Bridging traditional economics and econophysics

Newswise — Researchers at the Complexity Science Hub and the University for Continuing Education Krems have conducted a recent study focusing on the intersection of traditional financial market research and econophysics. Their objective is to provide a comprehensive overview of existing models in financial economics, as well as those developed by physicists and mathematicians. The aim is to make this information accessible to everyone, enabling widespread benefits from the findings, as stated by Matthias Raddant from the Complexity Science Hub and the University for Continuing Education Krems.

The scientists in both financial market research and econophysics share a common objective of classifying and potentially predicting market behavior. Their goal is to construct a comprehensive correlation matrix that encompasses the correlations between individual stocks and all other stocks. However, the progress made in these fields often goes unnoticed or unacknowledged by researchers in other disciplines. Many finance researchers are unaware that physicists are investigating similar topics albeit under different terminology. Recognizing this gap, Raddant emphasizes the importance of bridging the divide between the two disciplines.

WHAT ARE THE DIFFERENCES?

Traditional financial market experts place significant emphasis on accurately characterizing the statistical volatility of stocks. However, their finely tuned models face limitations when confronted with large-scale data sets that encompass tens of thousands of stocks. As the size of the data set increases, the effectiveness of these models diminishes, leading to inadequacies in their ability to capture and analyze the complexities of such extensive stock portfolios.

In contrast, physicists possess a strong aptitude for handling substantial volumes of data. Their approach is guided by the principle that increased data availability enhances their ability to discern underlying patterns and regularities. Matthias Raddant explains that physicists and mathematicians, similar to financial experts, employ correlations in their research. However, they adopt a distinct perspective by modeling financial markets as dynamic and intricate networks. These networks capture interdependencies among various assets, unveiling patterns of asset co-movement and identifying groups of stocks that exhibit similar behaviors. Nevertheless, physicists and mathematicians may not be fully aware of the existing insights in the finance literature, nor do they always consider all relevant factors in their analyses. This highlights the significance of bridging the gap between the two disciplines to ensure a comprehensive understanding of financial markets.

DIFFERENT LANGUAGE

Raddant and his co-author, Tiziana Di Matteo, an external faculty member of the Complexity Science Hub and affiliated with King's College London, highlight in their study that while the underlying mechanics of these models may be similar, the terminology used by researchers in finance, physics, and mathematics varies significantly. Finance researchers focus on uncovering the interconnected features of companies, whereas physicists and mathematicians concentrate on establishing order amidst numerous stock time series, where identifiable regularities emerge. Raddant emphasizes that what physicists and mathematicians refer to as regularities, economists describe as properties of companies, for instance. This disparity in language underscores the need to bridge the communication gap between these disciplines and promote cross-disciplinary collaboration.

AVOIDING RESEARCH THAT GETS LOST

Raddant emphasizes that the aim of their study is to raise awareness among young scientists, particularly those engaged in interdisciplinary research in financial markets. The goal is to familiarize researchers who may not have a background in financial economics with the connecting elements between disciplines. This includes providing them with the necessary vocabulary and understanding of the fundamental research questions that need to be addressed. By bridging this knowledge gap, there is a reduced risk of producing research that holds little interest for professionals in finance and financial economics. The study thus serves as a means of guiding young scientists towards conducting research that is both relevant and valuable to the finance field.

However, scientists specializing in the fields typically associated with financial markets need to grasp the techniques of physics and network science in order to effectively depict extensive data sets and statistical patterns.