In the many blogs on the internet and the analytical section
of our websites, we write a lot about algorithms and tools for predicting movement
in the financial markets. At the same time, many observers believe that such
activities are akin to playing in a casino - everything is random on the stock
exchange, which means nothing can be predicted. A quantitative analyst at NMRQL
hedge fund Stuart Reid published the results of a study on the Turing Finance website,
during which he used the random walk hypothesis, trying to confirm or refute
the thesis about the randomness of financial markets. We present to your
attention the main ideas of this material.
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As Reed writes, hackers and traders are essentially doing
the same thing—they find inefficiencies in the system and exploit them. The
only difference is that some, pursuing a variety of goals, hack computers,
networks, or even people, while the latter hack financial markets and their
goal is to make a profit.
In this context, the topic of random number generators is
very interesting - they are used to encrypt data and communications, but if a
vulnerability is found in the generator, it ceases to be protected, and hackers
can use the error to decrypt information. There are various sets of tests that
such generators must pass in order to evaluate their cryptographic strength.
One such set is the NIST test group. In this article, we will look at applying these
tests to financial strategies to see if the market can be hacked.
Random Walk Hypothesis
In the real world, many systems exhibit properties of
randomness. For example, the spread of epidemics like Ebola, the behavior of
cosmic radiation, the movement of particles in water, luck while playing
roulette, and, according to the random walk hypothesis, even the movement of
financial markets.
Consider an interesting test conducted by Princeton
University economics professor Burton G. Malkiel. In its course, students were
"issued" a hypothetical stock, which initially cost $50. The closing
price for this stock was determined each day by a coin toss. If it came up
heads, the price was half a point higher, and if it came tails, it was half a
point lower. Thus, each time the chance of the value rising or falling compared
to the previous "trading day" was 50%. Thus, price cycles and trends
were determined.
Subsequently, Malkiel visualized the results using charts
and showed them to “chartists,” that is, specialists who predicted future price
movements based on their past fluctuation patterns. The Chartists advised him
to buy shares immediately. But since this stock did not exist, and its price
was determined by the toss of a coin, there were no real patterns, and
therefore there could not be a trend. The result of the experiment allowed
Malkiel to argue that the stock market is as random as a coin toss.
It's like the " financial Turing test "”, during
which people familiar with the financial market are invited to look at the time
series chart and determine which of them is real market data, which is a
simulation using random processes:
Is this a real market?
And is it random?
Or is there no difference at all?
It's pretty hard to determine. It is observations like these
that led many researchers in the field of financial markets to think about how
to find out how random the behavior of stocks on the stock exchange really is.
The theory that prices move randomly is called the random walk hypothesis.
Many of the researchers have done tests like Malkiel's
experiment, but they don't actually prove that the stock market develops by
chance. They only prove that for the human eye, in the absence of additional
information, real price movements cannot be distinguished from random ones.
There are drawbacks to the hypothesis itself:
1. It considers
different markets as a homogeneous environment, not taking into account the
differences between them.
2. It does not explain the many empirical
examples in which people have consistently won in the market.
3. It is based on a
statistical definition of randomness, not an algorithmic one. And this means
that the hypothesis does not distinguish between local and global randomness, does
not take into account the concept of the relativity of randomness.
And yet, whether one likes it or not, it is undeniable that
the widespread use of the random walk hypothesis among quantitative analysts in
the stock market as a whole has had a major impact on how various financial
instruments, such as derivatives or structured products.
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