engleski

A Network-Based Approach to Modeling Market Bubbles and Crashes

Political, social and economic systems are built upon networks of individuals and organizations that mutually compete and cooperate. The underlying mechanisms behind these forces arise from the individuals’ specific preferences, and surface in various processes, such as bargaining – which result in deals, trades etc. Although standard axiomatic bargaining theory idealizes the bargaining problem by assuming rationality and complete information, in reality incomplete information and herding effects shift the outcomes beyond rational levels. This is particularly pronounced in financial systems, where such irrational behavior directly influences financial markets and induces the formation of so-called “bubbles”. Market bubbles are most commonly characterized as states when assets are traded at prices far beyond their intrinsic (rational) values, and often end in abrupt market crashes. The problem of identifying these phenomena and possibly anticipating the systems’ tipping points remains a question of great significance for the entire global economic system. Here we introduce an alternative bargaining model, based on assumptions of herding behavior in price formation, and cooperation and competition forces within the supply and demand sides. We present networks of mutually competing agents that cooperate with the other side through the bargaining process. Moreover, a feedback mechanism in price formation and trading is introduced by a variable R that accounts for the intrinsic market value and quantifies the degree of market overpricing. As a result, the network structures induce the emergence of bubbles, which, after some persistence, lead to critical shifts in networks and cause the market to crash. Due to the feedback and the complex nature of the system, the tipping points and the extent of bubble persistence are non-deterministic and depend on the changes in the intrinsic values and the variable R. We detect a strong hysteretic behavior of the probability that the market index will drop in the next year, from which we estimate the tipping point. Furthermore, we find that the probability distribution of R has a bimodal shape, which is typical of small systems near tipping points. The model is used to examine the S&P 500 market index, where the intrinsic price is estimated using a modified free cash flow (FCF) model. Without any fitting, we report that the average value of the ratio between the S&P 500 index price and our intrinsic price estimate is very close to 1, which, given that the S&P 500 is considered an efficient market, additionally supports our estimates. We demonstrate that the financial data of the S&P 500 index exhibits a hysteresis and a tipping point in agreement with the model predictions. Moreover, we report that the model price outputs based on the estimated S&P 500 intrinsic values are cointegrated with the real S&P 500 traded prices, examined on the S&P 500 data from 1920 until today. Based on the internal network structures and processes we construct early-warning indicators and demonstrate the applicability of the model in identifying and anticipating critical phenomena in markets.

Cooperative game theory ; Feedback ; Market crash

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