New Tool Can Help Predict the Next Financial Bubble
AnonTechie writes:
New tool can help predict the next financial bubble:
An international team of interdisciplinary researchers has identified mathematical metrics to characterize the fragility of financial markets. Their paper "Network geometry and market instability" sheds light on the higher-order architecture of financial systems and allows analysts to identify systemic risks like market bubbles or crashes.
With the recent rush of small investors into so-called 'meme stocks' and reemerging interest in cryptocurrencies, talk of market instability, rising volatility, and bursting bubbles is surging. However, "traditional economic theories cannot foresee events like the US subprime mortgage collapse of 2007" according to study author Areejit Samal. He and his colleagues from more than ten mathematics, physics, economics, and complex systems focused institutions around the globe have made a great stride in characterizing stock market instability.
Their paper abstracts the complexity of the financial market into a network of stocks and employs geometry-inspired network measures to gauge market fragility and financial dynamics. They analyzed and contrasted the stock market networks for the USA S&P500 and the Japanese Nikkei-225 indices for a 32-year period (1985-2016) and for the first time were able to show that several discrete Ricci curvatures are excellent indicators of market instabilities. The work was recently published in the Royal Society Open Science journal and allows analysts to distinguish between 'business-as-usual' periods and times of fragility like bubbles or market crashes.
(Source in German: Max Planck Society )
With all this theory, I hoped to gain some insight about the next financial bubble ... no such luck !! I have doubts whether this method is better than so many other sure-fire ways of predicting the stock markets. In the end, a random flip of a coin may be just as good. What are your thoughts ?
Journal Reference:
Network geometry and market instability, Royal Society Open Science (DOI: https://royalsocietypublishing.org/doi/10.1098/rsos.201734)
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