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In the ever-evolving world of financial markets, it is important to understand the unpredictable nature of stock market fluctuations. New research has made a breakthrough in this field by developing an innovative quantum mechanical model for analyzing stock markets.
The model not only covers economic uncertainty and investor behavior, but also aims to unravel the mysteries behind stock market anomalies such as fat tails, volatility clustering, and contrarian effects. Masu.
Analyzing stocks using quantum models
At the core of this model is quantum mechanics, a pillar of physics known for explaining the behavior of elementary particles.
This study leverages these principles to model the dynamics of stock returns. Dr. Kwangwon Ahn, associate professor of industrial engineering at Yonsei University and lead author of the study, sheds light on this approach.
“Drift in stock returns is caused by external potential forces representing market forces that pull short-term fluctuations back into long-term equilibrium,” he explains.
In an interesting development, this study introduces the diffusion coefficient to measure the volatility of stock returns. By solving the Schrödinger equation, a cornerstone of quantum mechanics, researchers discovered a power-law distribution with tails, a characteristic often observed in stock returns.
This power law distribution suggests that extreme events, such as stock market crashes, occur more often than a normal distribution would predict.
The researchers also found that a power-law index, which describes the “thickness” of the tail, is inversely proportional to the diffusion coefficient and external potential.
Quantum theory and stock market
What does this mean for stock markets? This means that increased volatility and delayed returns to equilibrium can amplify investor herding behavior, especially during times of uncertainty and information asymmetry. suggests.
The study goes further and tests the model using empirical data from the US stock market. We use gross domestic product (GDP) growth rate and forecaster uncertainty as indicators of business cycle and economic uncertainty, respectively, and find that there is a positive correlation between the power law index and GDP growth rate. We found a negative correlation with forecaster uncertainty.
This confirms their theoretical predictions and highlights the role of economic uncertainty in the relationship between business cycles and herd behavior on stock returns.
Corresponding author Dr. Daniel Sung-Yong Kim, associate professor of finance at Chung-Ang University, emphasizes the broader implications of their research.
“Our research shows that quantum mechanics can be a useful tool for understanding stock markets, which are complex systems with many interacting agents. “We hope that we can inspire more interdisciplinary research that combines research and finance to explore the hidden patterns and mechanisms of stock markets,” he says.
The future of physics and finance
Importantly, this study shows that economic uncertainty is the root cause of countercyclical herd behavior in stock returns.
This insight has profound implications for investors and policymakers alike, providing a new lens through which to view market dynamics and make more informed decisions.
In summary, this interesting study challenges traditional methods of analyzing stock markets while blending the realms of physics and finance.
As we continue to grapple with the complexities of financial markets, innovative approaches like this are not only welcome, but necessary to develop a deeper and more accurate understanding of the forces at play.
The entire study was published in the journal financial innovation.
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