AI stock trading experiment beats market in simulation

June 01, 2020

Researchers in Italy have melded the emerging science of convolutional neural networks (CNNs) with deep learning - a discipline within artificial intelligence - to achieve a system of market forecasting with the potential for greater gains and fewer losses than previous attempts to use AI methods to manage stock portfolios. The team, led by Prof. Silvio Barra at the University of Cagliari, published their findings on IEEE/CAA Journal of Automatica Sinica.

The University of Cagliari-based team set out to create an AI-managed "buy and hold" (B&H) strategy - a system of deciding whether to take one of three possible actions - a long action (buying a stock and selling it before the market closes), a short action (selling a stock, then buying it back before the market closes), and a hold (deciding not to invest in a stock that day). At the heart of their proposed system is an automated cycle of analyzing layered images generated from current and past market data. Older B&H systems based their decisions on machine learning, a discipline that leans heavily on predictions based on past performance.

By letting their proposed network analyze current data layered over past data, they are taking market forecasting a step further, allowing for a type of learning that more closely mirrors the intuition of a seasoned investor rather than a robot. Their proposed network can adjust its buy/sell thresholds based on what is happening both in the present moment and the past. Taking into account present-day factors increases the yield over both random guessing and trading algorithms not capable of real-time learning.

To train their CNN for the experiment, the research team used S&P 500 data from 2009 to 2016. The S&P 500 is widely regarded as a litmus test for the health of the overall global market.

At first, their proposed trading system predicted the market with about 50 percent accuracy, or about accurate enough to break even in a real-world situation. They discovered that short-term outliers, which unexpectedly over- or underperformed, generating a factor they called "randomness." Realizing this, they added threshold controls, which ended up greatly stabilizing their method.

"The mitigation of randomness yields two simple, but significant consequences," Prof. Barra said. "When we lose, we tend to lose very little, and when we win, we tend to win considerably."

Further enhancements will be needed, according to Prof. Barra, as other methods of automated trading already in use make markets more and more difficult to predict.
-end-
Fulltext of the paper is available: http://www.ieee-jas.org/en/article/doi/10.1109/JAS.2020.1003132

https://ieeexplore.ieee.org/document/9080613

IEEE/CAA Journal of Automatica Sinica aims to publish high-quality, high-interest, far-reaching research achievements globally, and provide an international forum for the presentation of original ideas and recent results related to all aspects of automation. Researchers (including globally highly cited scholars) from institutions all over the world, such as MIT, Yale University, Stanford University, University of Cambridge, Princeton University, select to share their research with a large audience through JAS.

IEEE/CAA Journal of Automatica Sinica is indexed in SCIE, EI, Scopus, etc. The latest CiteScore is 5.31, ranked among top 9% (22/232) in the category of "Control and Systems Engineering", and top 10% (27/269, 20/189) both in the categories of "Information System" and "Artificial Intelligence". JAS has been in the 1st quantile (Q1) in all three categories it belongs to.

Why publish with us: Fast and high quality peer review; Simple and effective online submission system; Widest possible global dissemination of your research; Indexed in SCIE, EI, IEEE, Scopus, Inspec. JAS papers can be found at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6570654 or http://www.ieee-jas.org

Chinese Association of Automation

Related Learning Articles from Brightsurf:

Learning the language of sugars
We're told not to eat too much sugar, but in reality, all of our cells are covered in sugar molecules called glycans.

When learning on your own is not enough
We make decisions based on not only our own learning experience, but also learning from others.

Learning more about particle collisions with machine learning
A team of Argonne scientists has devised a machine learning algorithm that calculates, with low computational time, how the ATLAS detector in the Large Hadron Collider would respond to the ten times more data expected with a planned upgrade in 2027.

Getting kids moving, and learning
Children are set to move more, improve their skills, and come up with their own creative tennis games with the launch of HomeCourtTennis, a new initiative to assist teachers and coaches with keeping kids active while at home.

How expectations influence learning
During learning, the brain is a prediction engine that continually makes theories about our environment and accurately registers whether an assumption is true or not.

Technology in higher education: learning with it instead of from it
Technology has shifted the way that professors teach students in higher education.

Learning is optimized when we fail 15% of the time
If you're always scoring 100%, you're probably not learning anything new.

School spending cuts triggered by great recession linked to sizable learning losses for learning losses for students in hardest hit areas
Substantial school spending cuts triggered by the Great Recession were associated with sizable losses in academic achievement for students living in counties most affected by the economic downturn, according to a new study published today in AERA Open, a peer-reviewed journal of the American Educational Research Association.

Lessons in learning
A new Harvard study shows that, though students felt like they learned more from traditional lectures, they actually learned more when taking part in active learning classrooms.

Learning to look
A team led by JGI scientists has overhauled the perception of inovirus diversity.

Read More: Learning News and Learning Current Events
Brightsurf.com is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon.com.