Combining Physics and Economics to Understand Stock Market Behaviour

Understanding how asset markets work and which stocks behave similarly is a topic that keeps experts from various fields busy, including economists, physicists, and mathematicians. However, the challenge is that they often don’t know what’s going on outside their discipline. Now, a new paper aims to bridge this gap.

The new study by the Complexity Science Hub identifies the common threads between traditional financial market research and econophysics. Matthias Raddant from the Complexity Science Hub and the University for Continuing Education Krems explains, “We want to create an overview of the models that exist in financial economics and those that researchers in physics and mathematics have developed so that everybody can benefit from it.”

Scientists from these fields are working to figure out or even predict how the market will act. They’re looking to build a large correlation matrix that shows how one stock relates to all others. However, Raddant notes, “Progress, however, is often barely noticed, if at all, by researchers in other disciplines. Researchers in finance hardly know that physicists are researching similar topics and just call it something different. That’s why we want to build a bridge.”

But how do these approaches differ?

Traditional financial market experts mainly focus on statistically figuring out how much stock prices can change. But, their detailed models start to fail when they have to deal with too many stocks.

On the flip side, physicists can deal with a lot of data. They believe more data is better as it helps spot patterns, Raddant explains. They also use correlations, but they see financial markets as changing complex networks. These networks can show dependencies that can reveal which stocks tend to behave the same way and therefore group together. But, physicists and mathematicians may not be aware of the knowledge already available in finance literature and the factors they should take into account.

The study by Raddant and his co-author, Tiziana Di Matteo of King’s College London, also points out that the actual mechanics of the models used by both fields are often pretty similar, but the terminology is different. Finance researchers look for common features in companies, while physicists and mathematicians try to find patterns in many stock time series. “What physicists and mathematicians call regularities, economists call properties of companies, for example,” Raddant says.

According to Raddant, their study is mainly aimed at young scientists working on financial markets from an interdisciplinary perspective. The goal is to familiarize them with the common aspects between the disciplines. “So that researchers who do not come from financial economics know what the vocabulary is and what the essential research questions are that they have to address.” This will prevent them from producing research that isn’t relevant to anyone in finance and financial economics.

Conversely, those from traditional finance backgrounds need to understand how to describe large datasets and spot patterns using methods from physics and network science.

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