Machine learning can predict market behavior

Date:
August 11, 2020
Source:
Cornell University
Summary:
Machine learning can assess the effectiveness of mathematical tools
used to predict the movements of financial markets, according to
new research based on the largest dataset ever used in this area.



FULL STORY ========================================================================== Machine learning can assess the effectiveness of mathematical tools used
to predict the movements of financial markets, according to new Cornell research based on the largest dataset ever used in this area.


==========================================================================
The researchers' model could also predict future market movements, an extraordinarily difficult task because of markets' massive amounts of information and high volatility.

"What we were trying to do is bring the power of machine learning
techniques to not only evaluate how well our current methods and models
work, but also to help us extend these in a way that we never could do
without machine learning," said Maureen O'Hara, the Robert W. Purcell
Professor of Management at the SC Johnson College of Business.

O'Hara is co-author of "Microstructure in the Machine Age," published
July 7 in The Review of Financial Studies.

"Trying to estimate these sorts of things using standard techniques
gets very tricky, because the databases are so big. The beauty of
machine learning is that it's a different way to analyze the data,"
O'Hara said. "The key thing we show in this paper is that in some cases,
these microstructure features that attach to one contract are so powerful,
they can predict the movements of other contracts. So we can pick up the patterns of how markets affect other markets, which is very difficult
to do using standard tools." Markets generate vast amounts of data,
and billions of dollars are at stake in mining that data for patterns
to shed light on future market behavior.

Companies on Wall Street and elsewhere employ various algorithms,
examining different variables and factors, to find such patterns and
predict the future.



==========================================================================
In the study, the researchers used what's known as a random forest machine learning algorithm to better understand the effectiveness of some of these models. They assessed the tools using a dataset of 87 futures contracts -
- agreements to buy or sell assets in the future at predetermined prices.

"Our sample is basically all active futures contracts around the world for
five years, and we use every single trade -- tens of millions of them --
in our analysis," O'Hara said. "What we did is use machine learning to try
to understand how well microstructure tools developed for less complex
market settings work to predict the future price process both within a
contract and then collectively across contracts. We find that some of
the variables work very, very well -- and some of them not so great."
Machine learning has long been used in finance, but typically as a
so-called "black box" -- in which an artificial intelligence algorithm
uses reams of data to predict future patterns but without revealing
how it makes its determinations. This method can be effective in the
short term, O'Hara said, but sheds little light on what actually causes
market patterns.

"Our use for machine learning is: I have a theory about what moves
markets, so how can I test it?" she said. "How can I really understand
whether my theories are any good? And how can I use what I learned
from this machine learning approach to help me build better models
and understand things that I can't model because it's too complex?"
Huge amounts of historical market data are available -- every trade has
been recorded since the 1980's -- and vast volumes of information are
generated every day. Increased computing power and greater availability of
data have made it possible to perform more fine-grained and comprehensive analyses, but these datasets, and the computing power needed to analyze
them, can be prohibitively expensive for scholars.

In this research, finance industry practitioners partnered with the
academic researchers to provide the data and the computers for the study
as well as expertise in machine learning algorithms used in practice.

"This partnership brings benefits to both," said O'Hara, adding that
the paper is one in a line of research she, Easley and Lopez de Prado
have completed over the last decade. "It allows us to do research in
ways generally unavailable to academic researchers."

========================================================================== Story Source: Materials provided by Cornell_University. Original written
by Melanie Lefkowitz. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Zhibai Zhang, Maureen O'Hara, Marcos Lo'pez de Prado, David Easley.

Microstructure in the Machine Age. The Review of Financial Studies,
2020; DOI: 10.1093/rfs/hhaa078 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200811142913.htm

--- up 3 weeks, 6 days, 1 hour, 55 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1337:3/111)