Measuring Randomness in Capital Markets
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What is Random?
As previously discussed, there’s no universal measure of
randomness. Randomness
implies the lack of pattern and the inability to predict future outcomes.
However, The lack of an obvious model doesn’t imply randomness anymore than a
curve fit one implies order. So what actually constitutes randomness, how can we
quantify it, and why do we care?
Randomness $\neq$ Volatility, and Predictability $\neq$ Profit
First off, it’s important to note that predictability doesn’t guarantee profit.
On short timescales structure appears, and it’s relatively easy to make short
term predictions on the limit order book. However, these inefficiencies are
often too small to capitalize on after taking into account commissions. Apparent
arbitrage opportunities may persist for some time as the cost of removing the
arb is larger than the payout.
Second, randomness and volatility are oftused interchangeably in the same way
that precision and accuracy receive the same colloquial treatment. Each means
something on its own, and merits consideration as such. In the example above,
predictability does not imply profitability anymore than that randomness
precludes it. Take subprime for example – the fundamental breakdown in pricing
and risk control resulted from correlation and structure, not the lack thereof.
Quantifying Randomness
Information theory addresses
the big questions of what is information, and what are the fundamental limits on
it. Within that scope, randomness plays an integral role in answering questions
such as “how much information is contained in a system of two correlated, random
variables?” A key concept within information theory is Shannon
Entropy – a measure
of how much uncertainty there is in the outcome of a random variable. As a
simple example, when flipping a weighted coin, entropy is maximized when the
probability of heads or tails is equal. If the probability of heads or tails is
.9 and .1 respectively, the variable is still random, but guessing heads is a
much better bet. Consequently, there’s less entropy in the distribution of
binary outcomes for a weighted coin flip than there is for a fair one. The
uniform distribution is a so called
maximum entropy probability distribution as there’s
no other continuous distribution with the same domain and more uncertainty. With
the normal distribution you’re reasonably sure that the next value won’t be far
away from the mean on one of the tails, but the uniform distribution contains no
such information.
There’s a deep connection between entropy and compressibility. Algorithms such
as DEFLATE exploit patterns in data to
compress the original file to a smaller size. Perfectly random strings aren’t
compressible, so is compressibility a measure of randomness? Kolmogorov
complexity measures,
informally speaking, the shortest algorithm necessary to describe a string. For
a perfectly random source, compression will actually increase the length of the
string as we’ll end up with the original string (the source in this case is its
own shortest description) along with the overhead of the compression algorithm.
Sounds good, but there’s one slight problem – Kolmogorov complexity is an
uncomputable function. In the general case, the search space
for an ideal compressor is infinite, so while measuring randomness via
compressibility kind of works, it’s always possible that a compression algorithm
exists for which our source is highly compressible, implying that the input
isn’t so random after all.
What about testing for randomness using the same tools used to assess
the quality of a random number generator? NIST offers a test
suite for doing so.
However, there are several problems with this approach. For starters, these
tests need lots of input – 100,000+ data points. Even for high frequency data
that makes for a very backward looking measure. Furthermore, the tests are
designed for uniformly distributed sample data. We could use the probability
integral transform
to map our sample from some (potentially empirical) source distribution to the
uniform distribution, but now we’re stacking assumptions on top of heuristics.
Visualizing Volatility and Entropy
Of the above it sounds like entropy gets us the closest to what we want, so
let’s see what it looks like compared to volatility. We’ll start by plotting the
20 day trailing absolute realized variation of the S&P 500 cash index as a
measure of volatility:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 

Now let’s look at entropy. Entropy is a property of a random variable, and as
such there’s no way to measure the entropy of data directly. However, if we
concern ourselves only with the randomness of up/down moves there’s an easy
solution. We treat daily returns as Bernoulli trials in which a positive or a
zero return is a one and a negative return is a 0. We could use a ternary
alphabet in which up, down, and flat are treated separately, but seeing as
there were only two flat days in this series doing so only obfuscates the
bigger picture.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 

Visually the two plots look very different. We see that most of the time $H(X)$
is close to one (the mean is .96), indicating that our “coin” is fair and that
the probability of a day being positive or negative over a trailing 20 day
period is close to .5.
How correlated are the results? If we consider the series directly we find that
$Cor(H(X),\ \sigma) = .095$. It might be more interesting to consider the
frequency in which an increase in volatility is accompanied by an increase in
entropy: .500 – spot on random. Entropy and volatility are distinct concepts.
In Closing…
The reoccurring theme of “what are we actually trying to measure,” in this case
randomness, isn’t trivial. Any metric, indicator, or computed value is only as
good as the assumptions that went into it. For example, in the frequentist view
of probability, a forecaster $F(Xx_{t1}, \ldots, x_0)$ is “well calibrated”
if the true proportion of outcomes $X = x$ is close to the forecasted
proportion of outcomes (there’s a Bayesian interpretation as well but the
frequentist one is more obvious). It’s possible to cook up a forecaster that’s
wrong 100% of the time, but spot on with the overall proportion. That’s
horrible when you’re trying to predict if tomorrow’s trading session will be up
or down, but terrific if you’re only interested in the long term proportion of
up and down days. As such, discussing randomness, volatility, entropy, or
whatever else may be interesting from an academic standpoint, but
profitability is a whole other beast, and measuring something in
distribution is inherently backward looking.
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