can try to simulate a higher return by borrowing. If for every $1,000 invested we borrow another $1,000 to invest (paying the risk rate of 10% for the money) one can show an apparent rate of return of ($2000*15% – $1000*10%)/$1000 or 20%. However, this is not free money- the investor is taking on twice as much risk for only half as much more return. In fact with sufficient leverage (three times, for times, thirty times) one can convert a safe investment into a risky investment that could even go bankrupt. The Sharpe ratio (by design) is not fooled by this sort of manipulation. Investing $1000 in investment A has the exact same Sharpe ratio as investing $1000 plus $1000 more borrowed at the risk-free rate (this is part of the cleverness of using excess returns instead of un-adjusted returns).

Unfortunately to use the Sharpe ratio you need good estimates of three things:

1) The expected return of the investment.

2) The risk-less available in the market (to compute excess).

3) The standard deviation of the investment.

All three of these facts are about the future, so we don’t really know any of them. The historic returns of an investment are not the same thing as the expected returns in the future, interest rates can change and the standard deviation is especially hard to estimate. However, if you have a model (or at least a theory) of what your investments are supposed to do then you can plug in estimates for these three quantities and use the Sharpe ratio to determine which investments really are best.

If you knew how investment “A” worked and could estimate that it returned 20% about half the time and 10% the other times you could estimate its Sharpe ratio as 1. And if you knew investment “B” was a gamble that almost always paid off at 20% with a single rare event that causes bankruptcy you could estimate its Sharpe ratio as 0.2. Even if your estimates were inaccurate (say you estimate investment “A”‘s Sharpe ratio is 0.7 and investment “B”‘s Sharpe ratio as 0.3) the indication is to stay away from investment “B.”

This is in stark contrast to the conclusion you would draw if you thought of these investments as a “black box” (like a fund of funds does) and looked only at their historic performance. If you looked at around 5 years of historic performance of both investments you would (incorrectly) think the following:

Investment A looks kind of noisy, some years it returns 10% and some years it return 20%. You would estimate (correctly) the return as averaging to 15% and you can even get a historic estimate of its standard deviation that is actually about right (5%)

Investment B looks like easy money. With about 80% chance you would not have seen a bankruptcy, just 5 years of 20% returns. You would mis-estimate the return as being 20% (all you have ever seen) and further mis-estimate the standard deviation as 0%.

Based on historic data alone you would fire the manager of investment “A”, give the manager of investment “B” a huge bonus and invest all of your money. And a few years later you would go bankrupt.

What is going on is very well explained by Nassim Nicholas Taleb as “the turkey paradox.” Domestic turkeys are all killed at about the exact same age (say 60 days). For somebody that understands commercial poultry farming there is not any mystery or uncertainty about it. 60 days before you want to sell a turkey carcass you buy a turkey chick. There is an inevitability and reverse causality- the desire for the turkey’s carcass funds and causes the turkey’s start of life 60 days earlier. Now if the turkey is a statistical empiricist (perhaps with a PhD in machine learning) things look good. The turkey sets up a model of each day having an unknown chance of being good or bad. The turkey figures that each day’s outcome is an independent trial drawn from this single unknown probability. The turkey collects evidence: every day it gets fed. Each day is more evidence that all days will be good. And then on day 60 the turkey gets a nasty surprise. The turkey’s life was a bad investment from day one, all of the “evidence” the turkey collects along the way was irrelevant because the model was wrong. And the model was wrong because the turkey guessed at the model instead of investigating the nature of poultry farming.

Much is the same in many investments. There are investments that look like investment “B” when you open the hood. Many of them involve writing “out of the money options” and “default swaps.” These are essentially selling insurance on events that nobody thinks are likely. Selling insurance that usually is not used is profitable, until the insurance gets used. This is why insurance companies (if they are ethical) don’t treat the entirety of collected payments as profit- but as a stockpile that must be kept to pay the claims that will inevitably some day come true.

It is important to point out the Sharpe ratio will give you incorrect results if you plug bad estimates into it. Overall the Sharpe ratio prefers good investments and diversification but it can be led astray. In fact that is the whole point: no amount of smart math will undo the inevitable consequences of wrong models that are used because “you need something you can solve” (like the turkey) or “everybody else is getting rich using them” (like investment “B”).

Related posts:

1. “Easy” Portfolio Allocation
2. What is the gambler’s equivalent of Amdahl’s Law?
3. What does the Market Think?